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Integration of sonic and resistivity conventional logs for identification of fracture parameters in the carbonate reservoirs (A case study, Carbonate Asmari Formation, Zagros Basin, SW Iran)
Journal Pre-proof Integration of sonic and resistivity conventional logs for identification of fracture parameters in the carbonate reservoirs (A case study, Carbonate Asmari Formation, Zagros Basin, SW Iran) Ghasem Aghli, Reza Moussavi-Harami, Behzad Tokhmechi PII:
S0920-4105(19)31148-9
DOI:
https://doi.org/10.1016/j.petrol.2019.106728
Reference:
PETROL 106728
To appear in:
Journal of Petroleum Science and Engineering
Received Date: 24 July 2018 Revised Date:
28 October 2019
Accepted Date: 19 November 2019
Please cite this article as: Aghli, G., Moussavi-Harami, R., Tokhmechi, B., Integration of sonic and resistivity conventional logs for identification of fracture parameters in the carbonate reservoirs (A case study, Carbonate Asmari Formation, Zagros Basin, SW Iran), Journal of Petroleum Science and Engineering (2019), doi: https://doi.org/10.1016/j.petrol.2019.106728. 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.
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Integration of Sonic and Resistivity Conventional Logs for Identification of Fracture Parameters in the Carbonate Reservoirs (A Case Study, Carbonate Asmari Formation, Zagros Basin, SW Iran) Ghasem Aghli (1.*), Reza Moussavi-Harami1, Behzad Tokhmechi 2
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1. Department of Geology, Ferdowsi University of Mashhad, Mashhad, Iran
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2. School of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
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[email protected]
Abstract
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Between all available methods for fractures evaluation, core and image logs are prime methods. However, both of these data are very expensive and practically available in less than 10% of drilled wells. Therefore, a less expensive and quick method would be very helpful for fracture analysis. The main aim of this study is to identify the fracture parameters in the carbonate reservoirs, i.e. fracture density and fracture aperture, using a combination of sonic, resistivity and other conventional logs which commonly available in all wells. For this aim, fractures and their parameters were precisely determined on the image logs in 6 wells from carbonate Asmari Formation as a typical fractured reservoir in the world. Then, the effect of fracture parameters on the sonic and resistivity logs were used to identify fracture parameters in the wells with no core and image logs. Results indicate that the zones with high fracture density and aperture are easily detectable by conventional logs. On the other hand, the behavior of these logs is a function of fracture parameters in the fractured intervals, especially in the zones with high fracture aperture. Separation between shallow (RLA2) and deep (RLA5) resistivity logs along with their cross plot are very reliable indicators for fractured intervals. Sonic transit time rapidly increases where fractures density or aperture increased as well. In this study, a combination of sonic and neutron porosity logs have also been used to determine the effect of fracture aperture, as the main effective factor on the reservoir properties. Furthermore, full waveforms in some studied wells combined with sonic transit time to improve the results of fractures evaluation. Using sonic and resistivity along with neutron porosity and gamma ray logs provides a reliable method for describing the fracture parameters which shows high correlation with image logs results. Due to the high impact of fracture aperture on both reservoir quality and conventional log responses, determination of this parameter is more crucial than other fracture parameters. This method is also applicable for OBM (Oil Base Mud) image tools which conventionally cannot detect fracture aperture.
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Keywords: Fractures, image logs, sonic logs, reservoir parameters, resistivity logs
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1. Introduction
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Fractured reservoirs formed giant oil and gas fields in the Zagros Basin (Alavi, 2007; Ameen et al., 2012; Nelson, 2001). Hence, studying and evaluating fractures are known as the basic challenge during production optimization and enhance recovery processes (Cacas et al., 2001; Nelson, 2001; Nie et al., 2013). Carbonate Asmari Formation is the most important and typical fractured reservoir in the Zagros Basin (Khoshbakht et al., 2009). In the recent years, image logs are considered as the best method for fractures evaluation (Aghli et al., 2014; Khoshbakht et al., 2012). However, this technique is mostly new and expensive and commonly unavailable in most drilled wells (Lyu et al., 2016; Martínez, 2002). In addition, among all image logs, only some electrical tools are capable to measure all fracture parameters such as fracture porosity and aperture (Aghli et al., 2017; Luthi and Souhaite, 1990). Two common electrical tools, FMI (Formation Micro Imager) and EMI (Electrical Micro Imager) are widely used by petrophysicists and geologists for formation and fractures evaluation (Chen and Wang, 2017; Rajabi et al., 2010). Sonic imaging tools, which usually run in the oil base mud systems, don’t have ability to measure fracture parameters and this is known as the most important weak point of these tools (de Jesus et al., 2016). Therefore, many attempts were devoted to evaluate fractures by complimentary methods, particularly prevalent conventional logs, as a replacement for image logs and core data (Aghli et al., 2016; Lyu et al., 2016; Saboorian-Jooybari et al., 2015; Tokhmchi et al., 2010; Zazoun, 2013). These studies indicated that the conventional logs can be used for identification of fractured zones and qualitatively for determination of fracture parameters. The behavior of conventional logs is a function of several factors e.g. lithology, porosity type, fluid flow, heterogeneity and fractures. So, many methods have been developed for purification of fractures response on the conventional logs, such as wavelet (Mohebbi et al., 2007; Tokhmechi et al., 2009), differentiation (Aghli et al., 2016), step by step analysis (Lyu et al., 2016), combination with other methods (Chen and Wang, 2017) and etc. It is notable that, thanks to the high effect of fractures on the reservoir quality, merely detection of fractured zones is not valuable for evaluation of reservoir porosity and permeability. Conventional logs such as sonic, porosity, gamma ray and resistivity are the best logs for evaluation of fractured intervals due to their sensitivity to the fractures (Lyu et al., 2016). Because of availability of the sonic and resistivity logs nearly in all wells, this study aims to use these logs and also their combination with other conventional logs for determination of fracture parameters in the fractured intervals. We refined the response of these logs by removing heterogeneity effect to extract real fracture behaviors. The present research tries to disclose the role of fracture parameters, i.e. density and aperture, on the behavior of conventional logs. The new approach may be applied in Oil Based Mud environments where the routine image analysis would not provide more information about the fractures parameters especially fracture aperture.
