A novel method to improve water saturation in shaly sand reservoirs using wireline logs

Journal Pre-proof A novel method to improve water saturation in shaly sand reservoirs using wireline logs Ayman Shebl El-Sayed PII:

S0920-4105(19)31023-X

DOI:

https://doi.org/10.1016/j.petrol.2019.106602

Reference:

PETROL 106602

To appear in:

Journal of Petroleum Science and Engineering

Received Date: 6 May 2019 Revised Date:

30 September 2019

Accepted Date: 17 October 2019

Please cite this article as: El-Sayed, A.S., A novel method to improve water saturation in shaly sand reservoirs using wireline logs, Journal of Petroleum Science and Engineering (2019), doi: https:// doi.org/10.1016/j.petrol.2019.106602. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

A NOVEL METHOD TO IMPROVE WATER SATURATION IN SHALY SAND RESERVOIRS USING WIRELINE LOGS Ayman Shebl El-Sayed Ain Shams University, Cairo, Egypt. ABSTRACT: Water saturation in shaly reservoirs is a difficult task due to the heterogeneity nature of shale, and the way the shale distributed in the reservoirs. So, many scientists proposed several models for water saturation in shaly sandstone. So far, the most reliable techniques depend on identification of shale habitat qualitatively to apply suitable water saturation model, these techniques can lead to inaccurate identification of shale habitat, especially when dealing with a mixture of different shale modes and massive superimpose well logging data, and hence drastically erroneous values of water saturation and hydrocarbon reserves. Well logs and routine core analysis from two main fields in Egypt. The North Sinai and the Gulf of Suez fields are used for this study to evaluate the novel proposed method. Unlike other qualitative methods, a new robust method has been introduced to determine accurate volumes of shale distribution types. This volumetric method checked for validation and accuracy using Thomas-Stieber crossplot, the results show good and a reliable degree of accuracy. Consequently, appropriate water saturation models can apply accurately. While the dispersed shale model is adequate to estimate water saturation in dispersed shale reservoirs, water saturations for laminated shaly sand reservoirs are more complicated. So, a new approach and algorithms have been introduced to evaluate water saturation in these complex problematic reservoirs specifically in the absence of triaxial induction resistivity measurements. Furthermore, to prove the superiority of this novel method over traditional techniques, a comparative study and statistical analysis performed between the new water saturation method and conventional techniques by using InetillLog software © 2015. These comparisons and statistics show a remarkable discrepancy and variance in values between conventional methods and the new method, where water saturation values obtained from conventional methods for two fields are drastically lower than the new method. This can lead to erroneous water saturation and hydrocarbon estimations. Water saturation derived from the new method were in perfect agreement with the corresponding water saturations from cores data. Moreover, scatter diagrams between water saturation from novel method and that from core analysis obtained correlation coefficient (R2) values of 0.82, 0.85, for the North Sinai and the Gulf of Suez fields, respectively, indicating strong linear relationships. Furthermore, this new method is taken care of one of several pitfalls of LSSA interpretation method. So, this novel method adds a valuable improvement in water saturation and the assessment of hydrocarbon saturation in shaly reservoirs for accurate continuous Net To Gross (NTG) calculations, better net pay detection, reservoir simulation, and reserve estimation.

KEY WORDS: IntelliLog; Shaly Sandstone Reservoir; Water Saturation; Shale Distribution Types; Well Logging; Formation Evaluation Techniques. 1- INTRODUCTION Formation evaluation for shaly reservoirs is a big challenge for any petrophysicists due to the complexity, heterogeneity nature of shale, and shale distributions in the reservoir, where shale severely decreases effective porosity, total porosity, and permeability (Ruhovets and Fertl, 1982), and adversely affects the reservoir quality (Paul, 2012). Water saturation in clean formation can be evaluated by using Archie's equation (1942), based on the hypothesis that brine water is the only conductor media in the formation, while in shaly sandstone formations, water saturations mainly depend on shale distribution types in formation which affect electrical conductivity of the reservoir. So, many scientists proposed different models, methods, and assumptions to evaluate water saturation in the shaly reservoir based on two main models Vsh (Shale Volume) and ionic double layer (Worthington, 1985). Shale volume models based on empirical formulas assuming that the extra conductivity of the shaly formations related to extra shale contents (Poupon and Leveaux, 1971; Simandoux, 1963; Bardon, C., and Pied, B., 1969; Poupon et al., 1967; Schlumberger, 1972; Fertl., 1975; Hossin., 1960; Abdelrahman et al., 2000). These models give erroneous water saturation results because there is no definite shale volume indicator, besides; they neglected the conductivity of shale, which depends on shale habitat (Al-Ruwaili and Al-Waheed, 2004; Ipek, 2002; Rezaee and Lemon, 1996; Waxman and Smits, 1968). While, ionic double layer models (Clavier et al., 1984; Best et al., 1978 (CYBERLOOK program); Waxman et al., 1968; Juhasz, I., 1979; Z.A. Bassiouni et al., 1990; Juhasz, I., 1981) depended on Cation Exchange Capacity (CEC) (Worthington, 1985). The cation exchange capacity obtained from SCAL (Special Core Analysis) data which is not available in most cases for analysis, independent on shale habitat, besides; it is a time consuming procedure and gives unreliable results (Shokir, 2004; Worthington, 1985). Max P., and Antony H., (2014) introduced a novel “difference” method. Where, shale volume models and ionic double layer models can be linked via bound water saturation, instead of using wet shale parameters that obtained from adjacent shale beds via well logs and unlike the others, he used cores, cuttings, or sidewall samples to determine clay type(s). For different shale types, X-Ray Diffraction analysis (XRD) is required to determine both the shale types and dry-clay parameters. Then, he used a dry clay volume and dry clay parameters together to determine cation exchange capacities that will be used in the Waxman-Smits equation. The drawbacks of this method are expensive proposed procedures to obtain shale type, CEC is unreliable, and is not available all the time for analysis as mentioned earlier. An advanced interpretation techniques have been introduced for water saturation in laminated sand/shale sequence based on Thomas Stieber cross-plot analysis to evaluate laminated volume and true formation resistivity of sandstone lamina derived from the multicomponent induction resistivity tool (MCI) to measure horizontal resistivity (Rh) and vertical resistivity (Rv). (Roddy Irwin., 2016). These advanced interpretation techniques rely on the qualitative determination of shale habitat, which lack of accuracy. Author. Tel. +20 01027980214; Author. E-mail: [email protected]

Shedid A. Shedid et al. (2017) performs a comparison study between five popular water saturation models in shaly sand reservoirs; he concluded that the most reliable method to determine water saturation in shaly sandstone reservoir is based on water saturation models that depend on the shale distribution types, other than that will give drastically erroneous water saturation estimation. But, there are several shortcomings of this study such as he performed this study on only a few zones [11 zones], this makes it impractical for application on massive superimpose well logging data, also he determined the type of shale habitat roughly qualitatively on density porosity versus neutron porosity crossplot, where in reality this determination is really more complicated, so this can lead to inaccurate estimation of shale distribution types specifically when dealing with massive superimpose well logging data and different shale distribution types, and hence apply inappropriate water saturation model on several zones in the reservoirs that do not really belong to the assumed predominant shale type. Besides, he used a simplified deterministic equation to solve laminated shale problem, which gave less accurate results of water saturation, water saturation will be very pessimistic. As a result, valuable reservoirs can simply ignored. Water saturation for dispersed shale zones can be evaluated from dozens of wide derivatives of Archie’s equation (1942) (Worthington, 1985), While the simplified deterministic solution for water saturation based on traditional resistivity tools for thin beds or laminated shaly sand reservoirs, the thickness of laminae is beyond the vertical resolution of well logging tools (Klein et al., 1997), makes the true resistivity lower than the actual resistivity value of the hydrocarbon saturated sandstone layers. As a result, the calculated water saturation is pessimistic, and the determination of oil volume is lower than the actual volume, usually underestimating hydrocarbon saturation by 30% or even more. Hence, the valuable reservoir can simply be bypassed. So, the only satisfactory solution for water saturation in these complex reservoirs is by using the multicomponent induction resistivity tool (MCI), triaxial induction resistivity tool, oil base mud resistivity micro image tool (OBMI), Invaded zone formation resistivity very high resolution (RXOI), Phasor induction, or similar tools that measure horizontal resistivity of the reservoir (Rh) and vertical resistivity (Rv) of the reservoir. Using these two orthogonal resistivity components in Laminated Shale Sand Analysis program (LSSA), water saturation can be resolved for these complex laminated shaly sand reservoirs. However, Laminated Shaly Sand Analysis (LSSA) interpretation model suffers from several pitfalls (Tyagi et al., 2009) such as: 1- Electrically anisotropic layer (e.g., Calcite) or calcareous sand streaks can boost up Rv leading to overestimation of Rsd, and consequently, higher saturation of hydrocarbon. 2- LSSA program considered shale distribution types as bimodal mode, so it considered the occurrence of calcite as sand and calcite cement as pore filling material. Besides; the silt occurrence in the reservoir is not taken care by LSSA. These third facies in any formation have a great effect on Rh and Rv makes water saturation determination more complicated and difficult. 3- This method assumed that the sand is only isotropic, any internal anisotropy is not taken into account. This can lead to overestimation of hydrocarbon saturation. 4- Higher shale anisotropy may enhance Rv and shifts the gas-water contact a few meters down if it occurred nearby the original contact. Not to mention, unfortunately, phasor induction tools and a triaxial induction tool are expensive and add additional cost (Fernando Angel., 2017), multicomponent induction resistivity tool is rarely used in major domestic petroleum fields (Fei et al., 2016), and in several cases many fields were developed before these advanced tools were invented and there is a necessity to review the evaluation from these fields to identify the hydrocarbon bearing laminated shaly sandstone reservoirs and to discriminate them from poor quality zones. (Rick et al., 2017). So, the main purpose of this research is to introduce novel, quick, and reliable method to determine water saturation in shaly sandstone reservoirs in both dispersed and laminated shaly sand reservoirs with a reliable degree of accuracy using conventional well logs, the new idea behind this method is based on a new robust volumetric shale types method, this new volumetric shale types model checked for validation and accuracy using the famous Thomas-Stieber crossplot by using new graphical techniques, as a feature added, proposed by the author to clarifying different shale distribution types very efficiently and smartly. Accordingly, a robust procedures flow and algorithms have been introduced to choose an appropriate water saturation model between dispersed shale model and a new approach of the laminated shale model developed to resolve thin beds problems. This novel method can applied in any reservoirs as long as constrained conditions for application are fulfilled and petrophysical parameters determined correctly and re calibrated as needed when further wells will be drilled.

2. MATERIALS, METHODS AND TECHNIQUES 2.1. Materials. Well logging tools are powerful methods to investigate and analysis the physical properties of the different subsurface formations around the wellbore at a cheaper cost (5% and 15% of total well costs) (Ofwona, 2010). While a complete coring procedure and core analyses of the entire zones of interest are literally impractical (Eshimokhai and Akhirevbulu, 2012). Well logging data used in the present study consists of (GR [Gamma Ray Log], NPHI [Neutron Log], RHOB [Density Log], DT [Sonic Log], and Resistivity log [AT90: Array Induction 90” Resistivity]) from the North Sinai field (Fig. 1) and the Gulf of Suez field (Fig. 2) are subjected to several environmental corrections, porosity logs corrections, and hydrocarbon effect corrections to deduce four crucial petrophysical parameters (Vsh, φt, φe, and Sw). In addition to well logging data, a routine core analysis has been performed on 156 core samples of the North Sinai Field, and 64 core samples of the Gulf of Suez Field to estimate two valuable petrophysical parameters, total porosity and water saturation. These petrophysical parameters are very important in comparison and statistical analysis with novel water saturation method.

