Consistent geological-simulation modeling in carbonate reservoirs, a case study from the Khuff Formation, Persian Gulf

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Consistent Geological-Simulation Modeling in Carbonate Reservoirs, a case study from the Khuff Formation, Persian Gulf Ashkan Asadi-Eskandar, Hossein RahimpourBonab, Shahab Hejri, Khalil Afsari, Alireza Mardani

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S0920-4105(13)00206-4 http://dx.doi.org/10.1016/j.petrol.2013.07.010 PETROL2478

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Journal of Petroleum Science and Engineering

Received date: 27 June 2012 Accepted date: 26 July 2013 Cite this article as: Ashkan Asadi-Eskandar, Hossein Rahimpour-Bonab, Shahab Hejri, Khalil Afsari, Alireza Mardani, Consistent GeologicalSimulation Modeling in Carbonate Reservoirs, a case study from the Khuff Formation, Persian Gulf, Journal of Petroleum Science and Engineering, http://dx.doi. org/10.1016/j.petrol.2013.07.010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. 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.

Consistent Geological-Simulation Modeling in Carbonate Reservoirs, a case study from the Khuff Formation, Persian Gulf Ashkan Asadi-Eskandar1& 2*, Hossein Rahimpour-Bonab1, Shahab Hejri3, Khalil Afsari2 and Alireza Mardani1 1- University College of Geology- University of Tehran 2- National Iranian Oil Company (NIOC) 3- Kish Petroleum Engineering Company (KPE)

Abstract The Khuff Formation constitutes reservoir body in many gas producer fields of the Persian Gulf and Arabian plate. This carbonate reservoir represents a complex character which strongly affects reservoir modeling and prediction of its reservoir performance. This paper examines construction of a reservoir model for this formation by the use of an integrated approach and shows how geological and simulation grids can perform consistently. This approach shows that in case of proper data integration, loss of value in z-dimension after grid scale-up would be ignorable and will not affect actual reservoir performance. The presented approach uses sequence stratigraphic framework (SSF) as the basis of reservoir zonation and permeability prediction. This is resulted to consistent poro/perm models that help accurate prediction of reservoir performance in simulation model. SSF also helped propagation of reservoir bodies in geological model. A seismic derived effective porosity (SPHIE) cube is used in conjunction with core and log data to distribute porosity. Hydraulic flow units (HFUs) which are assessed by the use of core and log data are used as the basis of grid scale-up. Our findings showed that if data integration is properly done, strong correlation of HFUs and SSF will be obtained which results to consistent geological and simulation models. Permeability should be populated into the 3D grid by the use of functions derived from SSF zonation and water saturation modeling should be upon capillary pressure curves assigned to each reservoir rock type (RRT) so that the final geological model and coarse simulation grid would be consistent. The presented approach in this study explains how various visions and different scale data could be properly used in a reservoir model. It also provides ideas about ideal consistent reservoir modeling for the Khuff Formation and similar heterogeneous carbonate reservoirs.

Keywords: Khuff, Static Model, Gridding, Flow unit, Persian Gulf. * Corresponding author email address:[email protected]; [email protected] Address: University College of Geology, University of Tehran, Tehran, Iran Tel: +989127925420

1- Introduction Reservoir modeling is a cyclic procedure which should not end up until proper data integration (Van de Graaff and Ealey, 1988; Henriquez et al., 1990; Mattax and Dalton, 1990; Webber and Van Geun, 1990; Sibley et al., 1997; Valle et al., 1997; Ainsworth and Sankosik, 1998; Dubrule, 1998; Akatsuka, 2000; Marion et al., 2000; Labourdette et al., 2008; Soleimani et al., 2008). Various data are used for construction of a reservoir model each of them having particular scale and resolution hence finding a way about how different data should be incorporated into a single model and how various levels of data resolution should be considered, has always been an important challenge for geo-modelers. The way a geological model is built and scaled-up highly affects the ultimate performance of simulation grid particularly by increasing the amount of heterogeneity in the reservoir. Main reason for mismatch between geological grid and simulation model is that geological models are often built in very fine scales which usually tend to keep every detail of the reservoir. Such models maintain variations in depositional facies and petrophysical properties (e.g., Lucia and Fogg, 1990; Walker, 1990; Lucia and Ruppel, 1996; Ratchkovski et al., 1999) in contrast, simulation grids which are always coarse grids (and large scaled) mainly represent the flow behavior of the reservoir that is characterized by flow units (e.g., Ebanks et al., 1984; Ti et al., 1995; Porras and Campos, 2001; Guo et al., 2005). Accordingly in coarse grids, the entire property of 3D grid would be different than expected values by the geologists. This study examines different visions on construction of reservoir models and presents a particular integrated approach for construction of geological model in carbonate reservoirs. The study has been conducted on the Khuff Formation which is characterized by having very fine scale heterogeneity and tries to find a way toward practical data integration. Proper scale of geological gridding is also examined in this study considering the ultimate reservoir behavior and fluid performance of the studied field. To investigate this, the geo-cellular model is built using a sequence-derived reservoir zonation (SSF zonation). Geological details are kept in the original grid so that the amount of lost data after grid scale-up would be clear. Hydraulic flow units (HFUs) are assessed by the use of core and log data which are compared to SSF based zonation. The purpose of this paper is to compare different visions on modeling and tries to find how the ultimate result could be satisfying. 2- Methodology This study is carried out by the use of a multidisciplinary approach for construction of a 3D geological grid with a wide range of input data from seismic to production analysis. A sequence stratigraphic analysis is carried out as the main geological approach considering the fact that depositional facies are only appropriately propagated into a 3D grid when they are interpreted in a proper chrono-startigraphic framework. To do this, a set of core and log data was used from the studied carbonate reservoir. This data includes five set of full cores containing the reservoir succession. The non-cored wells are correlated with cored intervals in terms of sequence stratigraphy and facies characteristics. Pre-interpreted seismic horizons and geological well markers are used for construction of structural model. Formation tops are quality controlled and depth adjusted by the use of well logs. A seismic derived effective porosity (SPHIE) cube is used as a trend for porosity propagation. This cube is calibrated with the well logs and core data (porosity measurements) as well. For this purpose single attribute (linear regression), multi attribute (multi-step regression) and neural network (Probabilistic Neural Networks-PNN) methods are analyzed and finally the method with the lowest validation error and highest correlation to well data is selected to build the porosity cube. Effective porosity (PHIE) is used as the target log for prediction