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2. Geological Setting
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Two Iranian oil fields with similar reservoir were chosen for this study. Both of these fields are located in the Dezful Embayment, Zagros Basin, SW Iran. Dezful Embayment is knows as a petroliferous part of the Middle-East with active tectonic as well, due to the activity of Zagros 2
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convergence. Zagros fold-thrust belt is formed during collision between the Iranian and Arabian plates, starting during the Late Cretaceous time and expedited during the Late Miocene and Pliocene time (Stocklin, 1968). This convergence is still active via NNE–SSW (maximum stress) direction (DeMets et al., 2010). Asmari Formation is the main reservoir in the Zagros Basin, containing about ¾ of all oil in place (OIP) in this area. The Asmari Formation consists of limestones (light gray to white shallow-marine grainstone, pelletal packstone and bioclastic wackestone) with intercalations of black fissile shale and anhydrite (Alavi, 2004). The Oligocene–Miocene Asmari Formation is a thick sequence of shallow water carbonate. The depositional environments of Asmari Formation correspond to inner, middle and outer ramp. In the inner ramp, the most abundant lithofacies are medium-grained wackestone–packstone with imperforated foraminifera. The middle ramp is represented by packstone–grainstone to floatstone. The outer ramp is dominated by argillaceous wackestone (Vaziri-Moghaddam et al., 2010). As shown in Figure 1, both of the studied fields are mainly influenced by a thrust fault in the southern flank and several strike slip faults. These active faults have complicated structural analysis of the studied fields. For example, in some wells the direction of maximum stress is different to general trend of maximum horizontal stress in the Zagros (NE-SW). The direction of maximum stress has been determined by strike of Drilling Induced Fractures (DIF) detected using image logs (Introduced method by Tingay et al., (2008)) for studied wells.
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3. Methods and Material
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Electrical (FMI, EMI and OBMI) and sonic (UBI) image logs integrated with conventional well logs (Sonic, Resistivities, Neutron Porosity, Density and Gamma ray) of 6 wells from Asmari Formation were interpreted for fracture identification and parameters calculation. For more accuracy, non-reservoir intervals such as shale and anhydrite were eliminated due to their flexibility and plasticity. At first, all open fractures were hand-picked on the image logs and their related parameters i.e. density and aperture, were calculated. Then, the effect of fracture parameters were evaluated on the conventional logs, especially sonic and resistivity logs. To improve the results, neutron porosity and gamma ray logs combined with available data as complimentary methods. Finally, regarding to the effect of fracture parameters on these conventional logs, a valid method was introduced for identification of fractures in the wells without core and image logs. Figure 2 illustrates the designed flowchart of the study. Between available image logs, only FMI and EMI are capable to measure all fracture parameters. OBMI (Oil Base Mud Imager) and UBI (Ultrasonic Borehole Imager), which usually applied in the OBM systems simultaneously, have less quality and merely used for detection of fractures, not fractures parameters. However, in the high pressure and temperature reservoir, application of oil based muds is inevitable to decrease the drilling risks and increase efficiency (Chen and Wang, 2017). So, a combination of these image logs with conventional logs is the best method for eliminating the main weak points of applied image logs in the OBM systems for fracture parameters detection. In addition, a combination of sonic, resistivity, porosity and gamma conventional logs may help petrophysicists and geologists to determine fractured zones and fracture parameters in the wells with no image logs and core data. 3
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4. Results and Discussion
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After analysis of electrical and sonic image logs in the 6 studied wells, several features such as fracture, bedding, induced fracture, borehole breakout and stylolite were identified. Considering to the importance of open fractures due to the aim of this study, all fracture parameters precisely calculated for these fractures in the WBM (water base mud) wells. Also, induced fractures and borehole breakouts were used to identify in situ stress direction in the studied fields (Fig. 1). It is also noteworthy that, between all fracture parameters; porosity, density and aperture are known as the most effective factors on the reservoir properties (Aghli et al., 2017). However, fracture porosity is too small value and only measurable using some electrical image logs such as FMI and EMI (Zerrouki et al., 2014). Due to its small values, the effect of fracture porosity is not detectable on the conventional logs, while fracture aperture may be considered as a dependable proof for fracture porosity. Results for identification of fracture parameters using conventional logs are presented in the following sections:
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4.1. Determination of fractures and their parameters using image logs
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The main aim of this research is to identify the effect of fracture parameters on the conventional logs to find a reliable way for determination of fracture parameters by conventional logs in the wells with no core and image logs data. Therefore, open fractures and their parameters should be precisely determined at first and some electrical image logs considered as the best tools for this purpose. Electrical image logs, i.e. FMI and EMI, are able to measure fracture aperture and fracture porosity as well as fracture density. Detection of fractures aperture is known as the biggest challenge for electrical image logs, while sonic image logs are not able to measure this parameter (Crain and Eng, 2006). Inability of sonic image logs for fracture aperture detection as well as their low resolution than electrical image logs heavily decreases their efficiency. However, as mentioned above, in the high pressure reservoirs, OBM drilling is better and safer than WBM. Therefore, in this situation, usually sonic and low quality electrical image logs such as OBMI-UBI have been run. So, it is necessary to find a method to cover the weak point of OBM image logs and detect fracture aperture in these wells. On the image logs, fractures are displayed as sinusoid features with dip more than structural dip (Schlumberger, 2003). Open fractures represent a conductive appearance on the electrical images, because their aperture is filled with the conductive drilling mud (in the WBM systems). However, the appearance for closed or filled fractures, if the filling materials be dense like calcite or anhydrite, is resistant (Serra, 1989). After hand-picking of all open fractures in the studied wells by FMI and EMI Image logs, fracture aperture was calculated using Luthi and Souhaite (1990) equation.