2.2. METHODS AND TECHNIQUES 2.2.1. IntelliLog Software. The IntelliLog software © 2015, Ayman Shebl El-Sayed. All rights reserved, which is invented and programmed by the author since 2013 and has been continually developed using a powerful VC++ programming language, is a petrophysical PC Windows based application platform gathering all well logging data into intuitive application to carry out analyses. It has the ability to integrate both logs and cores interpretations in one platform and has a lot of features for example, but not limited. 1- Fully compatible with different LAS file versions 1.2, 2, and 3 with the self check integrity of file structure. Also, auto-detect ASCII file contents without any format restrictions. 2- Fully compatible with the Dominion Land Survey (DLS) and the National Topographic System (NTS) for Canada, and API well number system for USA. 3- Loading, visualizing, editing, auto merging well logs, and auto depth shift solver (IntelliDepthShift © 2015) based on Artificial Intelligent (AI) Application. 4- Auto-detect unit systems of various well logs and convert between them from Metric to English units and vice versa. 5- Intelligent auto detection of logs' families and recognition, not just reading, of over 5000 well logs records, up to 20000 in case of logs duplication, including cores data and their descriptions. 6- Global Positioning System Module (GPS), comprised of all available worldwide six GPS format systems with up to 99 geodetic datums. 7- Math pack module with several petrophysical filters to be applied to the results. 8- Over 14 built-in models for water saturation in clean and shaly sandstone reservoirs. 9- Built-in all known petrophysical crossplots and charts. IntelliLog software © 2015 is used for all methods conducted in this study to perform a robust computation of petrophysical properties for conventional log interpretation which includes empirical equations, environmental corrections for wireline logs, logs display, statistical correlations, relationships, and innovative and custom cross plots. 2.2.2. Petrophysical Properties Evaluation. Before performing well logging analysis, it is important to determine petrophysical constants very accurately because any uncertainties or errors in the calculation of these parameters are continued on throughout the petrophysical evaluation process. The constant petrophysical parameters used in the study for the North Sinai well and the Gulf of Suez well are listed below in Table 1 and Table 2. 2.2.2. 1. Shale Volume Determination. Shale contents in the reservoir can result in inaccurate porosity and water saturation estimation from well logs, where shale affects log responses depend on several factors such as shale volume, petrophysical properties of the shale, and shale distribution types in the reservoir (dispersed, laminated, and structural) (G.M. Hamada., 1999). Constant Petrophysical Parameter

Value

Unit

Constant Petrophysical Parameter

Value

Unit

ST BHT TD BS MWT

39.00 149.00 4656.00 12.25 1.76 36.92 -0.05 55.50 110.98 185.00 2.65 2.44 1.10 51.93 6.96 34.07 1 1.358 2 0.025 0.94

C° C° M. In. lb/gal P.U. P.U. µsec/ft. µsec/ft. µsec/ft. GM/cm3 GM/cm3 GM/cm3 API API Ω.m. Unitless Unitless Unitless Ω.m. Ω.m.

ST BHT TD BS MWT

47.78 229.00 3200.50 8.5 1.16 26.28 -0.05 55.50 84.4 185.00 2.65 2.50 1.10 69.19 13.32 4.78 1 1.658 2 0.019 1.95

C° C° M. In. lb/gal P.U. P.U. µsec/ft. µsec/ft. µsec/ft. GM/cm3 GM/cm3 GM/cm3 API API Ω.m. Unitless Unitless Unitless Ω.m. Ω.m.

ϕ Nsh ϕ Nmat

∆Tmat ∆Tsh ∆Tf ρbma ρbsh ρbf GRMax GRMin Rcl a m n Rw Rsh

Table 1. The petrophysical constant parameters used in the study for the North Sinai well. All parameters in the above table defined in Nomenclature section.

ϕ Nsh ϕ Nmat

∆Tmat ∆Tsh ∆Tf ρbma ρbsh ρbf GRMax GRMin Rcl a m n Rw Rsh

Table 2. The petrophysical constant parameters used in the study for the Gulf of Suez well. All parameters in the above table defined in Nomenclature section.

Fig. 1. Well Logs from the North Sinai field, Mediterranean Sea, Egypt. [The track description from left to right: Track 1- CALI: Caliper, BS: Bit Size; Track 2- Depth: Measured Depth; Track 3- GR: Gamma Ray; Track 4- RHOB: Density Log , NPHI: Neutron Log, DT: Sonic Log; Track 5- AT90: Array Induction 90” Resistivity].

Fig. 2. Well Logs from the Gulf of Suez field, Egypt. [The track description from left to right: Track 1- CALI: Caliper, BS: Bit Size; Track 2Depth: Measured Depth; Track 3- GR: Gamma Ray; Track 4- RHOB: Density Log, NPHI: Neutron Log, DT: Sonic Log; Track 5- AT90: Array Induction 90” Resistivity].

So, in order to get a reliable reservoir quality assessment for shaly sandstone formation, it is crucial to accurately determine shale volume (Soto Becerra, R., et al., 2010). Also, in addition to the shale volume estimation, a good knowledge of shale habitat in the reservoir can improve the petrophysical properties and interpretation of shaly sandstone formations, where it is defined which appropriate shale model used for applying the proper water saturation model for a certain reservoir accurately. (Masoumeh Bashiri et al., 2017). Volume of shale can derive from several single indicators (gamma ray, neutron, and resistivity) and double shale indicators (density–neutron, neutron–acoustic, and density-acoustic). Where, each indicator may give either the actual value of shale content or the upper limit of it. The minimum shale volume among all indicators or by using any mean methods (Arithmetic mean, Geometric mean, Harmonic mean, and Median) gives a good approximation of the shale content (Ransom., 1977). In this study, GR indicator (IGR) is a good approximation of shale content, as it gives the minimum value among the other indicators [6 indicators]. 2.2.2.1.1. Shale Volume From GR Tool. The gamma ray tool records a continuous count rate of natural gamma emissions from various formations. If the non clay radioactive minerals are presented with a constant level of radioactivity of shale, in this case gamma ray indicator (IGR) can be expressed as a linear function of shale content as follows (Asquith and Krygowski., 2004):

IGR =

GRlog − GRmin GRmax − GRmin

.................................................................................................................................................. (1)

Where GRlog is gamma ray log reading, GRmin is minimum gamma ray log reading, GRmax is maximum gamma ray log reading. This linear function of shale content overestimated volume of shale than normal (Poupon and Gaymard, 1970). So, nonlinear methods give more accurate results. Therefore, gamma ray indicator (IGR) has been corrected by applying the following two non-linear corrections to obtain the volume of shale (Vsh): 1- Clavier et al. (1971):This equation is useful as a good empirical compensation between the tertiary and older rock equations. (Golnaz Jozanikohan., 2017).

Vsh = 1.7 − 3.38 − (IGR + 0.7 )2

........................................................................................................................................ (2)

2- Steiber (1973):This equation depends on different distribution of shale in sandstone formations versus that in shales. (Golnaz Jozanikohan., 2017). 0.5 * IGR Vsh = ................................................................................................................................................. (3) 1.5 − IGR 2.2.2.2. Total Porosity, Effective Porosity, and Petrophysical Parameters Determination. It is a crucial step to correct neutron and density logs from any environmental effects before calculating total and effective porosities. So, several corrections have been carried out for neutron logs in order to eliminate several factors affecting neutron readings. These factors include matrix type, pressure and temperature, bore hole size, borehole and formation water salinities, and mud cake thickness (Dan M. Arnold and Harry D. Smith, Jr., 1981). While bore hole effect correction has been applied for density logs (Glover, 2014). Accordingly, Total porosities ( ϕ t) and effective porosities ( ϕ e) can be calculated from corrected neutron porosity ( ϕ NC) and corrected density porosity ( ϕ DC) as follows: Corrected density porosity ( ϕ DC) from shale effect can be calculated from the density log in case of shaly formations as follows (Dresser Atlas., 1979): ρbma − ρblog ρb − ρbsh ϕ DC = -Vsh ma .................................................................................................................................................. (4) ρbma − ρb f ρbma − ρb f Where ρbma is the rock matrix density, ρblog is the density log reading, ρb f is the fluid density, ρbsh is the density value in front of shale zone.

While, corrected neutron porosity from shale effect ( ϕ N c ) can be calculated in case of shaly sand reservoirs from the given

equation (Schlumberger, 1974; Doveton, 1999): ϕ N = ϕ N − Vshϕ N ..................................................................................................................................................................... (5) c

sh

Where ϕ N

c

is corrected neutron porosity from shale effect, ϕ N is neutron log reading, and ϕ N

in front of shale zone. The total porosity ( ϕ t) is determined by averaging the values of ( ϕ D C ) and ( ϕ N C )

sh

is neutron porosity value

ϕt =

ϕ DC + ϕ N C

....................................................................................................................................................................... (6) 2 Finally, effective porosity ( ϕe ) can be calculated from the given equation (Hill et al., 1979).

ϕe = ϕt (1 − Vsh )

....................................................................................................................................................................... (7)

Other petrophysical parameters such as Rw, m, and a, are derived from Pickett's plot, lithology determination achieved by using simultaneous equations.

2.2.2.3. New Method to Determinate Volumes of Shale Distribution Types. Shale is distributed in the porous reservoir in different forms (dispersed, laminate, and structural) (Fig. 3.), where:1. Laminated shale: Thin lamina of clay intercalated with sandstone affects the porosity and vertical permeability of the reservoir. 2. Structural shale: Shale occurred in the rock as grains, constitutes as an integral part of the rock matrix and does not affect the porosity of the formation. 3. Dispersed shale: Clay occurred in different forms in the sandstone formations (pore filling, pore lining, and pore bridging) (Fig. 4.) (John W. Neasham, 1977). It severely reduces porosity and permeability of the sandstone reservoirs (Dewan., 1983).

Fig. 3. Different shale distribution types in the reservoir formation (After, Schlumberger)

Fig. 4. Different clay modes authigenic occurrence in sandstone reservoirs (Modified after Neasham, 1977). Pore-filling: clays attached as discrete particles to pore walls or occupying inter-granular pores. Pore-lining: clays attached to pore walls forming a thin (<=12 microns) and continuous clay mineral coating. Pore-bridging: clays attached to pore walls and extend to a pore or pore throat making a bridging effect. (John W. Neasham, 1977)

Volumes of shale distribution type (laminated, dispersed, and structural shale) can be calculated based on a method proposed by Ruhovets and Fertl (1982) as follows:Laminated shale presence in the reservoir reduces effective porosity ( ϕ e) according to the equation:

ϕ e = ϕ max (1 - Vsh) .................................................................................................................................................................. (8) Where ϕ max is the porosity in clean sand (fraction), Vsh is the shale volume (fraction) While, dispersed shale presence in the formation, reducing porosity as follows: ϕ e = ϕ max - Vsh .................................................................................................................................................................. (9) Whereas, structural shale has no effect on reservoir porosity. ................................................................................................................................................................ (10)

ϕ e = ϕ max

Any mixture of shale distribution types may exist in the reservoir, where in many cases effective porosity depends only on shale content and shale type. So, shale distribution types can be determined from the relationship between porosity in the clean sandstone reservoir, effective porosity in shaly sandstone, and the volume of shale. If a shaly sandstone reservoir contains only laminated shale (VshL), its volume can be calculated as follows: VshL= ( ϕ max - ϕ e) / ϕ max

........................................................................................................................... (11)

By comparing this volume of laminated shale to shale volume Vsh, three conditions may exist as follows:

1- If VshL = Vsh; Then, laminated shale only is present as defined by Equation (11).