according to available PHIE in wells. The SPHIE cube is the final output of porosity prediction using seismic attributes. For single attribute analysis some different attribute extracted and correlation coefficient of each attribute with PHIE (target log) calculated. To improve the predictive power, several groups of seismic attributes are used simultaneously. In multi attribute method, complementary features from different attributes are combined to discriminate subtle features on the target logs, which none of the individuals could predict by themselves. Multi attribute method showed the best result and was selected for SPHIE calculation in this study. Permeability prediction and modeling is done upon particular functions derived from the SSF zonation. Hydraulic Flow units (HFUs) are obtained by the integration of conventional core analysis (CCAL) and log data. The HFUs are correlated with the sequence stratigraphic framework (SSF) of the field and used as the base of geological grid up-scaling. Water saturation modeling is done by the use of capillary pressure curves assigned to each reservoir rock type (RRT) and the height above free water level concept. The performance of dynamic model is examined by the use of new drilled (blind wells) that was not involved while construction of static model. 3- Geological setting and Study area The studied field is a carbonate reservoir located in the central part of the Persian Gulf. The Persian Gulf and its adjacent area is globally renowned as hosting world’s largest gas bearing fields, almost all of them producing from Permian-Triassic Khuff succession. This Formation (equivalent to Kangan and Dalan Formations in Iranian nomenclature) consists of carbonate evaporatic intervals reaching to more than 600m thickness in Iranian and Arabian offshore, Persian Gulf area (Alsharhan, 2006). In Iran, Kangan and Dalan Formations aged early Triassic and upper Permian respectively constitute some very large gas bearing reservoirs including South Pars, North Pars, Golshan and Ferdowsi in the offshore and Aghar, Kangan, Nar, Dalan, Bandubast, Asaluyeh, Shanul and Varavi in onshore coastal Fars. These hydrocarbon bearing sequences in both outcrop and subsurface are less- documented in Iran than their equivalents in Abu Dhabi, Oman and Saudi Arabia (e.g., Alsharhan, 1993; Alsharhan and Nairn, 1997; Fontana et al., 2010; Koehrer et al., 2011; Angiolini et al., 2003) however, in recent years the number of publications about the latter formations has been increased because of their economic importance (e.g., Aali et al., 2006; Insalaco et al., 2006; Rahimpour-Bonab et al., 2009; Frebourg et al., 2010). Kangan and Dalan Formations are divided into five individual reservoir units including K1 to K5 in Iran as well as Arabian plate. K1 and K2 units coincide with the Triassic Kangan Formation. Dalan is divided into the upper and lower carbonate members separated by median anhydrite regionally called Nar (Szabo and kheradpir, 1978; Konyuhov and Maleki, 2006; Ghazban, 2007). Upper Dalan coincides with K3 and K4 reservoir units while lower Dalan is known as K5 in Arabian nomenclature. Having centimeter-scale heterogeneity is the most important characteristic of the Khuff Formation which has resulted to the reservoir compartmentalization in the studied field as well as Arabian equivalents (Insalaco et al., 2006; Rahmipour-bonab, 2007). Anhydrite layers which are often observed as thin beds (less than 2m) are supposed to be the source of the mentioned compartmentalization in the Khuff Formation (Insalaco et al., 2006). These anhydrite beds are usually intercalated with dolomitized mudstone filled with nodular and patchy anhydrite indicating the effect of sea level in the creation of the centimeter-scale heterogeneity. The global relationship of the porosity-permeability is originated from a syn-depositional or early diagenesis in the studied reservoir. Figure 1 illustrates the stratigraphical column of the studied formation.

4- Discussion and Results 4-1 Sequence Stratigraphy Framework & Depositional Facies The basic geological approach in this study is mainly based on the sequence stratigraphy framework of the reservoir; moreover the reservoir characteristic of each depositional facies is examined in detail. The Khuff reservoir is very well introduced in terms of depositional setting and reservoir characteristics in the literature. It is renowned as a heterogeneous reservoir including a centimeter-scale of heterogeneity (Insalaco et al., 2006; Rahimpour-bonab, 2007; Rahimpour-bonab et al., 2009). The formation is supposed to be deposited in a homoclinal carbonate ramp during Permo-Triassic time while the paleo-climate has been dry and warm (Konert et al., 2001; Ziegler, 2001; Alsharhan, 2006; Konyuhov and Malek, 2006). This is recognized by the volume of dolomitized lime and accompanying anhydrite which has intensively affected the characteristics of the Khuff reservoir. Findings of this study indicate existence of 15 main core-facies in the Khuff Formation (Table 1). They are deposited in various part of a carbonate ramp including hyper-saline sabkha to proximal open marine (Figure 2). These facies represent a wide range of porosity and permeability which has been illustrated in the Figure 3. The basis of the sequence stratigraphic interpretation in this study is adopted from Alsharhan (2006) which consider Khuff Formation as a second order transgressive-regressive regional sequence divided into five 3rd order cycles. These cycles encompasses K1 to K5 units in Arabian plate however in the studied area only four of the mentioned depositional cycles are producer zones of the field. In the studied reservoir, the sequences are interpreted using core and log data. In this regard, each sequence is subdivided into lowstand system tract (LST), transgressive system tract (TST) and highstand system tract (HST). The LST deposits and/or very early transgressional system tract (E-Early TST) are formed at the beginning of sea transgression in the studied basin. They are characterized by intercalations of laminated mud and anhydrite, sometimes in chicken-wire form. These deposits are not found in all studied sequences but are mainly observed in K1, K2 and K3 cycles. The LST deposits comprise the tightest successions in the studied formation however in the case of K2 cycle in which the sea was in a higher level, some grainy deposits are observed within this system tract. These deposits show very good reservoir quality resulted from leaching and dolomitization. TST represents the main phase of sea transgression in the studied basin at the time when Khuff formation was deposited and hence marks the highest level of water in the studied basin. TST deposits are usually characterized by massive distribution of ooidal-bioclastic shoals terminating to bioturbated wackestone and mudstone facies of the mid ramp or open marine. In the studied reservoir, TST is distinguished from LST and HST deposits by massive gas bearing limestone deposits with very good porosity and permeability. Maximum flooding surface (MFS) which marks the highest level of water in each sequence is mainly identified by large bioclastic seaward shoal deposits and hence an increasing trend in effective porosity (PHIE) is observed in TST particularly in K4 and K2 cycles. HST marks the end of transgression in the studied basin and hence those cycles ending to the next regression stage are characterized by it. These deposits are identified by limy to limydolomite beds at the base and dolomite to dolomitic-lime at the top. This system tract is characterized by shallowing upward depositional cycles comprising from ooilithic-bioclastic shoal deposits at the base which are capped by shallow intertidal to sabkha sediments. A general decreasing trend was observed in PHIE values in HST deposits. This is because of leaching and development of vuggy porosity at the base of HST followed by syn-depositional anhydrite pore-filling toward the sequence boundaries. It should be mentioned that the diagenetic overprint in the Khuff formation is a kind of syn-depositional to very shallow event which is represented by early reflux dolomitization accompanying with anhydrite in variety of