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1. where ‘W’ is fracture aperture (mm), ‘A’ is an access flow of each electrode, ‘Rm’ is the
ܹ = ܿ. ܣ. ܴ݉ . ܴݔଵି resistivity of drilling-mud (ohm), ‘Rxo’ indicates the resistivity of the invaded zone (ohm), c and b are constants, based on the tool and environmental properties. In general, 4
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ܣrepresents the additional current which may be injected through the formation divided by the voltage, integrated along a line perpendicular across the fracture trace. Parameter c and b are obtained from forward modeling numerically and their values are related to exact image tool feature. Coefficient c is related to a geometric factor, integrating the measured resistance to the actual formation resistivity (Rxo). Exponent b indicates the sensitivity of A to the resistivity contrast between formation and borehole mud and its value is primarily influenced by the amount of current focusing as a function of the resistivity contrast and the borehole diameter. In order to focus equally, b is assumed as b in all resistivity regimes and borehole sizes. Since the exponent b was found to be relatively close to 1, the added conductance A is primarily dominated by the aperture and the fluid in the fracture, and to a less degree by the resistivity of the formation in which the fracture occurs. Figure 3 shows the results of electrical image logs for determining of open fractures in some studied wells. The results of image logs entirely confirmed that the Asmari Formation in both studied fields is completely fractured with structural evidences. All detected fractures in Figure 3 are open and show very high aperture in some intervals. It is also noteworthy that hand-picking of fractures heavily increases the accuracy of results rather than automatic fracture detection methods. Systematic fracture analysis for studied wells indicated that open fractures can be classified in three groups. Longitudinal, oblique and transverse sets which longitudinal fractures are dominant and mainly open with high aperture (Fig. 4). Longitudinal fractures generally related to the folding and tensional systems (Fossen, 2010).
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4.2. The response of open fractures on the sonic logs
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There are many published literature about the propagation of acoustic waves in the fractured rocks and effect of fractures on their pattern (Anselmetti and Eberli, 1999; Brie et al., 1985; Raymer et al., 1980). Unfortunately, most of these studies are theoretical and are not supported by experimental and real data and only discussed the effect of fracture density among all fracture parameters. However, the sonic logs are the best fracture detector in older wells due to the unavailability of new and more modern methods at the time. Moreover, these logs are a very valuable method in the new drilled wells due to the weak points of image logs and core data. Sonic log (DT) is a log that computes the transit time in the formations. Many equations and relationships have been introduced between travel time and porosity system which Wyllie equation is the most common one (Wyllie et al., 1956). DT only indicates the values of P-wave, therefore sonic waveforms which include shear, compressional and stoneley waves, would be very useful methods for fractures detection as well (Chen and Wang, 2017). Undoubtedly, presence of open fractures heavily reduce the formation compaction and density and increase the sonic transit time, especially those with high aperture (Aghli et al., 2016). Also, this decrease is possible due to the low sonic waves velocity in fluids –open fractures are main pathway for fluids- compared to rock matrixes (Tokhmechi et al., 2009). Moreover, cycle skipping is a valid indicator for presence of fractures. The DT shows the first arriving sonic wave (compressional or P wave), and the mentioned cycle skipping may happen where the open fractures are presence and attenuation is unusually high. This attenuation is completely clear in the trend of DT in the 5
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studied wells, particularly in the zones with highest aperture (Fig. 5). Generally, DT shows more relation with fracture density than fracture aperture which is expectable, because sonic waves choose the easiest way for transit and this is independent from the value of fracture opening. Therefore, DT and other sonic logs are reliable methods for determination of the zone with more fracture density (Please see the caption of Figure 5 for more explanations) As mentioned above, full sonic waveforms are beneficial for fractures determination as well as DT. On the array sonic log, travel time and attenuation of the shear, compressional and Stoneley wave energies are measurable, in comparison to conventional DT which measures merely compressional wave slowness (Sibbit and Faivre, 1985). Theoretically, open fractures effect on the compressional transit time less than shear log as long as a free matrix path is present between receivers and transmitter. In contrast, energy of shear wave is, in theory, rapidly decreased by both vertical and horizontal fractures as shown clearly on Figure 6 (Crain and Eng, 2006). Practically, shear waves are not conductible through of fluids and open fractures which filled by fluids, hence fractured zones heavily attenuate the shear arrivals (Laongsakul and Dürrast, 2011). The results of this study show that the amplitude of Stoneley wave is low in the fractured zones (Fig. 6). Actually, compressional waves travel faster than shear and stoneley waves and theoretically separate on a sonic waveforms plot. The parameters that affected on the amplitude of all waves include the porosity, formation fluid, borehole rugosity, rock type, tool centralization and fracture size and fracture orientation. Hence, energy depending on many parameters, so absolute values mean little, while low values of amplitude often show fractures in the carbonate reservoirs. The effect of fractures dip on the conventional logs has been discussed by Aguilera (2010), however most of fractures in this study have dip more than 50 degree (Fig. 4) and nearly create same response on the logs.