2- If VshL < Vsh; Then, laminated and structural shales are present. Since Equation (11) gives the proper volume of laminated shale, the volume of structural shale (VshS) can be determined as follows:................................................................................................................................ (12) VshS = Vsh - VshL 3- If VshL > Vsh; Then, dispersed or combination of dispersed and laminated shales are present; thus, Equation (11) is not adequate to determine the proper amount of laminated shale. Since there is no structural shale present, the individual volumes of dispersed and laminated shales can be found as follows: Volume of dispersed shale (VshD) is defined as VshD = ( ϕ max - ϕ e.) ................................................................................................................ (13) With the volume of laminated shale (Vshlm) which replaces the sand matrix expressed as Vshlm= Vsh - ( ϕ max - ϕ e) ................................................................................................................................ (14) The part of laminated shale which replaces the pore space is defined as Vshlp = [Vsh - ( ϕ max - ϕ e)] * [ ϕ max / (1- ϕ max)] ............................................................................................................... (15) Total laminated shale volume, then can be expressed as VshL = [[Vsh - ( ϕ max - ϕ e)] * 1]+ [ ϕ max / (1- ϕ max)] = = [Vsh - ( ϕ max - ϕ e)] * [(1- ϕ max)] .......................................................................................................................... (16) And the volume of dispersed shale (VshD) is calculated as .................................................................................................................................... (17) VshD = Vsh - VshL. According to the above approach, different scenarios for shale distribution types arisen based on their percentages with each other, as follows:Scenario 1- Dispersed Shale Type Only. Scenario 2- Dispersed and Laminated Shale Types. Scenario 3- Laminated Shale Type Only. Scenario 4- Laminated and Dispersed Shale Types. Scenario 5- Laminated and Structural Shale Types. Scenario 6- Structural Shale Type Only. Scenario 7- Structural and Laminated Shale Types. The previous method did not consider the possibility of a Dispersed-Structural Distribution, or a Three-Type Distribution. So, the author applies a new approach, on the previous method, proposed by Willis et al. (2017) based on ratio analysis to determine the numerical fractions of the contributing shale to resolve this case, this dilemma arisen when a point falls in between laminar and dispersed shale lines (yellow point in Fig.5), this point can be interpreted as laminated-dispersed or dispersed-structural type, or a combination of three shale distribution types. So, ratio analysis proposed by Willis et al. (2017) (Fig.6), can solve this issue as follows: • All shale distribution type is laminar and no structural shale occurred, having a structural-to laminar ratio of zero-to-one (VshS/VshL = 0/1); • All shale distribution type is structural and no laminar shale, with a structural-to-laminar ratio of one-to-zero (VshS/VshL = 1/0); • A combination of laminar and structural shale distribution type, with structural-to-laminar ratios ranging from zero-to-one (VshS/VshL = 0/1) to one-to-zero (VshS/VshL = 1/0). Back to our example, (yellow point in Fig.5.), by applying the ratio approach we can discern variations between the two-type laminar-dispersed model, the three type model, or the two type dispersed structural model. It also provides the percentage of the shale distribution types, adding a numerical component to the graphical approach for a better understanding. As is clear from Fig. 6, moving from the laminar-dispersed model to the dispersed-structural model, the amount of laminar shale decreases, dispersed shale increases, and structural shale increases. So, accordingly the above scenarios will be extended to include Scenario 8- Structural and Dispersed Shale Types. Scenario 9- Dispersed and Structural Shale Types.

2.2.2.4. Techniques Used to Validate the New Volumetric Determination Of Shale Distribution Types Method. Thomas Stieber crossplot is a simple method using conventional well logging data (total porosity ( ϕ t) and shale volume (Vsh)) to differentiate between different shale distribution types with acceptable accuracy without using expensive instruments or analyses. In order to validate the calculations of the new proposed volumetric model of shale distribution types for every available scenario, the results of each individual scenario plotted in the famous Thomas Stieber crossplot (Thomas et al., 1975) (Fig. 7) to check its quality, validity, and accuracy, in the way that every data point in each scenario should be falling in the right supposed field of shale distribution type on the Thomas Stieber rhombus. To facilitate the validation of the new volumetric shale distribution types method, the author introduces a unique dual scale color legend where the right side scale of the legend represents the percentage of a certain predominant shale type, while the left side scale of the legend represents the percentage of the other minor shale type to easily check the distribution of the data points fall in the correct shale habitat fields very smartly and effectively (Fig. 7).

Fig. 5. Thomas Stieber crossplot showing an example of three shale distribution type case (yellow point), where this point can be interpreted as laminated-dispersed or dispersed-structural type (Modified after Willis et al., 2017).

Fig. 6. The ratio analysis for yellow point in the laminated-dispersed shale distribution type example (Fig. 5.), shows the volume of shales can be recognized for each distribution case to aid in the graphical analysis. Moving from the laminar-dispersed model to the dispersed-structural model, the amount of laminar shale decreases, dispersed shale increases, and structural shale increases. (Modified after Willis et al., 2017).

Fig. 7. The famous Thomas Stieber crossplot used in this study. Color of lines: LIGHT BLUE: Structural Shale, MAGENTA: Dispersed Shale, GREEN: Laminated Shale. Area (A) represents dispersed shale region, line (B) represents a laminated shale region, area (C) represents the structural shale region. The dual scale color legend, represents Scenario: 4 (laminated shale volume> dispersed shale volume, structural shale volume=0), where the right side scale of the legend represents the percentage of the predominant laminated shale type (VLam). While, the left side scale represents the percentage of the minor dispersed shale type (VDisp) is used to easily check the distribution pattern of point(s) for scenario 4 in the Thomas Stieber rhombus. DATA POINTS EXAMPLE: The data points move from bottom to top (dark green arrow) due to increase of the percentage of laminated volume at the expense of the percentage of dispersed volume at constant shale volume (30%) and vice versa (light blue arrow), while data points move from left to right when the ratio between laminated shale percentage and dispersed shale percentage remains constant with increasing shale volume (light green right arrow) and vice versa (light green left arrow). Also, another useful feature added to measure the percentage of data points falls in the correct shale habitat fields [Accuracy, 100.00%] located in the lower right corner of the crossplot.

Another crossplot used in this study for estimation of shale distribution types is a classic neutron porosity vs. density porosity (Fig. 8).

Fig. 8. Neutron porosity and density porosity crossplot used in this study. Color of lines: LIGHT BLUE: Structural Shale, MAGENTA: Dispersed Shale, GREEN: Laminated Shale. Area (A) represents dispersed and laminated shale region, Area (B) represents laminated and structural shale region.

2.2.2.5. Water Saturation Estimation in the Shaly Sand Formations. The presence of shales makes water saturation so difficult to achieve due to the conductivity of shales, which can bypass high resistive hydrocarbons (Toby Darling, 2005). So, many scientists developed several models to evaluate water saturation in shaly sandstone reservoirs based on different assumptions, approaches, models, methods, and laboratory tests, which may therefore lead to underestimation or overestimation in water saturation and hence hydrocarbon saturation. Two models are selected for the purpose of this study as follows.

2.2.2.5.1. Dispersed Shale Model. Dispersed shale formed as a result of chemical reactions between water and rock mineral constituents, the occurrence of dispersed shale in the reservoir even in a small percentage can wipe out effective porosity ( ϕe ) and permeability. DeWitte (1950) assumption for water saturation in shaly reservoirs depends on the formation conducts electrical current through a network consisted of water and dispersed shale. The equation for this model is

Sw =

 aRw   ϕ2 R  im t

  q ( Rshd − Rw )  2  q ( Rshd + Rw )  +  −       2 Rshd 2 Rshd     1− q

................................................................................................ (18)

Where ϕim is the inter-matrix porosity (%), sonic porosity, Rshd is the resistivity of dispersed shale (Ω.m.), the q parameter is the sonic response, and the given equation for q parameter is

q=

ϕ S −ϕ D

....................................................……………………………. (18-A)

ϕS Where

ϕS

is

the

sonic

porosity

(%),

ϕD

is

the

density

porosity

(%).

2.2.2.5.2. Laminated Shale Model About 30% of the world’s estimated hydrocarbon extensive reserves occurred in laminated shaly sand reservoirs. (Prasad et al., 2006). These reservoirs occurred in all environments of depositions, such as deltas, submarine fans, fluvial point bars and turbidities (Quirein et al., 2012).

Poupon et al. (1954) introduced a model for water saturation in laminated shaly sandstone formations based on that the reservoir is comprised of several thin parallel layers of shale intercalated with sand layers. The equation for this model is

V V 1 = sh + sd Rt Rsh Rsd

.............................................................................................................................................................. (19)

Where Vsh is the Shale volume (%), Rsh is the Resistivity of the pure shale zone (Ω.m.), Vsd is the Sand volume = (1 - Vsh) (%), Rsd is the Resistivity of pure sandstone zone (Ω.m.). This is a simplified deterministic formula that required a good knowledge of Rsd. So, it is a big challenge to achieve satisfactory precision in detecting laminated shaly sandstone reservoirs by using traditional well logging methods. Invention of phasor induction and multicomponent induction logging, or similar tools that measure Rv and Rh components are convenient for the identification of these complex laminated shaly sandstone reservoirs. Unfortunately, phasor induction tools and a triaxial induction tool are expensive and add additional cost (Fernando Angel., 2017), multicomponent induction resistivity tool is rarely used in major domestic petroleum fields (Fei et al., 2016), and in several cases many fields were developed before these advanced tools were invented and there is a necessity to review the evaluation from these fields to identify the hydrocarbon bearing laminated shaly sandstone reservoirs and to discriminate them from poor quality zones (Rick et al., 2017). As a result, identification and evaluation of laminated shaly sandstone significantly depend on conventional logging tools, array lateral resistivity tool and array induction resistivity tool, and common image logging (Fei et al., 2016). So, several scientists are tried to estimate the vertical resistivity (Rv) from the horizontal resistivity (Rh) in the absence of triaxial induction resistivity measurements, such as Passey et al. (2004), Tabanou et al. (2002), Ollivier et al. (2002), and Woodhouse et al. (1984). Fernando Angel (2017) performed a study on three methods based on the work of Passey et al. (2004), Tabanou et al. (2002), and Woodhouse et al. (1984), to investigate and estimate the vertical resistivity (Rv) and anisotropy of resistivity (A) from high resolution horizontal resistivity logging, in the absence of triaxial induction resistivity tools on six wells drilled with different mud types (oil and water base muds), with different maximum well deviation angles ranging in values from 3.20° to 33.00°, and in different zones saturated with oil and gas. Furthermore, a comparison between actual Rv and Rh estimated from triaxial induction resistivity logging and calculated Rv and Rh from three different methods revealed that these methods really gave very satisfactory results compared with actual triaxial induction measurements (See Fig. 9). Fig. 9. Summary of the evaluation of three methods compared with actual triaxial induction resistivity measurements. Green color indicating a good method, Red color indicating a bad method and finally Orange color indicating a somewhat useful method (After, Fernando Angel., 2017). NOTE. Method: 1 represents the estimated of Rv from a convolution average filter, Method: 2 represents the estimated of Rv from the estimation of the resistivity of the sand., Method 3 represents the estimated of Rv from the deep resistivity and GR tool.