forms. The latter is also recognized by non-dolomitized limestones at the TST cycles indicating strong influence of sea level on the creation and development of dolomitized successions. Dolomitization process is strongly under control of sea level change in the Khuff Formation and hence even fine scale drop in sea level is believed to have strong effect on the reservoir quality of the studied reservoir. The reservoir zonation in this study is done based on the SSF. In this way, after classification of each sequence into LST, TST and HST, the early and late stages of each system tract has been identified by the use of core and log data. In particular calycles which depositional packages were not easily recognizable and or in the absence of core data (e.g. core gap) classification is done based on petrophysical logs. The ultimate result of detailed SSF classification conducted us to identification of 21 reservoir zones in the studied formation which has been illustrated in the Figure 4. K1 is classified and divided into 3 depositional units (K1-1, K1-2 & K1-3) which are actually LST, TST and HST respectively. K2 is divided into K2-1, K2-2 and K2-3 (similar to K1). K3 is classified into K3-1 to K3-6. In this unit each system tract has been sub-divided into early and late stage (e.g. early LST, late LST, early TST, late TST, early HST & late HST). K4 is divided into 7 depositional units or reservoir zones similar to K3 including K4-1, K4-2, K4-2a, K4-3, K4-4, K4-4a and K4-5 in which K4-2a and K4-4a are petrophysical subunits representing very thin depositional packages not easily recognizable on the core and thin section slides. Upper Kangan (UK) is the top of the studied reservoir which is not considered as a reservoir zones and hence not included in the reservoir zonation of the field. This unit is characterized by the use of log data due to lack of core material. 4-2 Hydraulic Flow Units A hydraulic unit is defined as a volume of the total reservoir rock within which geological and petrophysical properties that affect fluid flow are internally consistent and predictably different from properties of other rock volume (Porras et al., 1999). The hydraulic flow units in this study are investigated on the base of RQI-FZI and Lorenz Plot concepts illustrated and explained by several authors (e.g., Ebanks et al., 1984; Amaefule et al., 1993; Abbaszadeh et al., 1996; Gunter et al., 1997; Rincones et al., 2000). It has long been established that on a log-log plot of RQI versus normalized porosity, samples lie on a line with unique slope will demonstrate a particular reservoir character. Lorenz plot is the plot of cumulative values of K*H versus Phi*H in which any change in the slope of the resulted line will be interpreted as a change in hydraulic flow unit along the well profile (Amaefule et al., 1993; Gunter et al., 1997). This is actually a way of finding how reservoir dynamically behaves by plotting flow capacity versus storage capacity. The result of such a plot is illustrated in Figure 5, twenty six critical breaks are observed in the Khuff reservoir indicating 26 flow units (flow zones). These flow units were investigated in an independent way from geological zonation but a very good correlation was observed between sequence stratigraphic-derived zones (SSF) and (HFUs). The boundary of the main flow units coincide with the depositional packages determined through sequence stratigraphic study. A comparison of the ultimate flow units and sequence stratigraphic framework (SSF) of the field is presented in Figure 6. It should be mentioned that the construction of static model is done based on SSF zonation but the ultimate dynamic model is built as per the flow units. Figure 7 illustrates well to well correlation of the Flow units. The reason that simulation model is built based on flow units (HFUs) is that some hydraulic flow units were not recognizable on the SSF classification (Figure 6). These zones which are important in terms of production constitute thin layers on cores that are not easily recognizable. Moreover, simulation model performs based on dynamic behavior of the reservoir and hence the geological grid is scaled up within HFUs. 4-3 Integrated Reservoir Modeling of the Khuff Formation

4-3-1 Seismic Porosity (SPHIE) Calibration To incorporate seismic porosity into the 3D grid, a depth converted seismic derived effective porosity (SPHIE) cube was re-sampled into the structural model. The geo-cellular model was built in a conformable layer format using interpreted seismic derived horizons and geological markers. The structural grid was constructed in 500*500 dimensions (X and Y) and variable Z layers which depend on the quality of each reservoir zone. No fault was incorporated into the geological grid as there are not any major faults in the studied reservoir. The porosity modeling in this study is based on a set of well log data calibrated with core porosity and also a seismic derived porosity (SPHIE) cube. The SPHIE cube was originated from an inverted seismic cube in time domain which was quality controlled by data acquired at well locations. The standard procedure for integration of SPHIE into a 3D grid is that by calculating the relation between seismic response and well data, the porosity will be estimated between well spaces (Doyen, 1998; Raghavan et al., 2001). The observations in this study indicated a correlation coefficient of approximately 0.5 between log porosity (PHIE) and SPHIE which is illustrated in Figure 8. In the process of modeling, the SPHIE was investigated in each reservoir unit. Although the global correlation of PHIE and SPHIE is high, the seismic cube (SPHIE) is unable to read the exact amount of log porosity (PHIE). This is due to the fact that the sampling rate of seismic data is approximately 4ms (equivalent to 20-25 m) and accordingly the SPHIE would be an average of PHIE in each zone. As previously mentioned, the Khuff Formation represents a fine scale heterogeneity resulting from high variations in depositional facies and porosity. Naturally, seismic cube (SPHIE) is not able to predict theses fine heterogeneity accurately. A comparison of PHIE and SPHIE is illustrated in Figure 9. This Figure demonstrates that the SPHIE follows the PHIE very well but the amount of porosity is often overestimated or underestimated. To resolve this, a difference map (residual map) was prepared for each reservoir zone representing the amount of mismatch between PHIE and SPHIE. To prepare residual maps, numerical difference between PHIE and SPHIE was calculated at well location (for each reservoir zone), the calculated values was converted to 2D maps in well space locations. Average SPHIE map was then calculated in each reservoir zone and finally the residual maps were added and/or subtracted to the average SPHIE maps in order to calibrate seismic data (SPHIE) with well logs (PHIE). Moreover, correlation coefficient of SPHIE and PHIE was calculated at well locations in each reservoir zone which was then interpolated in well spaces and concerted to 2D correlation coefficient maps (cc-maps). These cc-maps were used as auxiliary trends while porosity modeling to help accurate population of PHIE and prevent any deviation from the original values between wells. After SPHIE was corrected, direction of major and minor anisotropy was determined by preparing variogram maps; the necessary statistical data such as range, nugget and sill was then estimated in each reservoir zone. Finally the PHIE was collocated with SPHIE and propagated using co-kriging method. Kriging is a linear estimation method which calculates the un-known values by the use of variogram and kriging weights (Deutsch, 2002). In this method, any un-known value has a weight which is obtained by its distance to the known value. It also uses variogram to understand the variability of data over a distance (Deutsch, 2002). To find the un-known value a linear function would be solved by the use of Gaussian algorithm, within a particular matrix which its members are weights, known values and the un-known value. In this study the SPHIE was added to the linear functions of kriging as auxiliary data. As the SPHIE has been corrected by the use of residual maps, it helps the accuracy of porosity population. Correlation coefficient maps (cc-map) were prepared for each reservoir zone to compensate the amount of difference between SPHIE and PHIE. These maps conducted the porosity distribution