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4.3. The response of open fractures on the resistivity logs
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Generally, all transmitters and receivers type instruments (i.e. sonic and resistivity tools) are pad contact tools that usually detect fractures successfully and better than other tool types. Hence, if the borehole is normal due to the fractures and washouts, most of them will be seen because they are considered as a cause of anisotropy in the reservoirs. Moreover, these types of devices are useful to identify the parameters of fractures, even though qualitatively. In recent studies, resistivity logs are widely used for fractures studying. Because they have higher resolution than other conventional logs (Lyu et al., 2016; Saboorian-Jooybari et al., 2015). Also, resistivity tools record a set of logs with different depth of investigations (DOI) which include shallow and deep logs and provide better data set for fractures evaluation. Generally, if the resistivity of mud (Rm) is lower than the formation resistivity (Rt), as it is true for all WBM systems, then the shallow resistivity usually read lower resistivity and cross over the deep resistivity in the fractured zones because of more invasion in these intervals (Fig. 7). The separation between deep and shallow resistivities can be used for quantification of fractures in the fractured reservoirs. In the WBM systems, where fractures are open, shallow and deep resistivity logs show same records. This is expectable due to invasion of mud through open 6
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fractures, particularly where open fractures made a fracture system. This character is clearly observable in the Figures 7 and 8, where both of deep and shallow resistivity logs mostly show values less than 100Ω. Statistical parameters for shallow and deep resistivity logs may be useful for better evaluation of fracture response on the theses logs (Table 1). For example, maximum, minimum and median of deep resistivity values in the non-fractured zones for well A are 16784, 1.6 and 34 Ω respectively and for fractured zones are 305, 1.6 and 13. These parameters on the shallow resistivity log in this well are 166, 1.5 and 23 in the non-fractured zones and 121, 1.5 and 10 in the fractured zones. These statistical parameters help to find that the values of shallow and deep resistivity logs quickly decreased in the fractured zones especially those with high aperture. Also, they show more same records in the fractured intervals which increase their liner regression (represented in the figure 7) in these intervals (table 1).
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4.4. Other important conventional logs for fractures evaluation
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During our study, we found that there is an interesting relationship between fracture parameters and porosity logs, especially neutron porosity (NPHI). Other conventional logs such as sonic, density and resistivity logs have shown more relation with fracture density, while NPHI log was completely a function of fracture aperture. The wells in this research were selected from an entirely fractured reservoir with active tectonic in the SW Iran. Hence, all detected fractures by image logs are mainly open with medium to high aperture (0.1mm). Therefore, open fractures filled by high hydrogen contents (hydrocarbons and formation water) heavily affect NPHI values. Figure 9 shows the sensitivity of NPHI log to the values of fracture aperture in the studied wells. So, NPHI log and its combination with sonic logs is the best method to detect open fractures. Also, in some cases, the separation between SGR and CGR gamma ray logs is a quick qualitative indicator for fractured zones detection to the presence of uranium in the reservoir fluid flow in open fractures, particularly in the dolomitic zones (Fig. 6). Regarding to the Figure 6, among carbonates, dolomite has highest potential for presence of uranium as well as occurrence of fractures.
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5. Evaluation of the results in an oil base mud well (well E)
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As previously mentioned, image logs for OBM systems, have usually low quality and reliability than those for WBM systems. Furthermore, OBM image tools don’t have ability to measure fractures and reservoir parameters, especially fracture porosity and aperture. This shortcoming considered as the main weak point of OBM image logs. In this study, well E is chosen for evaluating the results of this research as an OBM well with OBMI-UBI image logs where only fracture density was determinable using these image logs. So, we tried to evaluate the reliability of detected fracture density and determine the opening of fractures in this well using a combination of sonic waveforms and gamma ray logs (Fig. 6), NPHI porosity and DT logs (Fig. 10-a) and resistivity logs (Fig. 10-b). Therefore, evaluation of fracture parameters in this well is explained below in the five detected zones in Figure 10. Zone 1: based on the separation between SGR and CGR (this well located in the same field as wells A and D and this separation is mainly 7
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observed at the top of Asmari Formation), fluctuation of the waveforms (Fig. 6), trend of DT and NPHI (Fig. 10-a) and propinquity of deep and shallow resistivity logs (Fig. 10-b), this zone is completely fractured with high aperture (dolomitic zone). Zone2: DT and waveforms logs show that this interval has a high fracture density potential, however NPHI and resistivity logs indicate that these fractures would be shown low aperture (calcitic zone). Zone 3: the values of all conventional logs for this interval confirm that the real fracture density in this zone is much more than detected density by image log and these fractures are potentially open (mostly dolomitic). Zone 4: this zone is a fractured zone with very low aperture (calcitic zone). Zone 5: the separation between SGR and CGR again observed in this zone and irregularity in the trend of other logs confirm that this zone is a fractured zone with good to high aperture (dolomitic zone). Generally, results indicate that the measured fracture density for this well is much less than the real values (in comparison with other wells), due to the low quality of OBMI-UBI image logs. Also, most of detected fractures, particularly in the dolomitic zones, are open and their effect on the conventional logs is obviously detectable.