Based on the best method that has good satisfactory results and worked well among the three methods, as well as the availability of the data in this study, the author developed a new approach and algorithms, a modified LSSA interpretation model, to resolve water saturation in the thin sand-shale interbeds using Array Induction 90” Resistivity (AT90) and gamma ray log, responds to the actual shale volume, (Method 3) as input parameters to calculate Rh and Rv, and accordingly Rsd can be estimated of each point in the reservoir as follows: Shale volume (Vsh) can be determined from (Section 2.2.2.1.1., equations 1, 2, and 3). Volume of sandstone (Vsd) can be calculated from the following equation. Vsd= 1- Vsh

........................................................................................................................................................................... (20)

Note, laminated shaly sand analysis (LSSA) interpretation model or method proposed by Fernando Angel (2017) assumed that the non shale component (1-Vsh), rock matrix, is only composed of pure sandstone matrix. But in reality, it may contain other facies than the assumed pure sandstone matrix, e.g., limestone or dolomite. So, it is crucial to use sand volume from simultaneous equations as it gives the actual volume of sand in the formation. The horizontal resistivity (Rh) and vertical resistivity (Rv) are estimated from the following parallel and series relationships (Klein et al., 1997).

V V 1 = sh + sd Rh Rsh Rsd

............................................................................................................................................................ (21)

Where Rh is the horizontal resistivity of laminar shale and can be estimated as Rt from AT90. Note, the author used VLam derived from new volumetric shale distribution types model (Section: 2.2.2.3.) for better accuracy, instead of using Vsh derived from GR logs, utilizing VLam derived from shale distribution types model proposed by Thomas-Stieber et al. (1975, 1977), or from a classic LSSA interpretation model based on four resistivity components Rh, Rv, shale horizontal resistivity (Rshh), shale vertical resistivity (Rshv) that derived from 3DEX, or similar tools. Then, by solving the following equation, Rsd can be estimated. Rv = Vsd*Rsd + Vsh * Rsh ..............................................................................................................................................(22)

The author applied two convolution filters on Rv and Rh values just to enhance signal, but it is not necessarily a needed step. Then, total porosity of sand with dispersed shale content ( ϕ tsd) and effective porosity of sand without dispersed shale content ( ϕ esd) are calculated from the given formulas (Hayden et al., 2009 and Mollison et al., 2001).

ϕ tsd=

(ϕt − VLamϕ sh ) (1 − VLam )

ϕ esd = ϕ tsd-

...................................................................................................................................................... (23)

(VshDϕ sh ) (1 − VLam )

..................................................................................................................................................... (24)

Where VshD is the dispersed shale volume (%), ϕ sh is the shale porosity (%). Finally, by applying Archie equation (1942) and knowing Rsd, ϕ esd, m, Rw, and a, water saturation for laminated shale type (Sw_sd) can be achieved as follows:-

Sw_sd= n

aRw

ϕ e sd m Rsd

...................................................................................................................................................................... (25)

Where Sw_sd is the water saturation of sandstone layer (%), n is the saturation exponent (Unitless).

2.2.2.5.2. 1. Workflow of the New Approach to Determine Water Saturation In Laminar Shale Reservoirs. • Estimation of laminated shale volume (VLam) and dispersed shale volume (VDisp) from the novel volumetric shale distribution types in Section. 2.2.2.3. • Determination of sand volume (Vsd) from the simultaneous equations. • Determination of Rv, and Rsd from equations 21, and 22, by knowing Rsh, VLam, Vsd, and Rh as Rt derived from AT90. • Determination of total porosity of sand ( ϕ tsd) and effective porosity of sand ( ϕ esd) from equations 23, and 24 respectively. • Determination of water saturation (Sw_sd) using Archie equation (1942) by knowing resistivity of sand Rsd and effective sand porosity ϕ esd as inputs. So, if dispersed shale is a predominant shale type in the reservoir, the algorithms based on the processing flow of the novel method will trigger dispersed shale model to calculate water saturation (Section 2.2.2.5.1), otherwise the algorithms will trigger laminated shale model (Section 2.2.2.5.2.). The processing flow of the new water saturation method is illustrated in Fig. 10. Anisotropy ratio (A) can be estimated from the given formula (Klein., 1996). A=

Rv Rh

....................................................................................................................................................................................... (26)

Where A is the Anisotropy ratio (Unitless). The anisotropy ratio is a useful parameter to determine the anisotropy level of the reservoir (Anderson et al., 2005) and differentiate between potentially thin bed or laminated sandstone reservoir from non-productive zone (Mollison et al., 2001). There are two kinds of electrical anisotropy, macroscopic anisotropy where the thickness of laminae is below the device vertical resolution (Mezzatesta et al., 2006), and the other type occurred when a flat elongated shape grains, such as mica or illite, deposited in parallel orientation to the bedding, this kind of anisotropy called microscopic anisotropy (Yin et al., 2008) Several sources can cause electrical anisotropy, such as anisotropic shales, thin shale-sand laminations, grain size variations within the sands, tight calcite streaks intercalations (Chanh et al., 2007), occurrence of calcite as sand and calcite cement as pore filling material, and occurrence of silt in sands (Tyagi et al., 2009). In this study, the anisotropy ratio (A) is used to identify laminated shaly sand zones and also as a benchmark to examine how effectively the proposed novel water saturation method activated for laminated shale model, trigger independently of anisotropy ratio, to deal with complex problematic laminated sandstone reservoirs.

2.2.2.5.2. 2. Limitations of the New Technique for Water Saturation Determination in Laminated Shaly Sandstone Reservoir. Perhaps the main limitations of this technique are, the natural gamma ray radiation should be due to shale presence and not from other minerals, if so, this method will not work, the quality of the data, the resolution of the tools and natural gamma ray logs should be free from bad data and noises that affect the quality of the signal (Fernando Angel., 2017). Also, in case of horizontal or highly deviated drilled wells with dipping layers, I think it is better to use more advanced approaches to resolve these complex laminated shaly sand reservoirs.

2.2.2.5.3. Bulk volume of

water (BVW)

Water saturation is a fraction of formation porosity, either total or effective porosities, that occupied by water. In some instances, it is beneficial to determine bulk volume water (BVW) which is water that occupied a fraction of rock volume. Bulk volume of water has some useful applications. Where within a certain reservoir, if the bulk volume of water values remain constant or nearly constant throughout the reservoir, this may be a sign that the reservoir is at or closeby irreducible water saturation (Swirr), also this

Fig. 10. The processing flow of the novel water saturation method.

may be taken as an indication of grain size, where irreducible water saturation increased when the grain size of sediment decreased and vice versa (Halliburton, 2001). The bulk volume of water can be estimated from the given formula (Asquith and Gibson, 1982). BVW = ϕ Sw

.................................................................................................................................................................................. (27)

Where Sw is the water saturation of the reservoir (%).

3. Application of the New Method to Field Data 3.1. Validation of Volumetric Model For Shale Distribution Types 3.1.1. North Sinai Field First, before validating the new volumetric shale distribution types model; let's determine shale distribution types qualitatively by traditional interpretation methods using different crossplots to see the superiority of the novel model over the other traditional methods. Fig. 11. Shows the Thomas Stieber crossplot for the North Sinai well, Egypt. The predominant shale distribution type is roughly laminated shale type (total laminated shale volume ≈ 55%), and minor constituent is dispersed shale (total dispersed shale volume ≈ 40%) with a few points located in structural shale region (total structural shale volume ≈ 5%). While, Fig. 12. Shows the relation between neutron porosity and density porosity crossplot for the North Sinai well, Egypt., this crossplot is indicated deceptively that the predominant shale type is dispersed shale (total dispersed shale volume ≈ 65%), and a lesser constituent is laminated shale (total laminated shale volume ≈ 28%) with a few points located in structural shale region (total structural shale volume ≈ 7%). The big differences and discrepancies in the estimation of dispersed and laminated shales between these two crossplots are due to that the neutron porosity and density porosity crossplot did not take into account the gravity of shale contents that shifted most of the points

downward in southwesterly direction, also the laminated shale zones shifted to dispersed shale field due to that the sand laminations have porosity reduced by dispersed shale. This pitfall is one of the main drawbacks for this crossplot. (See Fig. 13.).

Fig. 11. Thomas Stieber Crossplot for the North Sinai Well, Mediterranean Sea, Egypt. Showing shale distribution types.

Fig. 12. Neutron porosity and density porosity crossplot for the North Sinai Well, Mediterranean Sea, Egypt. Showing shale distribution types, color coded by volume of shale.

Figs., 14 - 19. Represent the new graphical techniques to illustrate the distributions of each scenario of the new volumetric shale distribution types model on the Thomas Stieber crossplot for the North Sinai well, Egypt as follows: Fig. 14. Thomas Stieber crossplot for the North Sinai well, Egypt. (Scenario 1: Dispersed Shale Type Only), shows that a lesser points distributed around the dispersed shale line [Accuracy, 19.89%], the increase in volume of dispersed shale results in severely decrease in the values of total porosity. Most of the points fall outside the rhombus (Triangle L, Low Porosity Effect). These data points represent 80.11% of plotting points and about 11.80% of the whole reservoir zones. The possible reasons for low porosity effect are due to several aspects such as, a conventional Thomas-Stieber model assumed that the presence of shale is the main reason for reduction of porosity, but it did not take into account the reduction of porosity by any diagenetic processes, including burial, compaction, and cementation (Hossain Z., and Vera de Newton P., 2014), also any presence of silt or the occurrence of calcite as sand

Fig. 13. Neutron porosity and density porosity crossplot for the North Sinai Well, Mediterranean Sea, Egypt. Showing laminated and dispersed shale distribution types, Scenario [4]. Color coded based on the criteria [VLam. > VDisp; VStr.=0].

and calcite cement as pore filling materials can reduce porosity significantly (Hossain Z., and Vera de Newton P., 2014; Tyagi et al., 2009). Moreover, these complex facies can cause internal electrical anisotropy (Hossain Z., and Vera de Newton P., 2014). Fig. 15. Thomas Stieber crossplot for the North Sinai well, Egypt. (Scenario 2: Dispersed and Laminated Shale Types), shows that the dual scale color legend indicates that all data points are distributed in the right shale habitat field between dispersed and laminated regions [Accuracy, 98.17%], where the majority of points are located near dispersed region and shifted toward laminated shale type region as the volume of laminated shale increases at the expense of dispersed one. Fig. 16. Thomas Stieber crossplot for the North Sinai well, Egypt. (Scenario 4: Laminated and Dispersed Shale Types), shows that the dual scale color legend indicates that all points are distributed in the right shale habitat field between laminated and dispersed regions [Accuracy, 88.99%], where an increase in volume of dispersed shale type can significantly decrease porosity leading to shift half of the data points drastically downward, Region (A), due to that the sand laminations have porosity reduced by dispersed shale. Fig. 17. Thomas Stieber crossplot for the North Sinai well, Egypt. (Scenario 6: Structural Shale Type Only), shows that the points distributed around the structural shale field [Accuracy, 56.86%], a few points shifted even above designated area of structural shale and outside rhombus due to organic matter or gas effects. As the data corrected for gas effect, so the organic matter effect will be the cause of these shifts Fig. 18. Thomas Stieber crossplot for the North Sinai well, Egypt. (Scenario 7: Structural and Laminated Shale Types), shows that the all points fall in their correct and supposed locations between structural shale and laminated shale lines [Accuracy, 90.99%]. Fig. 19. Thomas Stieber crossplot for the North Sinai well, Egypt. (Scenario 8: Structural and Dispersed Shale Types), shows that the dual scale color legend indicates that all points are distributed in their right shale habitat field between dispersed and structural regions [Accuracy, 96.73%], where, presence of dispersed shale has a severe impact to reduce porosity of structural shale. As a result, almost all data points pull down from structural shale field to laminated field. This crossplot is a direct application of using ratio analysis. Without applying ratio analysis, these data points can be interpreted deceptively as laminated and structural shale types (Scenario 5). Based on the results attained from the above discussions, the qualitative traditional methods to identify shale habitats by using Thomas Stieber crossplot for the North Sinai field shows that the predominant shale type is laminated shale with minor dispersed shale and a few amounts of structural shale, while neutron porosity and density porosity crossplot implies that the main predominant shale type is dispersed shale with minor laminated shale and a few amounts of structural shale. While, the results of the new volumetric method reveal that the precise percentages of laminated, dispersed, and structural shales are 62.68%, 14.87%, and 22.45%, respectively. The level of accuracy for the new deterministic model of shale distribution types using Thomas Stieber crossplot found to be excellent.