and reduced the amount of uncertainty in porosity modeling. Figure 10 (A to D) illustrates ccmaps in some zones. 4-3-2 Anisotropy and Data Analysis Geo-statistical analysis on PHIE, indicated that range of porosity varies from 2500m to 8000m in the studied reservoir. A cyclicity effect is observed in some geological units particularly at K4 and K2 depositional sequences indicating disconnected carbonate shoals and inter-lagoon deposits in the studied basin (Figure 11 A&B). These cycles reduce the major range (continuity of the reservoir property). In case of ignoring minor cyclicity effects, the range could increase to 11000m at some reservoir zones. A change in trend of depositional setting was observed on the variogram maps. It is considered that direction of depositional setting was along NE-SW and the lateral variation in carbonate ramp was along NW-SE or East-West direction. The main anisotropy direction is along North-South with a minor direction of East-West (Figure12). The main direction of anisotropy coincides with the main depositional trend showing minor rotation from Permian basin to Triassic (Figure 12). 4-3-3 Porosity, Rock Type and Water Saturation Modeling The Sequential Gaussian Simulation (SGS) method was used to populate porosity in this study. PHIE is used as the main input data and away from borehole would be simulated by it, SPHIE is used as trend (as per co-kriging method). Cc-maps would compensate and correct any difference between the PHIE and SPHIE in non-drilled locations. In this method, PHIE will be converted to normalized PHIE by the use of normal score transformation. Variogram driven information such as range provides variability of data over distance (Deutsch, 2002). In this Gaussian method various realizations are generated from a constant input. Each realization provides different but equal-probable result. The realizations are defined by a particular semirandom path given by the start point in the matrix (Deutsch, 2002). To reduce the uncertainty of petrophysical modeling, only 10 realizations was allowed in this study, different realizations provided similar results however, which is because of trends maps and cc-maps used in the porosity modeling process. Figure 13 to Figure 16 illustrate maps of the populated porosity in the main reservoir zones (K1 to K4). A main concern in SGS modeling is that extreme low values (flow barriers) and extreme high values (fluid conduits) are disconnected (Deutsch, 2002). This is prohibited and resolved by the use of in this study which provides laterally continues porosity values. Water saturation has been modeled using capillary pressure curves assigned to each reservoir rock type (RRT) in this study, saturation was then calculated in every cell of the 3D grid by the use of height above free water level concept. RRTs are classified on the basis of RQI-FZI concept. To do this, the calculated DRTs (Figure 17) were merged together as per geological knowledge of the studied reservoir and investigation of capillary pressure curves. Table 2 shows RRT classification in the studied formation. As shown in Table 2, each RRT has a particular range of porosity and permeability and hence the RRT modeling in the 3D grid is done deterministically instead of stochastic population. In this way, RRTs are directly calculated from modeled PHIE and modeled permeability by the use of poro-perm range in each RRT. Figure 18 illustrates examples of the modeled RRT in main reservoir zones (K1 to K4). Range of porosity and permeability in each RRT is presented in Table 2. This procedure helped the accuracy of petrophysical model. In addition the RRT model is fully consistent with porosity and permeability models.

Modeling of water saturation was done in a deterministic way as well. Each RRT represented particular relation of water saturation and height above free water level curve (Figure 19) which conducted the water saturation modeling in the studied reservoir. Water saturation is calculated in each cell of the 3D grid by knowing the unique RRT of the cell, amount of saturation water is obtained from the elevation of the cell above free water level. Figure 20 illustrates the water saturation model in the studied field. As shown the saturation model is completely compatible with the RRT model. 4-3-4 Permeability Prediction and Modeling Artificial methods, rock types and porosity-permeability transformation are common methods of permeability prediction found in the litterateur (Wendt et al., 1986; Pittman, 1992; Kerans, 1994; Lucia, 1995). It has long been recognized that excellent permeability-porosity relationships would be obtained once the conventional core data are grouped according to their rock types (Guo et al., 2005). In the same way, rock type are used as a powerful tool for permeability prediction (Pittman, 1992; Amaefule et al., 1993; Gunter at al., 1997; Rincones et al., 2000; Porras et al., 2001; Soto et al., 2001; Babadagli and Al-Salmi, 2002). Artificial methods such as neural work (or fuzzy-logic) use log data in order to predict permeability in non-cored wells which is very common in reservoir characterization studies (Katz and Thompson, 1986; Jonhson, 1994; Mohaghegh et al., 1995; Ahrimankosh et al., 2011). Transformation method is rarely used in carbonates because of complexity of the reservoirs (Deutsch, 2002). In this study, unique porosity-permeability relationship was obtained in each depositional package (sequences & system tracts) which made the permeability prediction possible by the use of transformation method. The correlation coefficient between porosity and permeability in sequence based zones (SSF) is more than 0.5 (varying from 0.5 to 0.7), which provided unique poro-perm equations in each reservoir zone. Considering the fact that global correlation coefficient of the porosity-permeability is approximately 0.1 in the studied Formation, the achieved correlation is considered perfect for such a heterogeneous reservoir. The global relationship of poro-perm and examples of the sequence-derived permeability functions are illustrated in Figure 21 and Figure 22, respectively. Accordingly, the permeability was assigned to porosity in the procedure of permeability modeling which has resulted to a geological orientated permeability model. Moreover, the uncertainty of stochastical rock type population is eliminated in this way. The amount of ultimate correlation coefficient between porositypermeability after building block wells (well log up-scaling) reached to 0.8 in some cases. One main disadvantage of using regression for permeability modeling is that the low and high permeability values are smoothed (Deutsch, 2002), to avoid this, the regression equations obtained in geological zonation are not directly used for permeability prediction, Instead permeability modeling was performed by the integration of regression method (porositypermeability functions) combined with collocated co-kriging approach. Highlighting points of the mentioned method for 3D permeability distribution is that it avoids pixel type permeability modeling. Moreover, the ultimate correlation coefficient of collocated co-kriging reached to more than 0.8 in some cases (varying from 0.5 to 0.85). Figure 23 (A to D) illustrates 3D populated permeability in the main reservoir zones. 4-4 Log and Grid Scale-up 4-4-1 Well log scale-up The accuracy of block wells completely depends on the number of layers in each zone and hence finding the proper layering is the most important issue while building of a 3D grid. In this study the number of layers is calculated based on a histogram presenting the minimum, maximum and average thickness of each reservoir zone. The layering was conducted by the