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6. Conclusion
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Between direct and indirect methods of fractures evaluation, image log interpretation is the best and reliable choice. Nevertheless, image logs are known as an expensive and new method which practically available in a few drilled wells. Furthermore, designed image logs for oil base mud wells e.g. OBMI-UBI, have poor accuracy and ability to detect fractures and measuring their parameters. However, in the heterogeneous carbonate reservoirs, the effect of fractures on the reservoir quality should be evaluated and for this reason, measuring of fracture parameters, especially aperture is necessary. On the other hand, identification of fracture opening is more important than other parameters in the fractured reservoirs. In the wells with no image logs and core, a combination of conventional logs –not a specific log- is the paramount way in the detection of fracture parameters. Particularly array logs which come from a set of one transmitter and several receivers such as some sonic and resistivity tools. Also, this combination is applicable for OBM systems where designed image logs are not able to measure fracture aperture. Sonic transit times, waveforms and resistivity logs can precisely determine fractured zones. Moreover, their combination with NPHI porosity and gamma ray logs clearly increase the reliability of their results, especially for identification of fracture aperture. Among conventional logs, neutron porosity shows the highest relevance with fracture aperture due to the concentration of high hydrogen content fluids in the open fractures. It seems that sonic transit time and all waveforms have depended on the fracture density more than fracture aperture due to their moving traits. However, sudden decreases on the resistivity logs in the fractured zones was observed and it is clearly visible that the values of shallow and deep resistivity logs approach to each other (less than 100Ω in this case) on their cross plot. Also, their behavior shows a relative relationship with fracture aperture due to the concentration of less resistive fluids in the open fractures. Finally, results indicate that detected fracture density by OBMI-UBI image logs in an OBM well is less than the real value because of their lower quality than WBM image tools. Also, combination of all studied conventional logs successfully identifies the zones with highest 8
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fracture aperture in this OBM well. The results of our study confirmed that fractures concentration in the dolomitic zones is more than the limestone zones and they also showed more aperture as well.
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Rajabi, M., Sherkati, S., Bohloli, B., Tingay, M., 2010. Subsurface fracture analysis and determination of in-situ stress direction using FMI logs: An example from the Santonian carbonates (Ilam Formation) in the Abadan Plain, Iran. Tectonophysics 492, 192–200. https://doi.org/10.1016/j.tecto.2010.06.014
30 31
Raymer, L.L., Hunt, E.R., Gardner, J.S., 1980. An improved sonic transit time-to-porosity transform, in: SPWLA 21st Annual Logging Symposium. Society of Petrophysicists and Well-Log Analysts.
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Saboorian-Jooybari, H., Dejam, M., Chen, Z.J., Pourafshary, P., 2015. Fracture Identification and Comprehensive Evaluation of the Parameters by Dual Laterolog Data, in: SPE Middle East Unconventional Resources Conference and Exhibition. Society of Petroleum Engineers.
35 36
Schlumberger, 2003. Using borehole imagery to reveal key reservoir features, in: Reservoir Optimization Conference. Tehran, Iran.
37
Serra, O., 1989. Formation MicroScanner image interpretation. Schlumberger Educational Services.
38
Sibbit, A.M., Faivre, O., 1985. The dual laterolog response in fractured rocks, in: SPWLA 26th Annual 10
1
Logging Symposium. Society of Petrophysicists and Well-Log Analysts.
2 3
Stocklin, J., 1968. Structural history and tectonics of Iran: a review. Am. Assoc. Pet. Geol. Bull. 52, 1229–1258.
4 5
Tingay, M., Reinecker, J., Müller, B., 2008. Borehole breakout and drilling-induced fracture analysis from image logs.
6 7
Tokhmchi, B., Memarian, H., Rezaee, M.R., 2010. Estimation of the fracture density in fractured zones using petrophysical logs. J. Pet. Sci. Eng. 72, 206–213.
8 9 10
Tokhmechi, B., Memarian, H., Noubari, H. a, Moshiri, B., 2009. A novel approach proposed for fractured zone detection using petrophysical logs. J. Geophys. Eng. 6, 365–373. https://doi.org/10.1088/17422132/6/4/004
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Vaziri-Moghaddam, H., Seyrafian, A., Taheri, A., Motiei, H., 2010. Oligocene-Miocene ramp system (Asmari Formation) in the NW of the Zagros basin, Iran: Microfacies, paleoenvironment and depositional sequence. Rev. Mex. Ciencias Geológicas 27.
14 15
Wyllie, M.R.J., Gregory, A.R., Gardner, L.W., 1956. Elastic wave velocities in heterogeneous and porous media. Geophysics 21, 41–70.
16 17 18
Zazoun, R.S., 2013. Fracture density estimation from core and conventional well logs data using artificial neural networks: The Cambro-Ordovician reservoir of Mesdar oil field, Algeria. J. African Earth Sci. 83, 55–73. https://doi.org/10.1016/j.jafrearsci.2013.03.003
19 20 21
Zerrouki, A.A., Aïfa, T., Baddari, K., 2014. Prediction of natural fracture porosity from well log data by means of fuzzy ranking and an artificial neural network in Hassi Messaoud oil field, Algeria. J. Pet. Sci. Eng. 115, 78–89.