3.1.2. Gulf of Suez Field Again, before validating the new volumetric shale distribution types model; let's determine shale distribution types qualitatively by traditional interpretation methods using different crossplots to see the superiority of the novel model over the other traditional methods. Fig. 20. Shows the Thomas Stieber crossplot for the Gulf of Suez well, Egypt. The predominant shale distribution type is roughly dispersed shale type (total dispersed shale volume ≈ 86%), and minor constituent is laminated shale type (total laminated shale volume ≈ 10%) with a few points located in structural shale region (total structural shale volume ≈ 4%). Fig. 21. Shows the relation between neutron porosity and density porosity crossplot for the Gulf of Suez well, Egypt. This crossplot also indicate that the predominant shale type is dispersed shale (total dispersed shale volume ≈ 85%), and minor constituent is laminated shale type (total laminated shale volume ≈ 10%) with a few points located in structural shale region (total structural shale volume ≈ 5%). (Figs., 22 - 27). Illustrate the distribution of each scenario of the new volumetric shale distribution types model on Thomas Stieber crossplot by the aid of the new graphical techniques as follows:

Fig. 14. Thomas Stieber Crossplot for the North Sinai Well, Mediterranean Sea, Egypt. Showing dispersed shale distribution type only, Scenario [1]. Color coded based on the criteria [VDisp. >0; VLam. =0; VStr.=0]. Triangle (L), shows Low porosity effect.

Fig. 15. Thomas Stieber Crossplot for the North Sinai Well, Mediterranean Sea, Egypt. Showing dispersed and laminated shale distribution types, Scenario [2]. Color coded based on the criteria [VDisp. > VLam.; VStr.=0].

Fig. 16. Thomas Stieber Crossplot for the North Sinai Well, Mediterranean Sea, Egypt. Showing laminated and dispersed shale distribution types, Scenario [4]. Color coded based on the criteria [VLam. > VDisp.; VStr.=0]. Region (A), sand laminations have porosity reduced by dispersed shale.

Fig. 17. Thomas Stieber Crossplot for the North Sinai Well, Mediterranean Sea, Egypt. Showing structural shale distribution type only, Scenario [6]. Color coded based on the criteria [VLam. =0; VDisp.=0; VStr. >0].

Fig. 18. Thomas Stieber Crossplot for the North Sinai Well, Mediterranean Sea, Egypt. Showing structural and laminated shale distribution types, Scenario [7]. Color coded based on the criteria [VStr. > VLam.; VDisp.=0].

Fig. 19. Thomas Stieber Crossplot for the North Sinai Well, Mediterranean Sea, Egypt. Showing structural and dispersed shale distribution types, Scenario [8]. Color coded based on the criteria [VStr. > VDisp.; VLam.=0].

Fig. 20. Thomas Stieber Crossplot for the Gulf of the Suez Well, Egypt. Showing shale distribution types.

Fig. 21. Neutron porosity and density porosity crossplot for the Gulf of Suez Well, Egypt. Showing shale distribution types, color coded by volume of shale.

Fig. 22. Thomas Stieber crossplot for the Gulf of Suez Well, Egypt. (Scenario 1: Dispersed Shale Type Only), shows that a nearly half of points distributed around the dispersed shale line perfectly (Accuracy, 45.68%), where the increase in dispersed shale volume can severely reduce the values of total porosity, while the rest of the points laid outside the rhombus (Triangle L, Low Porosity Effect). These points represent 54.32% of plotting points and about 8% of the whole reservoir zones. There are several factors that can cause low porosity effects such as, reduction of porosity by any diagenetic processes (burial, compaction, and cementation), also the occurrence of silt in sandstone or the presence of calcite as sand and calcite cement as pore filling materials can reduce significantly porosity. Moreover, these complex lithology can cause internal electrical anisotropy (Hossain Z., and Vera de Newton P., 2014). Fig. 23. Thomas Stieber crossplot for the Gulf of Suez Well, Egypt. (Scenario 2: Dispersed and Laminated Shale Types), shows that the dual scale color legend indicates that all points are distributed and fall in the correct locations between dispersed and laminated regions (Accuracy, 96.40%), where the majority of points are located near dispersed region and shifted toward to laminated shale type region as laminated shale volume increases at the expense of dispersed shale volume. Fig. 24. Thomas Stieber crossplot for the Gulf of Suez Well, Egypt. (Scenario 4: Laminated and Dispersed Shale Types), shows that the dual scale color legend indicates that all points are distributed in the right locations between laminated and dispersed regions (Accuracy, 94.18%), where an increase in volume of dispersed shale type can significantly decrease porosity leading to pull large amounts of data points downwards, region (A), due to the sand laminations have porosity reduced by dispersed shale. Fig. 25. Thomas Stieber crossplot for the Gulf of Suez Well, Egypt. (Scenario 6: Structural Shale Type Only), shows that the points distributed around the structural shale field (Accuracy, 60.00%), one point shifted even above designated area of structural shale due to organic matter or gas effects. As the data corrected for gas effect, so the cause of this shift is due to organic matter effect. Fig. 26. Thomas Stieber crossplot for the Gulf of Suez Well, Egypt. (Scenario 7: Structural and Laminated Shale Types),

Fig. 22. Thomas Stieber Crossplot for the Gulf of Suez Well, Egypt. Showing dispersed shale distribution type only, Scenario [1]. Color coded based on the criteria [VDisp. >0; VLam. =0; VStr.=0]. Triangle (L), shows Low porosity effect.

Fig. 23. Thomas Stieber Crossplot for the Gulf of Suez Well, Egypt. Showing dispersed and laminated shale distribution types, Scenario [2]. Color coded based on the criteria [VDisp. > VLam.; VStr.=0].

Fig. 24. Thomas Stieber Crossplot for the Gulf of Suez Well, Egypt. Showing laminated and dispersed shale distribution types, Scenario [4]. Color coded based on the criteria [VLam. > VDisp.; VStr.=0]. Region (A), sand laminations have porosity reduced by dispersed shale.

Fig. 25. Thomas Stieber Crossplot for the Gulf of Suez Well, Egypt. Showing structural shale distribution type only, Scenario [6]. Color coded based on the criteria [VLam. =0; VDisp.=0; VStr. >0].

shows that the all points located in the correct and expected locations between structural shale and laminated shale lines (Accuracy, 100.00%). Fig. 27. Thomas Stieber crossplot for the Gulf of Suez Well, Egypt. (Scenario 8: Structural and Dispersed Shale Types), shows that the dual scale color legend indicates that all points are distributed in the correct locations between dispersed and structural regions (Accuracy, 99.00%), where, presence of dispersed shale has a severe impact to reduce porosity of structural shale, this negative effect pull down almost all points from structural shale field to laminated field. This crossplot is a direct application of using ratio analysis. Without applying ratio analysis, these points can be interpreted wrongly as laminated and structural shale types (Scenario 5). Based on aforementioned discussions, the qualitative traditional methods to identify shale habitats using Thomas Stieber crossplot for the Gulf of Suez well shows that the predominant shale type is dispersed shale with minor laminated shale and a few amounts of structural shale, and neutron porosity and density porosity crossplot implies the same conclusion. But the results of new volumetric method show that the actual percentages of the laminated shale, dispersed shale, and structural shale types are 63.23%, 24.00%, and 12.77%, respectively. The level of accuracy for the new deterministic model of shale distribution types using Thomas Stieber crossplot found to be excellent.

3.2. Comparative Study between New Water Saturation Method and Traditional Water Saturation Method [Note, to avoid any bias in comparison between new method and traditional method, since a new proposed method depends on two water saturation models (dispersed and laminated shale type models). So, logs comparisons and statistical correlations were carried out between the bulk volume of water based on application of the new method on correct shale type zones and corresponding bulk volume of water based on an erroneous application of traditional method on wrong shale type zones. There are several factors that affect electrical anisotropy of reservoir rather than the presence of laminated shale in the formation, as aforementioned earlier, so in order to keep and maintain focus on the laminated shale problem the author used several filters to exclude shale zones and limestone layers from logs presentations and comparative study.] A comparative study has been performed between the bulk volume of water derived from new method and bulk volume of water derived from traditional method by using statistical correlations and graphical logs display as follows.

3.2.1. North Sinai Field The proposed water saturation model for the North Sinai field based on traditional methods, from Section 3.1.1., is laminated shale model. Fig. 28. Shows a comparison between the bulk volume of water based on application of the new method on dispersed layers only (BVW_N_D) and bulk volume of water based on an erroneous application of laminated shale model derived from traditional methods on dispersed layers only (BVW_L_D_T). This comparison reveals that the traditional methods yields drastic lower values for water saturation than that ones from the new water saturation method, makes water saturation from traditional method more optimistic and hence inaccurate reservoir assessment. Although the two models show a good responsiveness and similar patterns with each others (Fig.28, Track 5), there is a big variance in values between two methods. Where BVW_N_D is greater than BVW_L_D_T, in 99.55% of the entire reservoir with the maximum difference in values reaches up to 12.51%, the maximum error in water saturation estimation reaches up to 60.99%, and the average difference in values reaches up to 6.30%, the average error in water saturation estimation reaches up to 61.85%, while the bulk water volumes from both two methods are identical in 0.45% of the whole reservoir. Also, water saturation from the new method shows a good match with the water saturation derived from core analysis (black dots shown on Fig. 28, Track 3). There are consistencies and sensitive responsiveness between anisotropy ratio and application of the laminated shale model (PURPLE FLAG, Fig. 28. Track: 9), which has been triggered according to robust procedures of the new water saturation method. So, wherever the application of the laminated shale model for water saturation has been triggered, there is electrical anisotropy in front of laminated shale zones (Fig. 28. Track: 8) throughout the entire reservoir. Also, a GREEN FLAG (Fig. 28. Track: 9), which represents the plotting data points in the Triangle L (See Fig. 14), located mainly between depths 2873.7 m and 2927.25 m, and between depths 3562.95 m and 3584.55 m, these low porosity zones show a lesser volume of water and a kick in the resistivity (See

Fig. 1), and high values of the anisotropy ratio giving a false indication that these zones are really promising hydrocarbon producers. Despite this deceptive interpretation and conclusion, the application of the new laminated shale method works in a different way and effectively has not been triggered. On the other hand, the statistical correlation between BVW_N_D and BVW_L_D_T (Fig. 29.), shows a strong positive linear relationship (R2=0.91), also a statistical correlation between water saturation values from the new method (Sw_New) and water saturation values from core measurements (Sw_Core) (Fig. 30), indicates a strong positive linear relationship (R2=0.82).