minimum possible layers gradually shifting to maximum, in each stage the histogram of upscaled logs was compared with original well data. The proper layering was chosen by the best fitted histogram. Our finding indicates that the number of layers in non-porous zones could be up to 3m however in producer layers it should be about 0.8m. Figure 24 and Figure 25 (A&B) illustrate well sections and histograms of original and up-scaled logs for porosity and permeability. 4-4-2 Grid Scale-up The proper scale of constructing a reservoir model is the scale at which depositional facies can be properly correlated and petrophysical properties and fluid flow could be accurately modeled (King et al., 2006). High frequency cycles could be used as an applied factor for scaling-up grids. Lucia (2007) suggested that data variance increases significantly within a cycle but only slightly among cycles.In the studied reservoir, hydraulic flow units (HFUs) coincide with SSF based zonation of the field and hence final geological model was scaled-up by the use of HFUs. Some extra HFUs however were detected within the reservoir representing particular flow behavior which was not recognized in geological zonation. These HFUs are comprised from very thin layered dolomitized beds which are part of HST and early TST system tracts. In some cases these flow zones are composed of fracturized rocks which are only characterized by well test data. These zones are common in K1 and K3 reservoir units. 4-5 Simulation Model Dynamic modeling was carried out with the objective of evaluating field performance in the studied reservoir. The full-field dynamic model was constructed from the up-scaled static model. The up-scaled model comprised of 76*73*26 grids (160,892 cells) resulted from 76*73*405 (2,246,940 cells) down-scale model. The dynamic data consisting of capillary-pressure and relative permeability for 12 rock types were loaded in the model and GEM compositional simulator was used as the software package. The single porosity model was used to simulate the field. The fluid model was set for gas condensate reservoir and GEM simulator was selected for this study. Peng-Robinson (1978) EOS and then a single model (EOS – 7 components) was built. The results of tuned EOS were compared with those obtained in the laboratory and showed close agreement. then the fluid component properties was exported at datum temperature of 216 °F for GEM simulator. The dew-point pressure was calculated to be 4,500 psia at 216 °F. After all required data were entered in the model, model initialization was performed. The model was initialized using pressure of 5650 psia set at datum depth of 2,900 mss (9,514.4 ftss). The initial gas-in-place was calculated by gravity-capillary equilibrium. The OGIP was calculated to be 60 TSCF. This is in line with static model. 4-6 Performance of Models, a case of proper Data Integration The constructed fine-scale PHIE model which was in complete agreement with well data, showed an average value of the porosity (similar to the SPHIE) after geological grid was scaledup (Figure 26 and Figure 27). Losing values after grid scaling-up is common in integrated reservoir studies however it usually opens too many discussions between technicians involved in the study. Geologists often argue about destruction of original property. On the other side, reservoir engineers claim the field performance and well history match to establish the accuracy of simulation model.

In this study, the geological grid was built upon a SSF based zonation and was then scaled-up in a compatible HFU-SSF zonation. Lost values in Z-dimension also observed but it is not considered as a failure in reservoir modeling of the studied field. This is justified by well performance in simulation model (Figure 28). One reason behind very well performance of dynamic model in this case is that the overall field performance is controlled by massive gas producers of K4 and K2 units which are laterally conformable and continues. Dynamic model performance is hence originated from accurate distribution of porosity, permeability and water saturation in X and Y direction of the studied reservoir. The centimeter reservoir heterogeneity which was observed in Z-dimension at well locations although important, but it is not controlling the ultimate reservoir behavior. This supports the fact that coarse grids can perform similar to fine grids even if some details are lost in the scale-up stage and that the key for construction of realistic geological/simulation grids is to have an accurate distribution of petrophysical properties by precise data integration and real multi-disciplinary understanding. If a model is built upon the integrated approach presented in this study, loss of value is not a big deal even though the reservoir is strongly heterogeneous. 5- Conclusions The result of a multidisciplinary approach was examined in a heterogonous carbonate formation hosting some of the world’s largest gas reserves in the Persian Gulf and Arabian plate. Construction of the geo-cellular model was done upon particular integrated geological methods by which the original rock property and reservoir heterogeneity was kept but the model is completely compatible and correlatable with hydraulic flow units of the field. This approach uses the sequence stratigraphic framework (SSF) of the studied formation as the basis of reservoir zonation. Depositional packages are determined in an accurate way that made permeability prediction possible by the use of some very powerful porosity-permeability equations. These functions were derived from SSF zonation. An accurate porosity model conducted by well and seismic data provided a realistic geological model. Moreover, the permeability population is compatible with effective porosity (PHIE) distribution as it is derived from porosity- permeability functions. The presented approach in this study showed how a dynamic model can perform properly although many details would be lost in z-dimension after scaling-up geological grid. This indicates that in case of proper data integration constructing a model with geological concepts and scaling-up it within hydraulic flow units (HFUs) will provide realistic reservoir models accepted by all involved disciplines in a field study. Acknowledgments Data and facility used in this study was provided by National Iranian Oil Company and POGC which the author is really thankful. Mr. Asghari is thanked for reviewing the manuscript and helpful suggestions. Head of Geology & R&T department are appreciated for technical advices and permission to publish this paper. University of Tehran is thanked for providing the source materials. Mr. MeysamTavakoli and Mr. Farid are thanked for helpful technical comments while performing this study. Schlumberger and CMG are thanked for providing the commercial software of Petrel and GEM. References Aali, J., Rahimpour-Bonab, H., Kamali, M. R., 2006. Geochemistry and origin of the world's largest gas field from Persian Gulf, Iran. J. Petrol.Sci. Engin. 50 (3), 161-175. Abbaszadeh, M., Fujii, H., Fujimoto, F., 1996. Permeability prediction by hydraulic flow unitstheory and applications. SPE Formation Evaluation. 11 (4), 263-271.