22 23 24 25 26 27 28 29 30
11
1 2 3 4 5 6
12
Table 1. Statistical parameters for resistivity logs of two WBM studied wells A and B. Results indicate that, both deep and shallow resistivity logs represent a great decrease in the fractured zones and their values are more same in these zones. Well A
Fractured zone RLA2
Maximum Median Minimum
Well B
121 10 1.5
305 13 1.6
Fractured zone RLA2
Maximum Median Minimum
RLA5
913 54 8
Non-fractured zone RLA2
RLA5
166 23 1.5
16782 34 1.6
Non-fractured zone
RLA5
RLA2
RLA5
6790 88 8
2000 111 8
24410 266 11
C
B A
D
Figure 1: Location map of the Zagros Basin and UGC map of studied wells. Location of Dezful Embayment in the SW Iran (A), location of studied fields in the Dezful Embayment (B), UGC map of studied fields with their active faults (C and D). Rose diagrams show the strike of induced fractures in the location of studied wells, which they indicate the direction of maximum stress. The direction of maximum stress in some wells is not compatible with Zagros trend (NE-SW).
Figure 2: Flow chart for evaluation of sonic and resistivity conventional logs for fractures parameters identification, designed in this study.
Well A
Well B
Well C
Well D
Figure 3: Detected open fractures and their parameters in the 4 of studied wells with EMI and FMI image logs. Well A with 650, well B 1490, well C 550 and well D by 300 open fractures are completely fractured. Track 1 is depth per meters, track 2 shows fractures tadpoles, track 3 shows fracture logs (Blue fracture density (between 0 to 60) and Red fracture aperture (less than 0.5 cm)) and DT log (green).
Figure 4: Display of histogram and strike of open fractures (blue points) and bedding (green points) in the some studied wells. This is clear that longitudinal fractures (Red circles) are dominant and show same strike with bedding. These fractures considers as folding related fractures and tensional systems.
Well A
Well C
Well B
Well D
2800
1650 2400
1550
2750
1700 2450
1600
1750
2700
2500
1650
1800
2650
2550
1700
1850 2600
2600
1750
1900 2550
2650
1800
1950
50
60
10
20
0.02
0.06
60
80
20
40
0.1
0.2
50
60
6
14
0.1
0.2
50
70
2
8
0.01
Figure 5: The effect of fracture density and fracture aperture on the DT log for studied wells. For each well, first log is DT (µs/f), second log is fracture density (1/m) and third one is fracture aperture (mm). In the well A, DT shows fluctuation in the depth 1620 to 1700 and 1800 to 1850 which these intervals are the main fractured zones. With same explanation, intervals 2400 to 2450 and 2500 to 2600 in well B, 2575 to 2675 and 2700 to 2750 in well C, 1530 to 1650 and 1700 to 1750 in well D. It seems that, the trend of DT is a function of fracture density and, accordingly fracture aperture shows less relation with this log.
0.02
Well A
Well B
Well E
Figure 6: Display of waveforms and transit times in some studied wells. It is clear that waveforms show an irregularity in the fractured intervals, especially zones with high fracture density (FVDC) and aperture (FVAH). This fluctuation is visible on the stoneley, shear and compressional waves. Also, the separation between SGR and CGR logs is consequential and remarkable in some fractured intervals (not all zones). For example, in the well A, separation between SGR and CGR is clear in the depth 1622 to 1700, also the fluctuation of waveforms are obvious in this interval and 1800 to 1850. Same events for well B observed in the depths 2360 to 2420 and 2500 to 2600. For well E, 1500 to 1620 and 1650 to 1750. Well E is an OBM well with OBMI-UBI image logs which fracture aperture has not been measured using these tools and it seems that the detected fracture density is less than the real value, due to the low quality of this tools, particularly for zone 1500 to 1620 (this well located in the same field with well A). Furthermore, in the three wells, dolomitic zones show more values for both fracture density and fracture aperture than calcitic zones.
1000
10000
Well A
Well B
RLA5
RLA5
1000
100
100
10
Non-fractured zones Fractured zones
Non-fractured zones Fractured zones 10
10
100
RLA2
10
100
1000
RLA2
Figure 7: The cross plot between shallow (RLA2) and deep resistivity (RLA5) logs in the studied wells A and B. Fracture response on the resistivity logs is usually less than 100Ω for both shallow and deep logs. Also, these resistivity logs show more linear regression (same records) in the fractured zones.
1000
Well B
Resistivity
RLA2 RLA3 RLA4 RLA5
100
10
1 2400
2450
2500
2550
Well A
2650
RLA2 RLA3 RLA4 RLA5
10000
Resistivity
2600
Depth
1000
100
10 1650
1700
1750
1800
1850
1900
Depth
Figure 8: Schematic of resistivity logs (Ω) for wells A and B per depth (meter). Deep (RLA5) and shallow (RLA2) resistivity logs show a same values in the fractured intervals (1600 to 1700 and 1800 to 1850 in the well A and 1500 to 1600 in the well B) in contrast of other zones. The presence of separation between these logs shows non-fractured intervals or very low aperture fractured zones. Fracture parameters for these wells presented in the figure 3.