Fig. 26. Thomas Stieber Crossplot for the Gulf of Suez Well, Egypt, showing structural and laminated shale distribution types, Scenario [7]. Color coded based on the criteria [VStr. > VLam.; VDisp.=0].

Fig. 27. Thomas Stieber Crossplot for the Gulf of Suez Well, Egypt, showing structural and dispersed shale distribution types, Scenario [8]. Color coded based on the criteria [VStr. > VDisp.; VLam.=0].

3.2.2. Gulf of Suez Field The proposed water saturation model for the Gulf of Suez field based on traditional methods, from Section 3.1.2., is dispersed shale model. Fig. 31. Shows a comparison between the bulk volume of water based on application of the new method on laminated shale zones only (BVW_N_L) and bulk volume of water based on an erroneous application of dispersed shale model derived from traditional methods on laminated shale layers only (BVW_D_L_T). This comparison reveals that water saturation from the traditional method is lower in values than the new water saturation method, makes water saturation from traditional method more optimistic and hence inaccurate estimation of hydrocarbon reserves. Also, the two models show neither any responsiveness nor similar patterns to each other (Fig.31, Track 5).

Fig. 28. Comparison between novel water saturation method and water saturation based on the sole application of laminated shale model (traditional methods), for the North Sinai field, Mediterranean Sea, Egypt. [The track description from left to right: Track 1- Vsh: Shale volume; Track 2- Depth: Measured depth; Track 3- Sw_New: Water saturation from the new method, Sw_Core, Water saturation measured from cores (black dots); Track 4- BVW_New: Bulk volume of water from the new method, BVW_Lam: Bulk volume of water from laminated shale model (traditional method); Track 5- BVW_N_D: Bulk volume of water based on the application of the new method on dispersed shale zones only, BVW_L_D_T: Bulk volume of water based on an erroneous application of laminated shale model on dispersed shale layers only (traditional method); Track 6- Phi_T: Total porosity, Phi_E: Effective porosity, Phi_Core: Porosity estimated from core (light green dots); Track 7- Aniso_ratio: Anisotropy ratio; Track 8- VLam: Laminated shale volume; Track 9- FLAGS: PURPLE FLAG: Represents zones where the laminated shale model of the new method has been triggered, GREEN FLAG: Represents data points located in Triangle L (See Fig. 14)]. NOTE: The missing data in all tracks, except track 1 (shale volume), may be either due to shale zones or limestone layers.

Fig. 29. Crossplot of BVW_N_D versus BVW_L_D_T for the North Sinai field, Mediterranean Sea, Egypt.

Fig. 30. Crossplot of Sw_New versus Sw_Core for the North Sinai field, Mediterranean Sea, Egypt.

Moreover, there is a big variance in values between two methods where BVW_N_L is greater than BVW_D_L_T throughout the entire reservoir with the maximum difference in values reaches up to 6.36%, the maximum error in water estimation reaches up to 79.40%, and the average difference in values reaches up to 3.75%, the average error in water estimation reaches up to 72.82. Also, water saturation from new method shows a good agreement with the water saturation derived from core analysis (black dots shown on Fig. 31, Track 3). There are a decent consistency and a sensitive responsiveness between the anisotropy ratio and the application of the laminated shale model, triggered according to robust procedures of the new water saturation method (PURPLE FLAG, Fig. 31. Track: 9). So, wherever the application of the laminated shale model for water saturation has been triggered, there is high electrical anisotropy in front of laminated shale zones (Fig. 31. Track: 8) throughout the whole reservoir. Also, a GREEN FLAG (Fig. 31. Track: 9), which represents the plotting points fall in the Triangle L (See Fig. 22), located mainly between depths 2824.480 m and 2839.416 m, these low porosity zones show a lesser volume of water and a kick in the resistivity (See Fig. 2), and high values of the anisotropy ratio yielding a false indication that these zones are good hydrocarbon reservoirs. Despite this misleading conclusion. Again, the application of the new laminated shale method was not activated. On the other hand, the statistical correlation between BVW_N_L and BVW_D_L_T (Fig. 32), shows that the correlation coefficient of the relationship is (R2=0.10) which indicates a positive very weak linear relationship, also a statistical correlation between water saturation values from the new method (Sw_New) and water saturation values from core measurements (Sw_Core) (Fig. 33), shows a strong positive linear relationship with correlation coefficient of the relationship is (R2=0.85).

Fig. 31. Comparison between novel water saturation method and water saturation based on the sole application of dispersed shale model (traditional methods), for the Gulf of Suez Well, Egypt. [The track description from left to right: Track 1- Vsh: Shale volume; Track 2- Depth: Measured Depth; Track 3- Sw_New: Water saturation from the new method, Sw_Core, Water saturation measured from cores (black dots); Track 4- BVW_New: Bulk volume of water from the new method, BVW_Dis: Bulk volume of water from dispersed shale model (traditional method); Track 5- BVW_N_L: Bulk volume of water based on the application of the new method on laminated shale layers only, BVW_D_L_T: Bulk volume of water based on an erroneous application of dispersed shale model on laminated shale layers only (traditional method); Track 6- Phi_T: Total porosity, Phi_E: Effective porosity, Phi_Core: Porosity estimated from core (light green dots); Track 7Aniso_ratio: Anisotropy ratio; Track 8- VLam: Laminated shale volume; Track 9- FLAGS: PURPLE FLAG: Represents zones where laminated shale model for the new method has been triggered, GREEN FLAG: Represents data points located in Triangle L (See Fig. 22)]. NOTE: The missing data in all tracks, except track 1 (shale volume), may be either due to shale zones or limestone layers.

Fig. 32. Crossplot of BVW_N_L versus BVW_D_L_T for the Gulf of Suez Well, Egypt.

Fig. 33. Crossplot of Sw_New versus Sw_Core for the Gulf of Suez Well, Egypt.

4. SUMMARY AND CONCLUSIONS The main objective of this study is to introduce a novel, accurate, and fast method for water saturation in shaly sandstone reservoirs based on a novel volumetric method of shale distribution types. Then, a robust procedures flow for a novel water saturation method has been introduced to select appropriate water saturation model depending on the most predominant shale type. While the dispersed shale model is adequate to estimate water saturation in dispersed shale reservoirs, water saturation for laminated shaly sand reservoir is more complicated. So, the author introduced a new approach and algorithms to determine water saturation in these complex reservoirs. Furthermore, a comparison between the new proposed method and qualitative traditional methods has been carried out, the conclusions obtained from this research can be summarized as follows: 1- The worldwide traditional qualitative methods to determine shale distribution types via different crossplots are suffering from several drawbacks concerning their accuracy, especially when dealing with massive superimpose well logging data and different shale distribution types in the reservoir. Besides; they ignored the gravity of shale contents and the effect of dispersed shale that could shift points from laminated shale region to dispersed shale region. As a result, deceptive interpretations and erroneous conclusions are obtained. Unlike other volumetric approaches or methods which depend on one or two shale distribution types, the new volumetric method of shale distribution types proves itself as an accurate method to determine different shale habitats voulmes, as it takes into account the three shale distribution types case by the aid of ratio analysis technique which added a valuable contribution to complete the picture and reveal a full spectrum of different shale distribution types. So, after checking the results of a new volumetric approach of shale distribution types for accuracy and validation on Thomas Stieber crossplot, the accuracy level of this novel method found to be excellent. 2- A comparative study using statistical correlation and logs display in case of two field applications show that applying solely water saturation shale model over the entire reservoir can leads to drastically erroneous in the values of the bulk volume of water saturation, as in the North Sinai field, erroneous application of laminated shale model on dispersed shale zones, comprise 40% of the reservoir, provides underestimated values of bulk volume of water saturation and error in estimation of water saturation up to 60.99% when compared with the new method, and the relationship between them has a good correlation coefficient of 0.91, while in the Gulf of Suez field, the erroneous application of dispersed shale model on laminated shale zones, comprise 63% of the reservoir, yields underestimated and unrealistic values of the bulk volume of water saturation and error in estimation of water saturation up to 79.40% when compared with the new method, and the relationship between them has a very bad correlation coefficient of 0.10. These discrepancies and a big variance in values of water saturation between the new method and other traditional techniques in this study can lead to inaccurate water saturation values and erroneous hydrocarbon estimation. On the other hand, water saturation values from core analysis were in a good consistency with the new water saturation method in both two field applications, with good correlation coefficients of 0.82 and 0.85 for the North Sinai field and the Gulf of Suez field, respectively. 3 - The new proposed model of water saturation for laminar shale reservoirs shows consistency and sensitive responsiveness with an anisotropy ratio throughout the entire reservoirs in two field applications, where the algorithms of the new procedure flow for water saturation triggered the application of the modified laminated shale model with the corresponding anisotropy laminated shale zones very efficiently. Also, the low porosity zones, located in Triangle L in Thomas Stieber crossplot, in two field applications are composed of calcite as sand and calcite cement as pore filling materials and characterized by a lesser volume of water and a kick in the resistivity and high values of anisotropy ratio. As a result, these zones can give a false indication that they are actually hydrocarbon reservoirs. Even though this deceptive pitfall cannot be resolved even by advanced LSSA interpretation model, the novel method of water saturation is taken care of this pitfall and not activated the application of the new laminated shale method. 4- The limitations of this new method, specifically for the laminated shale model, are limited to that the natural gamma ray logs should be free from bad data and noises that affect the quality of signal and the gamma ray must essentially respond to the real shale volumes, if not the results will be wrong. If so, it is better to use more advanced techniques to evaluate these complex thin beds reservoirs. However, the processing flow of the novel water saturation method can guide the petrophysicists where and when to apply

the LSSA interpretation model more precisely. Furthermore, the new volumetric shale distribution types method can be used to improve NTG (Net to Gross) calculations and adding a significant contribution to enhancing LSSA interpretation model when using accurate estimation of dispersed and laminated shale volumes. 5- The application of this novel water saturation method adds a valuable improvement in the formation evaluation techniques and well logging analysis, since it provides accurate water and hydrocarbon saturations, reserve estimation, reservoir simulation, and better net pay detection.

Acknowledgements The author gratefully thanks three anonymous reviewers for their insightful, stimulating, and constructive editorial and technical comments that greatly improved the manuscript. Finally, I would like also to thank the editors for their helpful suggestions and support during the reviewing process.

Future Work My future work is to perform a comparative study between the novel volumetric shale distribution types method and different Artificial Intelligence techniques (AI) to examine how far the accuracy of this novel method in a big scale.

Nomenclature Term a F GR m n Rw Rt Rsh Rshd Sw q Vsh

ϕt ϕe ϕD ϕ im ϕS ϕ NC ϕ max

Description Tortuosity factor. Formation resistivity factor. Gamma Ray log reading. Cementation exponent. Water saturation exponent. Formation water resistivity. True resistivity of formation. Resistivity of shale. Resistivity of dispersed shale. Water saturation. Sonic response in dispersed shale model. Shale volume. Total porosity.

Unit Unitless Unitless API Unitless Unitless Ω.m. Ω.m. Ω.m. Ω.m. % Unitless % %

Effective porosity.

%

Density porosity.

%

Inter-matrix porosity, sonic porosity.

%

Sonic porosity.

%

Neutron porosity corrected from shale effect.