Ainsworth, R., Sankosik, H., 1998. 3-D Reservoir Modeling of the Sirikit West Field, Phitsanulok Basin, Thailand. SPE Conference. SPE 39737. Akatsuka, K., 2000. 3D Geological Modeling of a Carbonate Reservoir, Utilizing Open-Hole Log Response-Porosity & Permeability-Lithofacies Relationship. SPE Conference. SPE 87239. Alsharhan, A. S., 1993. Facies and sedimentary environment of the Permian carbonates (Khuff Formation) in the United Arab Emirates. Sediment. Geol. 84 (1-4), 89-99. Alsharhan, A.S., 2006. Sedimentological character and hydrocarbon parameters of the middle Permian to Early Triassic Khuff Formation, United Arab Emirates. GeoArabia. 11, 121–158. Alsharhan, A.S., Nairn, A.E.M., 1997. Sedimentary Basins and Petroleum Geology of the Middle East. Elsevier, Netherlands. Amaefule, J.O., Altunbay, M., Tiab, D., Kersey, D.G., Keelan, D.K., 1993. Enhanced reservoir description: using core and log data to identify hydraulic (flow) units and predict permeability in uncored intervals/wells. SPE Conference. SPE 26436. Babadaglı, T., Al-Salmi, S., 2002. A Review of Permeability-Prediction Methods for Carbonate Reservoirs Using Well-Log Data. SPE Conference. SPE 87824. Deutsch, C. V., 2002. Geostatistical Reservoir Modeling (Applied Geostatistics Series). Oxford, New York. Doyen, P. M., 1988. Porosity from seismic data: A geostatistical approach. Geophys. 53 (10), 1263-1275. Dubrule, O., 1998. Geostatistics in petroleum geology, AAPG Special Volume. 38. Dubrule, O., 2003. Geostatistics for seismic data integration in earth models. Distinguished Instructor Short Course, Soc. Exploration Geophysicists. Ebanks, W.J., Scheihing, M.H., Atkinson, 1984. Flow Units for Reservoir Characterization. AAPG Bull. 282-289. Fontana, S., Nader, F. H., Morad, S., Ceriani, A., Al-Aasm, I. S., 2010. Diagenesis of the Khuff Formation (Permian–Triassic), northern United Arab Emirates. Arab. J. Geosci. 3 (4), 351-368. Frebourg, G., Davaud, E., Gaillot, J., Virgone, A., Kamali, M., 2010. An Aeolianite in the Upper Dalan Member (Khuff Formation), South Pars Field, Iran. J. Petrol. Geol. 33 (2), 141-153. Ghazban, F, 2007. Petroleum geology of the Persian Gulf. Tehran University press, Tehran. Gunter, G.W., Finneran, J.M., Hartmann, D.J., Miller, J.D., 1997. Early Determination of Reservoir Flow Units Using an Integrated Petrophysical Method. SPE Conference. SPE 38679. Guo, G., Diaz, M., Paz, F., Smalley, J., Waninger, E., 2005. Rock Typing as an Effective Tool for Permeability and Water-Saturation Modeling: A Case Study in a Clastic Reservoir in the Oriente Basin. SPE Conference. SPE 97033.

Henriquez, A., Tyler, K., Hurst, A., 1990. Characterization of fluvial sedimentology for reservoir simulation modeling. SPE Formation Evaluation, 5 (3), 211-216. Insalaco. E., Virgone, A., Coutme, B., Gaillot, J., Kamali, M., Moallemi, A., Lotfpour, M., Monibi, S., 2006. Upper Dalan Member and Kangan Formation between the Zagros Mountains and offshore Fars, Iran: depositional system, biostratigraphy and stratigraphic architecture. GeoArabia 11, 75-176. Katz, AJ., Thompson, A.H., 1986. Quantitative Prediction of Permeability in Porous Rocks. Physical Review. 34 (11), 8179-8181. Kerans, C., 1994. Integrated Characterization of Carbonate Ramp Reservoirs Using Permian San Andres Formation Outcrop Analogs. AAPG Bull. 78 (2), 181. King, M., Burn, K., Wang, P., Muralidharan, V., Alvarado, F., Ma, X., Datta-Gupta, A., 2006. Optimal coarsening of 3D reservoir models for flow simulation. SPE Res. Eval. & Eng, 9 (4), 317-334. Koehrer, B., Aigner, T., Pöppelreiter, M., 2011. Field-scale geometries of Upper Khuff reservoir geobodies in an outcrop analogue (Oman Mountains, Sultanate of Oman). Petrol. Geosci. 17 (1), 3-16. Konert, G., Afif, A.M., AL-Hajari, S.A., Droste, H., 2001. Paleozoic stratigraphy and hydrocarbon habitat of the Arabian Plate. GeoArabia 6 (3), 407–442. Konyuhov, A.I., Maleki, B, 2006. The Persian Gulf Basin: Geological history, sedimentary formations, and petroleum potential. Lithology and Mineral Resources. 41, 344–361. Labourdette, R., Hegre, J., Imbert, P., Insalaco, E., 2008. Reservoir-scale 3D sedimentary modelling: approaches to integrate sedimentology into a reservoir characterization workflow. Geol. Soci. London, Special Publications. 309 (1), 75-85. Lucia, F. J., 1995. Rock-fabric/petrophysical classification of carbonate pore space for reservoir characterization. AAPG Bull.. 79 (9), 1275-1300. Lucia, F., and Fogg, G., 1990. Geological/Stochastic Mapping of Heterogeneity in a Carbonate Reservoir. J. Petrol. Tech. 42 (10), 1298-1303. Lucia, F.J. 1995. Rock fabric/petrophysical classification of carbonate pore space for reservoir characterization. AAPG Bull. 79 (9), 1275-1300. Lucia, F.J., 2007. Carbonate Reservoir Characterization. Springer-Verlag, Berlin. Lucia, F.J., Ruppel, S.C., 1996.Characterization of Diagenetically Altered Reservoirs, South Cowden Grayburg Reservoir, West Texas. SPE Conference. SPE 36650. Marion, D., Insalaco, E., Rowbotham, P., Lamy, P., Michel, B., 2000. Constraining 3D static models to seismic and sedimentological data: a further step towards reduction of uncertainties. SPE Conference. SPE 65132.