1950
Well A
Well B
Well D
Well F 2350
1650 2400
1600
1700
2400 2450
1750
2450
1800
D e p th
2500
1800
2550
2500
2600
2550
1850
2000
1900
2650
2600 1950
0.05
0.2
10
20
0.02 0.06
0.05
0.2
2
8
0.01
0.02
0.1
0.3
20
40
0.1 0.2
0.1
0.3
2
8
0.02
0.08
Figure 9: The effect of fracture density and fracture aperture on the NPHI log for studied wells. For each well, firs log is NPHI (fraction), second log is fracture density (1/m) and third one is fracture aperture (mm). It is clear that NPHI shows more relation with fracture aperture than fracture density. For example, in well A, fracture density in depth 1800 to 1850 is more than 1620 to 1700, while NPHI indicates more values in depth 1620 to 1700 as well as fracture aperture. This explanation is also extensible for well D in depth 1520 to 1600, well B in depth 2500 to 2600 and initial depths of well F.
a DT
NPHI
FVDC
1550
Zone 1
1600
Depth
Zone 2 1650
Zone 3
1700
Zone 4
Zone 5 1750
45
50
55
60
65
700.000.050.100.150.200.250.300.35 0
10
20
30
40
b AE20 AE30 AE60 AE90
Zone 1
Resistivity
1000
Zone 4
100
Zone 2
Zone 5
Zone 3
10
1 1550
1600
1650
1700
1750
Depth
Figure 10: Display of conventional logs for well E, NPHI (fraction) and DT (us/f) and fracture density (1/m) log (FVDC) (figure a) and resistivity (Ω) logs (figure b). Regarding to the trend of conventional logs, five zones with different properties were detected on the conventional logs. Results of conventional logs indicated that zones 1 and 3 are the main fractured zones in this well which it seems that detected fracture density by OBMI-UBI image logs is less than the real fracture density for these zones. High values of NPHI and DT, trend of resistivity logs and High separation for SGR and CGR logs (presented in the figure 6) indicate that these zones are main fractured zones in this well with high aperture.
Integration of Sonic and Resistivity Conventional Logs for Identification of Fracture Parameters in the Carbonate Reservoirs (A case study, carbonate Asmari Formation, Zagros Basin, SW Iran)
Highlights: 1. Conventional logs are used for fractures evaluation as an economical and available data in all wells. 2. Fracture aperture, as the most effective fracture parameter on the reservoir properties, is determined using conventional logs. 3. The presented approach can be applied in Oil Based Mud image logs for determination of fracture aperture.
Declaration of interests ☐ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
S0920-4105(19)31148-9
DOI:
https://doi.org/10.1016/j.petrol.2019.106728
Reference:
PETROL 106728
To appear in:
Journal of Petroleum Science and Engineering
Received Date: 24 July 2018 Revised Date:
28 October 2019
Accepted Date: 19 November 2019
Please cite this article as: Aghli, G., Moussavi-Harami, R., Tokhmechi, B., Integration of sonic and resistivity conventional logs for identification of fracture parameters in the carbonate reservoirs (A case study, Carbonate Asmari Formation, Zagros Basin, SW Iran), Journal of Petroleum Science and Engineering (2019), doi: https://doi.org/10.1016/j.petrol.2019.106728. 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.
1 2 3
Integration of Sonic and Resistivity Conventional Logs for Identification of Fracture Parameters in the Carbonate Reservoirs (A Case Study, Carbonate Asmari Formation, Zagros Basin, SW Iran) Ghasem Aghli (1.*), Reza Moussavi-Harami1, Behzad Tokhmechi 2
4 5
1. Department of Geology, Ferdowsi University of Mashhad, Mashhad, Iran
6
2. School of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
7 8
[email protected]
Abstract
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Between all available methods for fractures evaluation, core and image logs are prime methods. However, both of these data are very expensive and practically available in less than 10% of drilled wells. Therefore, a less expensive and quick method would be very helpful for fracture analysis. The main aim of this study is to identify the fracture parameters in the carbonate reservoirs, i.e. fracture density and fracture aperture, using a combination of sonic, resistivity and other conventional logs which commonly available in all wells. For this aim, fractures and their parameters were precisely determined on the image logs in 6 wells from carbonate Asmari Formation as a typical fractured reservoir in the world. Then, the effect of fracture parameters on the sonic and resistivity logs were used to identify fracture parameters in the wells with no core and image logs. Results indicate that the zones with high fracture density and aperture are easily detectable by conventional logs. On the other hand, the behavior of these logs is a function of fracture parameters in the fractured intervals, especially in the zones with high fracture aperture. Separation between shallow (RLA2) and deep (RLA5) resistivity logs along with their cross plot are very reliable indicators for fractured intervals. Sonic transit time rapidly increases where fractures density or aperture increased as well. In this study, a combination of sonic and neutron porosity logs have also been used to determine the effect of fracture aperture, as the main effective factor on the reservoir properties. Furthermore, full waveforms in some studied wells combined with sonic transit time to improve the results of fractures evaluation. Using sonic and resistivity along with neutron porosity and gamma ray logs provides a reliable method for describing the fracture parameters which shows high correlation with image logs results. Due to the high impact of fracture aperture on both reservoir quality and conventional log responses, determination of this parameter is more crucial than other fracture parameters. This method is also applicable for OBM (Oil Base Mud) image tools which conventionally cannot detect fracture aperture.