%

Porosity in clean sand

Fraction

VshL VshS VshD Vshlm Vshlp GRMax GRMin IGR Rcl ρbma ρbsh ρbf ρblog

Laminated shale volume Structural shale volume Dispersed shale volume Volume of laminated clay replaces the sand matrix Volume of laminated clay replaces the pore space Maximum Gamma Ray log reading. Minimum Gamma Ray log reading. Gamma Ray Shale Indicator. Resistivity of pure sandstone. Matrix density. Density value of shale. Fluid density value. Density log reading. Neutron porosity of matrix.

Fraction Fraction Fraction Fraction Fraction API API Fraction Ω.m. GM/cm3 GM/cm3 GM/cm3 GM/cm3 P.U.

Neutron porosity in front of shale zone.

P.U.

∆Tmat ∆Tsh ∆Tf Vsd Rsd Rv Rh Vsh A Rshh Rshv

Sonic travel time of rock matrix. Sonic travel time in front of shale zone. Sonic travel time of fluid. Volume of sand in the formation Resistivity of pure sandstone zone Vertical resistivity Horizontal resistivity Volume of shale. Anisotropy ratio Shale horizontal resistivity Shale vertical resistivity Total porosity

µsec/ft. µsec/ft. µsec/ft. % Ω.m. Ω.m. Ω.m. % Unitless Ω.m. Ω.m. %

ϕ Nmat ϕ Nsh

ϕ tsd ϕ esd ϕ sh

Effective porosity

%

Shale porosity

%

Water saturation of sandstone layer Sw_sd Bulk volume of water BVW Saturation exponent n IntelliLog Nomenclature Formation depth. Fd Surface temperature. ST Bottom hole temperature. BHT

% % Unitless M. C° C°

TD BS MWT NPHI RHOB DT AT90 CALI VshD.,%. VshL.,% VshS.,% Phi_T Phi_E Phi_Core VLam Aniso_ratio Sw_New Sw_Core BVW_N_L BVW_D_L_T BVW_Dis BVW_Lam BVW_N_D BVW_L_D_T

Total depth. Bit size. Mud weight. Neutron porosity. Formation density compensated log. Sonic travel time value. Array Induction 90” Resistivity Caliper log value. Ratio of dispersed shale volume to shale volume. Ratio of laminated shale volume to shale volume. Ratio of dispersed shale volume to shale volume. Total porosity. Effective porosity. Porosity estimated from core. Laminated shale volume Anisotropy ratio Water saturation calculated from the new method. Water saturation estimated from core. Bulk volume of water based on application of the new method on laminated layers only. Bulk volume of water based on an erroneous application of dispersed shale model on laminated layers only (traditional method) Bulk volume of water saturation calculated from the dispersed shale model. Bulk volume of water saturation calculated from the laminated shale model. Bulk volume of water based on application of the new method on dispersed layers only Bulk volume of water based on an erroneous application of laminated shale model on dispersed layers only (traditional method)

M. In. lb/gal P.U. GM/cm3 µsec/ft. Ω.m. In. % % % % % % % Unitless % % % % % % % %

CONFLICT OF INTERESTS The author has not declared any conflict of interests regarding the publication of this paper.

REFERENCES 1- Abdelrahman EM, Kamel MH, Bayoumi MA., 2000. QPEST: a program for computing reservoir parameters in marginal hydrocarbon zones. Application to the Rudeis section, Gulf of Suez Basin, Egypt. Petrophysics (March-April). SPWLA-2000-v41n2a2, vol. 41. No. 2 pp. 148-159. 2- Al-Ruwaili, S.B., Al-Waheed, H.H., 2004. Improved petrophysical methods and techniques for shaly sands evaluation. SPE Annual Technical Conference and Exhibition, 26-29 September, Houston, Texas. SPE-89735-MS. https://doi.org/10.2118/89735-MS 3- Anderson, B., J. Clavaud, R. Nelson, K. U. Guru, and H. Wang, 2005. Field examples of enhanced hydrocarbon estimation in thinly laminated formation with a triaxial array induction tool: A laminated sand-shale analysis with anisotropic shale. Society of Petrophysicists and Well-Log Analysts SPWLA 46th Annual Logging Symposium, 26-29 June, New Orleans, Louisiana. SPWLA-2005-WW 4- Anil Kumar Tyagi, Rupdip Guha, Deepak Voleti, and Kamlesh Saxena., 2009. Challenges In The Reservoir Characterization Of A Laminated Sand Shale Sequence. Presented at 2nd SPWLA-India Symposium, Mumbai, India, November 19-20. SPWLA-2009-S. 5- Archie, G.E., 1942. The electrical resistivity log as an aid in determining some reservoir characteristics. Transactions of AIME 146, SPE-942054-G. pp. 54–62. https://doi.org/10.2118/942054-G 6- Asquith, G. and Gibson, C. 1982. Basic Well Log Analysis and for Geologists. AAPG, Tulsa, Oklahoma. 216. https://doi.org/10.1306/Mth3425 7- Asquith,G.B. and Krygowski, D., 2004. Basic Well Log Analysis, 2nd Edition. AAPG Method in Exploration Series 16. Published by the American Association of Peteroleum Geologists Tulsa,Oklahoma, 244p. 8- Bardon, C., and Pied, B., 1969. Formation water saturation in shaly sands. Society of Professional Well Log Analysts. SPWLA 10th Annual Logging Symposium, 25-28 May, Houston, Texas. SPWLA-1969-Z. Paper Z, pp.19. 9- Bassiouni, Z.A ., M.N. Lau., 1990. Development And Field Applications Of Shaly Sand Petrophysical Models Part III - Field Applications. Society of Petroleum Engineers. SPE-20388-MS. P. 27. 10- Best, D. L., J. S. Gardner, and J. L. Dumanoir 1978. A COMPUTER-PROCESSED WE LLSITE LOG COMPUTATION. SPWLA 19th Annual Logging Symposium. 13-16 June, El Paso. SPWLA-1978-Z. 11- Chanh Cao Minh., Isabel Noemia Joao., Jean-Baptiste Nicolas Clavaud., Padmanabhan Sundararaman., 2007. Formation Evaluation in Thin Sand/Shale Laminations. Society of Petroleum Engineers. SPE Annual Technical Conference and Exhibition, 11-14 November, Anaheim, California, U.S.A. SPE-109848-MS. https://doi.org/10.2118/109848-MS 12- Clavier, C., Coates, G., and Dumanoir, J., 1984. Theoretical and Experimental Bases for the Dual-Water Model for Interpretation of Shaly Sands. SPE J. 24 (2): 153-168. SPE-6859-PA. http://dx.doi.org/10.2118/6859-PA 13- Clavier, C., W.R. Hoyle and D. Meunier., 1971. Quantitative interpretation of TOT logs; part I and 11, Journal of Petroleum Technology, (6): 743-763. Colorado, D.W. Hilichie, Inc. 14- Dan M. Arnold and Harry D. Smith, Jr., 1981. Experimental Determination Of Environmental Corrections For A Dual-Spaced Neutron Porosity Log. Society of Petrophysicists and Well-Log Analysts, SPWLA 22nd Annual Logging Symposium, SPWLA-1981-VV, 23-26 June, Mexico City, Mexico. SPWLA-1981-VV. 15- Dewan JT., 1983. Essentials of modern open-hole log interpretation. PennWell Books, Tulsa. 16- DeWitte, L., 1950. Relations between resistivities and fluid contents of porous rocks, Oil and Gas Journal, August 24, pp. 120-134. 17- Dresser Atlas, 1979. Log Interpretation Charts. Dresser Industries, Inc. Houston, TX. 107 pp. 18- Eshimokhai, S. and Akhirevbulu, O.E., 2012. Reservoir Characterization Using Seismic and Well Logs Data (A Case Study of Niger Delta), Ethiopian Journal of Environmental Studies and Management EJESM, Vol. 5 no.4. 19- Fei Wang, Hui-yuan Bian, Xu-hua Gao and Bao-zhi Pan., 2016. Application of fullbore formation microimager logging in the evaluation of anisotropic resistivity in a thin interbed reservoir. J. Geophys. Eng. 13 (4) 454–461, https://doi.org/10.1088/1742-2132/13/4/454. 20- Fernando Angel., 2017. Using high resolution horizontal resistivity measurements to estimate resistivity anisotropy, and thus indicate the presence of thin beds in hydrocarbon reservoirs. Master's Thesis. Department of Geoscience and Petroleum. Norwegian University of Science and Technology. June. 21- Fertl, W. H., 1975. Shaly-sand analysis in development wells, Soc, Professional Well Log Analysis. SPWLA 16th Annual Logging Symposium, 4-7 June, New Orleans, Louisiana., SPWLA-1975-A. 22- Glover, P., 2014. THE FORMATION DENSITY LOG. In: Petrophysics MSc Course Notes Chapt. 13. Leeds University, Yorkshire, United Kingdom. 23- Golnaz Jozanikohan., 2017. On the development of a non-linear calibration relationship for the purpose of clay content estimation from the natural gamma ray log. International Journal of Geo-Engineering 8(1). November. https://doi.org/10.1186/s40703-017-0058-7. 24- Halliburton., 2001. Basic Petroleum Geology and Log Analysis. 25- Hill, H.J.,Klein,G.E., Shirley, O.J., Thomas, E.C., Waxman,W.H., 1979. Bound water in shaly sands— its relation to q and other formation properties. Log Anal 20(3), 1–17. SPWLA-1979-vXXn3a1 26- Hossin A., 1960. Calcul des Saturations en eau Para la methods du clment argilleux. Bull. AFTP, Calculation of the useful porosity in shaly sandstones. Trans SPWLA 11: pp. 97–115. 27- Ipek, I., 2002. Log- derived cation exchange capacity of shaly sands: application to hydrocarbon detection and drilling optimization. PhD thesis, Louisiana State University. 28- John Quirein, Burkay Donderici, David Torres, Eric Murphy, and Jim Witkowsky., 2012. Evaluation of General Resistivity Density-Based Saturation in Thin, Laminated Sand-Shale Sequences. AAPG Search and Discovery Article #90155©2012 AAPG International Conference & Exhibition, Singapore, 16-19 September 2012