Mattax, C., and Dalton, R., 1990. Reservoir Simulation (includes associated papers 21606 and 21620). J. Petrol. Tech. 42 (6), 692-695. Mohaghegh, S., Balan, B., Ameri, S., 1995. State-Of-The-Art in Permeability Determination from Well Log Data: Part 2- Verifiable, Accurate Permeability Predictions, the Touch- Stone of All Models. SPE Conference. SPE 30988. Pittman, E.D., 1992. Relationship of Porosity and Permeability to Various Parametes Derived From Mercury Injection-Capillary Pressure Curves for Sandstone. AAPG Bull. 76 (2), 191-198. Porras, J., and Campos, O., 2001. Rock Typing: A Key Approach for Petrophysical Characterization and Definition of Flow Units, Santa Barbara Field, Eastern Venezuela Basin2001. SPE Conference. SPE 69458. Porras, J.C., Barbato, R., Khazen, L., 1999. Reservoir flow units: a comparison between three different models in the Santa Barbara and Pirital fields, North Monagas area, Eastern Venezuela basin. SPE Conference. SPE 53671. Raghavan, R., Dixon, T., Phan, V., Robinson, S., 2001. Integration of geology, geophysics, and numerical simulation in the interpretation of a well test in a fluvial reservoir. SPE Reservoir Evaluation & Engineering. 4 (3), 201-208. Rahimpour-Bonab, H., 2007. A procedure for appraisal of a hydrocarbon reservoir continuity and quantification of its heterogeneity. J. Petrol. Sci. Engin. 58, 1-12. Rahimpour-Bonab, H., Asadi-Eskandar, A., Sonei, R., 2009. Controls of Permian-Triassic Boundary over Reservoir Characteristics of South Pars Gas Field, Persian Gulf. Geol. J. 44, 341-364. Ratchkovski, A., Ogbe, D.O., Lawal, A.S., 1999. Application of Geostatistics and Conventional Methods to derive Hydraulic Flow Units for Improved Reservoir Description: A Case Study of Endicott Field, Alaska. SPE Conference. SPE 54587. Rincones, J.G., Delgado, R., Ohen, H., Enwere, P.,Guerini,A., Marquez, P., 2000. Effective Petrophysical Fracture Characterization Using the Flow Unit Concept-San Juan Reservoir, OrocualFieldi Venezuela. SPE Conference. SPE 63072. Sibley, M. J., Bent, J., and Davis, D. W., 1997. Reservoir modeling and simulation of a Middle Eastern carbonate reservoir. SPE Reservoir Evaluation & Engineering. 12 (2), 75-81. Soleimani, B., Nazari, K., Bakhtiar, H., Haghparast, G., and Zandkarimi, G., 2008. ThreeDimensional Geostatistical Modeling of Oil Reservoirs: A Case Study From the Ramin Oil Field in Iran. J. Appl. Sci. 8 (24), 4523-4532. Soto R., Torres, F., Arango, S., Cobaleda, G., 2001. Improved Reservoir permeability Models from Flow Units and Soft Computing Techniques: A Case Study, Suria and Reforma- Libertad Fields, Colombia. SPE Conference. SPE 69625. Szabo, F., Kheradpir, A., 1978. Permian and Triassic stratigraphy, Zagros Basin, South- West Iran. J. Petrol. Geol. 1, 57-82.

Ti, G., Ogbe, D., Munly, W., Hatzignatiou, D., 1995. The Use of Flow Units as a Tool for Reservoir Description: A Case Study. SPE Formation Evaluation. 10 (2), 122-128. Valle, A., Faulhaber, J.J., Keith, T.H., Hsueh, P.T., 1997. Development of an integrated reservoir characterization and simulation model for a heterogeneous carbonate reservoir, ArabD reservoir, east flank of Ghawar field. SPE Conference. SPE 37778. Van de Graaff, W., Ealey, P., 1989. Geological modeling for simulation studies. AAPG Bull. 73 (11), 1436-1444. Walker, R. G., 1990. Facies modeling and sequence stratigraphy-Perspective. J. Sediment. Petrol. 60, 777-786. Webber, K., and Van Geuns, L., 1990. Framework for constructing clastic reservoir simulation models. J. Petrol. Tech., 42, 1248-1253. Wendt, W.A., S. Sakurai and P.H. Nelson, 1986. Permeability Prediction From Well Logs Using Multiple Regression. In: Reservoir Characterization, Lake, L.W. and H.B. Carroll (Eds.). Academic Press, Orlando, Florida, pp. 223-247. Ziegler, M., 2001. Late Permian to Holocene paleofacies evolution of the Arabian plate and its hydrocarbon occurrences. GeoArabia 6, 445-504.

 

Figure 1: Generalized stratigraphic column of the Permian‐Triassic succession in the studied area (modified after  Rahimpour‐Bonab et al., 2009).   

Figure 2: Schematic conceptual depositional model of the Khuff Formation in the studied area (adopted and  modified after Insalaco et al., 2006).   

 

Figure 3: Reservoir characteristic of the depositional facies in the studied formation, the scattered porosity‐ permeability makes the permeability prediction very hard.  

     

 

Figure 3 (Continued): Reservoir characteristic of depositional facies in thestudied formation. 

       

 

  Figure 4: Sequence stratigraphic reservoir zonation (SSF based zonation) in some of the studied wells (Log tracks  from left to right in each well section: GR (0‐100 API), RHOB (1.95‐2.95 g/cm3)/NPHI (‐0.15‐0.45), PHIE (0‐0.3). SB=  sequence boundary, blue triangle= sea transgression phase, red triangle= sea regression phase.   

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  Figure 6: Comparison of SSF based zonation (Left) with HFUs (Right) in the studied formation (Log tracks from left  to right in each well section: GR (0‐100 API), RHOB (1.95‐2.95 g/cm3)/NPHI (‐0.15‐0.45), PHIE (0‐0.3).

  Figure 7: Well to well correlation of the hydraulic flow units in the studied formation (Log tracks from left to right  in each well section: GR (0‐100 API), RHOB (1.95‐2.95 g/cm3)/NPHI (‐0.15‐0.45) & PHIE (0‐0.3). Upper Kangan (UK)  and Nar are non reservoir zones and hence not classified in HFUs. 

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2000 2100   Figure 8: Comparison  of PHIE and SPHIE in the studied formation, the correlation coefficient between PHIE and  SPHIE is approximately 0.5. 

  Figure 9: Comparison of PHIE (black line) and SPHIE (blue line) in the studied area. SPHIE follows the general trend  of PHIE but it is usually overestimated or under estimated and hence a correction has been done on SPHIE to make  the PHIE and SPHIE consistent. 

 

Figure 10: Examples of the correlation coefficient maps (cc‐maps) used to calibrate SPHIE with PHIE while  propagation of porosity into the 3D grid. 

 

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  Figure 11 (A&B): Existence of cyclicity in some reservoir zones indicates disconnected carbonate shoals and inter‐ lagoon deposits in the studied basin. Continuity of the reservoir property is reduced due to the cyclicity effect.    

 

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  Figure 12: The main anisotropy direction coincides with the depositional trend, original shoreline is supposed to be  along NW‐SE with lateral change in depositional along SW‐NE.                                       

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  Figure 13: Modeled PHIE in the K1 unit (A). The horizontal variogram (B) indicates a range of 10Km continuity in  this zone. Vertical variogram (C) shows a range up to 15m.  

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  Figure 14: Modeled PHIE in K2 unit (A). The horizontal variogram (B) indicates a range of 9.5Km in this zone.  Vertical variogram (C) shows a range of up to 10m, a minor cycle effect is observed at the middle of this zone.     

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    Figure 15: Modeled PHIE in K3 unit (A). A range of about 8Km is assigned to this zone based on horizontal  variography (B). Vertical range is about 4m indication strong heterogeneity in z dimension at this zone.    

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     Figure 16: Modeled PHIE in K4 unit (A). This zone represents the maximum continuity in the studied reservoir with  a horizontal range (B) reaching to more than 11Km and vertical range of up to 18m.     

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    Figure 18: Examples of modeled RRT in the main reservoir units (A=K1, B= K2).   

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  Figure 20: Example of water Saturation model in the studied reservoir (K1 zone), as obvious the saturation model is  completely compatible with RRT model because the water saturation model is directly derived from the capillary  pressure curves in each RRT.              

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35

40

PHI (%)   Figure 21: Global correlation coefficient of the porosity‐permeability in the studied reservoir.

   

Zone 6 (K2‐TST)

Zone 4 (K1 LST)

10.00

1000

y = 0.0548e30.809x R² = 0.6656

100

1.00

K (mD) 

K (mD) 

10

y = 0.0829e21.266x R² = 0.6468

1

0.10

0

0.01

0 0.00

0.05

0.10

0.15

0.20

0.25

0.00

0.05

PHIE_Frac

0.10

0.15

0.20

0.25

0.30

PHIE_Frac

    Zone 11 (K3‐Late TST)

Zone 15 (K4 Late HST)

1000

1000

y = 0.3437e20.161x R² = 0.6417 100

100

10

K (mD) 

10

K (mD) 

y = 0.0503e29.436x R² = 0.7219

1

1

0

0

0

0 0.0

0.1

0.2

PHIE_Frac

0.3

0.4

0.00

0.05

0.10

0.15

0.20

PHIE_Frac

0.25

0.30

  Figure 22: Examples of Porosity‐permeability relationship in SSF based reservoir zones representing excellent  correlation coefficient in comparison with global poro‐perm relationship of the studied formation.             

A 

B 

Figure 23: Examples of the modeled permeability in the main reservoir units (A=K1, B=K2)

C 

 

 

D 

      Figure 23 (Continued): Examples of the modeled permeability in the main reservoir units (C=K3, D=K4)   

   

Figure 24: Comparison of the original PHIE (black), up‐scaled PHIE (pink) indicating the accuracy of well log up‐ scaling in the studied reservoir.  Z size is variable depending on the reservoir quality of each zone with a range of  0.8m for porous layers and 3‐5m for tight layers (average of 1.2m). PHIE up‐scaling method is arithmetic averaging.      

 

   

A 

B 

  Figure 25 (A&B): Histogram comparing up‐scaled and original PHIE (up) and K (down). Z size is variable depending  on the reservoir quality of each zone with a range of 0.8m for porous layers and 3‐5m for tight layers (average of  1.2m). PHIE up‐scaling method is arithmetic averaging.                                 

Figure 26: Comparison of the Modeled PHIE in the up‐scaled 3D grid (green) with the SPHIE (blue) showing a good  correlation. Grid size in up‐scaled 3D grid is 500*500. Z layers are variable in each zone which depends on the  thickness of HFUs (average of 15m).     

    Figure 27: Comparison of the Modeled PHIE in the scaled‐up 3D grid (green) with the SPHIE (blue) and PHIE (black)  indicating a loss of information in z‐dimension due to grid scale‐up. Dynamic model performance of the reservoir is  very good however which shows that in case of accurate integrated studies, coarse grids can perform similar to  fine grids although some details will be lost in z‐dimension as a result of grid scale‐up.       

A 

 

B

  Figure 28: The performance of simulation grid was absolutely acceptable comparing the model with the real  production history of the filed which showed that loss of values in Z‐dimension will not be a big deal even in  heterogeneous formations. The key to accurate reservoir modeling is real multi‐disciplinary understanding and  proper integration of geological and reservoir engineering data.  

         

Table1: Core facies classification of the Khuff Formation in the study area

                                                       

Code

Description

CF1

Anhydrite (Massive to Layer)

CF2

Mudstone often Fenestrate/Evaporate Casts

CF3

Stromatolite Boundstone

CF4

Wackestone to Packstone (Skeletal/Peloid often with Oncoids)

CF5

Onciod, Peliod Packstone to Grainstone

CF6

Fine-grained Ooid, Peloid Grainstone

CF7

Medium-grained Skeletal, Ooid Grainstone

CF8

Coarse-grained Skeletal, Intraclast Grainstone

CF9

Beach Barrier Packstone to Grainstone

CF10

Intra-formational Conglomerate/Collapse Breccia

CF11

Bioturbated Mudstone to Wackestone

CF12

Dark argillaceous Mudstone to Claystone

CF13

Fossiliferous mudstone to skeletal wackestone

CF14

Shale to Claystone

CF15

Thrombolite Boundstone

  Table 2: Reservoir Rock Types (RRTs) in the studied formation

RRT 1 2 3 4 5 6 7 8 9 10 11 12

Porosity (frac.) 0.0137 0.0665 0.17 0.27 0.0115 0.084 0.174 0.25 0.06 0.1 0.18 0.25

Permeability (md) 0.09 0.5 2.35 2.86 1 10.6 19 34 1 112 276 481

RQI (µm) 0.08 0.09 0.12 0.1 0.29 0.35 0.33 0.37 0.13 1.05 1.23 1.38  

PhiZ 0.01 0.07 0.2 0.37 0.01 0.09 0.21 0.33 0.06 0.11 0.22 0.33

FZI (µm) 5.79 1.21 0.57 0.28 25.17 3.85 1.56 1.1 2.01 9.46 5.6 4.13

Swc 0.57 0.4 0.16 0.06 0.58 0.35 0.15 0.07 0.42 0.31 0.15 0.07

• • • • •

Integrated reservoir study  Corporation of the seismic data into  geological grid  Reservoir zonation using sequence stratigraphic framework   Innovation in permeability prediction and modeling  Grid up‐scaling suing hydraulic flow units