29- John W. Neasham, 1977. The Morphology Of Dispersed Clay In Sandstone Reservoirs And Its Effect On Sandstone Shaliness, Pore Space And Fluid Flow Properties: SPE Annual Fall Technical Conference and Exhibition, 9-12 October, Denver, Colorado. https://doi.org/10.2118/6858-MS 30- Juhasz, I. 1979. The Central Role of Qv and Formation Water Salinity in the Evaluation of Shaly Formations. SPWLA 20th Annual Logging Symposium, 3-6 June, Tulsa, Oklahoma. SPWLA-1979-AA. 31- Juhasz, I. 1981. Normalised Qv - The Key To Shaly Sand Evaluation Using The Waxman-Smits Equation In The Absence Of Core Data. SPWLA 22nd Annual Logging Symposium, 23-26 June, Mexico City, Mexico. SPWLA-1981-Z. 32- Klein, J.D., 1996. Saturation Effects on Electrical Anisotropy. The Log Analyst, 37, No. 1, Jan.-Feb., 47-49. SPWLA-1996-v37n1a4. 33- Klein, J. D., P. R. Martin, and D. F. Allen., 1997. The petrophysics of electrically anisotropic reservoirs: The Log Analyst, May-June. vol 38(3). SPWLA-1997-v38n3a1. 34- Masoumeh Bashiri, Mosayyeb Kamari, and Ghassem Zargar., 2017. An Improvement in Cation Exchange Capacity Estimation and Water Saturation Calculation in Shaly Layers for One of Iranian Oil Fields. Iranian Journal of Oil & Gas Science and Technology, Vol. 6, No. 1, pp. 45-62. https://dx.doi.org/10.22050/ijogst.2017.44376 35- Max Peeters, Antony Holmes., 2014. Review of Existing Shaly-Sand Models and Introduction of a New Method Based on Dry-Clay Parameters. Society of Petrophysicists and Well-Log Analysts, Petrophysics, VOL. 55, NO. 6; PAGES 543–553. SPWLA-2014-v55n6a1 36- Mezzatesta, A.G.,Méndez, F.E.,Rodríguez, E.F., and Georgi, D., 2006. A Novel Approach to Numerical Integration of Conventional, Multicomponent Induction, And Magnetic Resonance Data In Shaly-Sand And Carbonate Systems. Society of Petrophysicists and Well-Log Analysts SPWLA 47th Annual Logging Symposium, 4-7 June, Veracruz, Mexico. SPWLA-2006-GGG 37- Mollison, R.A., O.N. Fannini, B.F. Kriegshauser, L. Yu, G. Ugueto, and J. van Popta., 2001. Impact of multicomponent induction technology on a deepwater turbidite sand hydrocarbon saturation evaluation. Society of Petrophysicists and Well-Log Analysts. SPWLA 42nd Annual Logging Symposium, 17- 20 June, Houston, Texas. SPWLA-2001-T. 38- Ofwona, C., 2010. Introduction to Geophysical Well Logging and Flow Testing, Exploration for Geothermal Resources Presented at Short Course V on Exploration for Geothermal Resources, organized by UNU-GTP, GDC and KenGen, at Lake Bogoria and Lake Naivasha, Kenya, Oct. 29 – Nov. 19, 2010. 39- Ollivier Faivre, Tom Barber, Laurent Jammes, and Dat Vuhoang., 2002. USING ARRAY INDUCTION AND ARRAY LATEROLOG DATA TO CHARACTERIZE RESISTIVITY ANISOTROPY IN VERTICAL WELLS. Society of Petrophysicists and Well-Log Analysts SPWLA 43rd Annual Logging Symposium, 2-5 June, Oiso, Japan. SPWLA-2002-M 40- Passey, Q., Dahlberg, K., Sullivan, K., Yin, H., Xiao, Y., Guzman-Garcia, A., Brackett, R., 2004. A systematic approach to evaluate hydrocarbons in thinly bedded reservoirs. Society of Petrophysicists and Well-Log Analysts SPWLA 45th Annual Logging Symposium, 6-9 June, Noordwijk, Netherlands. SPWLA-2004-NNN. 41- Paul, W.J., 2012. Petrophysics, Dept. of Geology and Petroleum Geology, University of Aberdeen. 42- Poupon, A., Gaymard, R., 1970. The evaluation of clay content from logs. Society of Petrophysicists and Well-Log Analysts. SPWLA 11th Annual Logging Symposium, 3-6 May, Los Angeles, California. SPWLA-1970-G. 43- Poupon, A., Leveaux, J., 1971. Evaluation of water saturation in shaly formation. Proceeding of SPWLA 12th Annual Logging Symposium. SPWLA-1971-vXIIn4a1. 44- Poupon, A., Loy, M. E., and Tixier, M. P., 1954. A Contribution to Electrical Log Interpretation in Shaly Sands. Journal of Petroleum Technology 6 (6), pp. 27-34. SPE311-G. https://doi.org/10.2118/311-G 45- Poupon, A., Strecker, L., and Gartner, L., 1967. Introduction To A Review Of Log Interpretation Methods Used In The Niger Delta. SPWLA 8th Annual Logging Symposium, 12-14 June, Denver, Colorado. SPWLA-1967-Y. 46- Ransom, R.C., 1977. Methods based on density and neutron well logging responses to distinguish characteristics of shaly sandstone reservoir rocks. Log Anal. 18 (3), 47. SPWLA-1977-vXVIIIn3a5 47- Rezaee, M.R., Lemon, N.M., 1996. Petrophysical evaluation of kaolinite-bearing sandstones: water saturation (Sw), an example of the Tirrawarra sandstone reservoir, copper basin, Australia. Proceeding of SPE Asia Pacific Oil & Gas conference. Paper SPE, 37023. 48- Rick Aldred, W. A. Manan and Edwin Wong., 2017. Improved Interpretation of Laminated Formations in Mature Fields Using Calculated Rh and Rv Logs From Conventional Resistivity Measurements in Multiple Wells. Society of Petrophysicists and Well-Log Analysts, 23rd Formation Evaluation Symposium of Japan, SPWLA-JFES-2017-Y, 11-12 October, Chiba, Japan. SPWLA-JFES-2017-Y 49- Roddy Irwin., 2016. CLASTICS; How to choose the right petrophysical evaluation method using standard logs. London Petrophysical Society: Petrophysics 10, 17th March. 50- Rodolfo Soto Becerra., Duarry Arteaga., Freddy Rodriguez., 2010. The Correct Shale-Volume Characterization Increases Hydrocarbon Reserves: Case Study of Cretaceous Formation, Lake of Maracaibo, Venezuela. Society of Petroleum Engineers. SPE Latin American and Caribbean Petroleum Engineering Conference, 1-3 December, Lima, Peru. SPE-136811-MS. https://doi.org/10.2118/136811-MS 51- Ruhovets, N. Fertl, W. H., 1982. Volumes, Types, and Distribution of Clay Minerals in Reservoir Rocks Based on Well Logs. Society of Petroleum Engineers. SPE Unconventional Gas Recovery Symposium, 16-18 May, Pittsburgh, Pennsylvania, pp. 64-79. SPE-10796-MS. https://doi.org/10.2118/10796-MS 52- Schlumberger., 1972. Log Interpretation; Volume 1-Principles, Chapter 16. New York, Texas: Schlumberger. 53- Schlumberger Limited., 1974. Log Interpretation: Applications 2. 54- Shedid A. Shedid, Mohamed A. Saad, 2017. Comparison and sensitivity analysis of water saturation models in shaly sandstone reservoirs using well logging data. Journal of Petroleum Science and Engineering Volume 156, July 2017, pp 536-545. https://doi.org/10.1016/j.petrol.2017.06.005 55- Shedid A. Shedid, Mohamed A. Saad, 2017. Analysis and field applications of water saturation models in shaly reservoirs. Journal of Petroleum and Gas Engineering, December. Vol. 8(10), pp. 111-122. https://doi.org/10.5897/JPGE2017.0270 56- Simandoux, P., 1963. Mesuresd ielectriques en milieu poreux, application a mesure des saturations en eau, Etude du Comportment des massifs Argileux. Supplementary Issue, Revue de I’Institut Francais du Petrol. 57- S. S. Prasad, C. S. Sajith, and S. S. Bakshi., 2006. Evaluation of Low Resistivity Laminated Shaly Sand Reservoirs. 6th International Conference & Exposition on Petroleum Geophysics “Kolkata 2006”. 58- Steiber RG., 1973. Optimization of shale volumes in open hole logs. Jour Pet Tech 31: pp. 147–162. 59- Tabanou, J. R., Cheung, P., Liu, C. B., Hansen, S., Lavigne, J., Pickens, T., Borbas, T., Wendt, B., 2002. Thinly laminated reservoir evaluation in oil-base mud: High resolution versus bulk anisotropy measurement-a comprehensive evaluation. Society of Petrophysicists and Well-Log Analysts SPWLA 43rd Annual Logging Symposium, 2-5 June, Oiso, Japan. SPWLA-2002-P 60- Thomas, E.C., and Haley, R.A., 1977. Log Derived Shale Distribution In Sandstone And Its Effects Upon Porosity, Water Saturation And Permeability. Canadian Well Logging Society 6th Annual Formation Evaluation Symposium, Calgary. 61- Thomas, E. C., and S. J. Stieber., 1975. The distribution of shale in sandstones and its effect upon porosity: Society of Petrophysicists and Well-Log Analysts. SPWLA 16th Annual Logging Symposium, 4-7 June, New Orleans, Louisiana. SPWLA-1975-T. 62- Toby Darling., 2005. Well Logging and Formation Evaluation. El-Sevier Inc. Copyright, pp67. 63- Waxman, M.H. and Smits, L.J.M., 1968. Electrical Conductivities in Oil-Bearing Shaly Sands. SPE J. 8 (2): pp. 107-122. SPE-1863-A. http://dx.doi.org/10.2118/1863-A 64- Willis, J. J., D. S. McIntosh, Jr., J. W. Zwennes, and G. J. Ferguson, 2017. Quicklook technique for quantifying shale distribution types using total porosity versus shale volume crossplots. Gulf Coast Association of Geological Societies Transactions, v. 67, p. 539-549. 65- Woodhouse, R., Greet, D. N., Mohundro, C., 1984. Induction log vertical resolution improvement in vertical and deviated wells using a practical deconvolution filter. Society of Petroleum Engineers . Journal of petroleum technology 36 (06), 993–1. SPE-11855-PA. https://doi.org/10.2118/11855-PA. 66- Worthington, P.F., 1985. The evolution of shaly sand concepts in reservoir evaluation. The Log Analyst 26 (1), pp. 23–40. SPWLA-1985-vXXVIn1a2. 67- Yin, Y. and Kurniawan, B., 2008. Resistivity Anisotropy Models and Multi-Component Induction Measurements: Impact on Sw and Uncertainty of PPV Estimation.Paper LLLL presented at the SPWLA Annual Symposium. 68- Zakir Hossain, Paola Vera de Newton., 2014. Vertical and horizontal resistivity analysis by an electrical anisotropy template. Society of Exploration Geophysicists International Exposition and Annual Meeting. Denver, Colorado, 26-31 October, USA. pp. 2439–2443. https://doi.org/10.1190/segam2014-1259.1

"A NOVEL METHOD TO IMPROVE WATER SATURATION IN SHALY SAND RESERVOIRS USING WIRELINE LOGS " Ayman Shebl El-Sayed Ain Shams University, Cairo, Egypt.

Highlights •

Water saturation is a vital parameter in hydrocarbon saturation and reserve estimation. Water saturation in shaly sandstone formations is a complex and difficult task due to the complexity, heterogeneity nature of shale, and shale distributions in the reservoir. So, several models have been proposed to determine water saturation in shaly sandstone formations which may lead to either overestimation or underestimation of water saturation and hence inaccurate hydrocarbon estimation.

•

Using solely qualitative techniques to interpret and detect different shale habitats could be deceptive and give discrepancy results even between different crossplots techniques.

•

Unlike other methods which depend on qualitative techniques to determine shale distribution types and accordingly an appropriate water saturation model has been selected to apply, this method uses a robust procedure to determine shale distribution types based on the novel volumetric method, which gives the accurate shale distribution types, hence more accurate and reliable estimation of water saturation than other methods or approaches.

•

A comparative study using statistical correlation and logs display in case of two field applications show that applying solely water saturation shale model over the entire reservoir can lead to drastically erroneous and even unrealistic results in water saturation calculations.

•

A new proposed model for water saturation in laminated shaly sand reservoirs gives satisfactory results, and is taken care of one of the pitfalls of LSSA interpretation model. Despite the limitations of this model, it adds a valuable contribution to LSSA interpretation model, and guides the petrophysicists where and when to use LSSA interpretation model more accurately.

•

The application of the attained results of this robust method can lead to accurate water saturation, reserve estimation, improve Net to Gross calculations, and better net pay detection, and hence a valuable improvement in the formation evaluation techniques and well logging analyses.