33
Keywords: Fractures, image logs, sonic logs, reservoir parameters, resistivity logs
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1
1
1. Introduction
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Fractured reservoirs formed giant oil and gas fields in the Zagros Basin (Alavi, 2007; Ameen et al., 2012; Nelson, 2001). Hence, studying and evaluating fractures are known as the basic challenge during production optimization and enhance recovery processes (Cacas et al., 2001; Nelson, 2001; Nie et al., 2013). Carbonate Asmari Formation is the most important and typical fractured reservoir in the Zagros Basin (Khoshbakht et al., 2009). In the recent years, image logs are considered as the best method for fractures evaluation (Aghli et al., 2014; Khoshbakht et al., 2012). However, this technique is mostly new and expensive and commonly unavailable in most drilled wells (Lyu et al., 2016; Martínez, 2002). In addition, among all image logs, only some electrical tools are capable to measure all fracture parameters such as fracture porosity and aperture (Aghli et al., 2017; Luthi and Souhaite, 1990). Two common electrical tools, FMI (Formation Micro Imager) and EMI (Electrical Micro Imager) are widely used by petrophysicists and geologists for formation and fractures evaluation (Chen and Wang, 2017; Rajabi et al., 2010). Sonic imaging tools, which usually run in the oil base mud systems, don’t have ability to measure fracture parameters and this is known as the most important weak point of these tools (de Jesus et al., 2016). Therefore, many attempts were devoted to evaluate fractures by complimentary methods, particularly prevalent conventional logs, as a replacement for image logs and core data (Aghli et al., 2016; Lyu et al., 2016; Saboorian-Jooybari et al., 2015; Tokhmchi et al., 2010; Zazoun, 2013). These studies indicated that the conventional logs can be used for identification of fractured zones and qualitatively for determination of fracture parameters. The behavior of conventional logs is a function of several factors e.g. lithology, porosity type, fluid flow, heterogeneity and fractures. So, many methods have been developed for purification of fractures response on the conventional logs, such as wavelet (Mohebbi et al., 2007; Tokhmechi et al., 2009), differentiation (Aghli et al., 2016), step by step analysis (Lyu et al., 2016), combination with other methods (Chen and Wang, 2017) and etc. It is notable that, thanks to the high effect of fractures on the reservoir quality, merely detection of fractured zones is not valuable for evaluation of reservoir porosity and permeability. Conventional logs such as sonic, porosity, gamma ray and resistivity are the best logs for evaluation of fractured intervals due to their sensitivity to the fractures (Lyu et al., 2016). Because of availability of the sonic and resistivity logs nearly in all wells, this study aims to use these logs and also their combination with other conventional logs for determination of fracture parameters in the fractured intervals. We refined the response of these logs by removing heterogeneity effect to extract real fracture behaviors. The present research tries to disclose the role of fracture parameters, i.e. density and aperture, on the behavior of conventional logs. The new approach may be applied in Oil Based Mud environments where the routine image analysis would not provide more information about the fractures parameters especially fracture aperture.
37
2. Geological Setting
38 39 40
Two Iranian oil fields with similar reservoir were chosen for this study. Both of these fields are located in the Dezful Embayment, Zagros Basin, SW Iran. Dezful Embayment is knows as a petroliferous part of the Middle-East with active tectonic as well, due to the activity of Zagros 2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
convergence. Zagros fold-thrust belt is formed during collision between the Iranian and Arabian plates, starting during the Late Cretaceous time and expedited during the Late Miocene and Pliocene time (Stocklin, 1968). This convergence is still active via NNE–SSW (maximum stress) direction (DeMets et al., 2010). Asmari Formation is the main reservoir in the Zagros Basin, containing about ¾ of all oil in place (OIP) in this area. The Asmari Formation consists of limestones (light gray to white shallow-marine grainstone, pelletal packstone and bioclastic wackestone) with intercalations of black fissile shale and anhydrite (Alavi, 2004). The Oligocene–Miocene Asmari Formation is a thick sequence of shallow water carbonate. The depositional environments of Asmari Formation correspond to inner, middle and outer ramp. In the inner ramp, the most abundant lithofacies are medium-grained wackestone–packstone with imperforated foraminifera. The middle ramp is represented by packstone–grainstone to floatstone. The outer ramp is dominated by argillaceous wackestone (Vaziri-Moghaddam et al., 2010). As shown in Figure 1, both of the studied fields are mainly influenced by a thrust fault in the southern flank and several strike slip faults. These active faults have complicated structural analysis of the studied fields. For example, in some wells the direction of maximum stress is different to general trend of maximum horizontal stress in the Zagros (NE-SW). The direction of maximum stress has been determined by strike of Drilling Induced Fractures (DIF) detected using image logs (Introduced method by Tingay et al., (2008)) for studied wells.
19
3. Methods and Material
20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Electrical (FMI, EMI and OBMI) and sonic (UBI) image logs integrated with conventional well logs (Sonic, Resistivities, Neutron Porosity, Density and Gamma ray) of 6 wells from Asmari Formation were interpreted for fracture identification and parameters calculation. For more accuracy, non-reservoir intervals such as shale and anhydrite were eliminated due to their flexibility and plasticity. At first, all open fractures were hand-picked on the image logs and their related parameters i.e. density and aperture, were calculated. Then, the effect of fracture parameters were evaluated on the conventional logs, especially sonic and resistivity logs. To improve the results, neutron porosity and gamma ray logs combined with available data as complimentary methods. Finally, regarding to the effect of fracture parameters on these conventional logs, a valid method was introduced for identification of fractures in the wells without core and image logs. Figure 2 illustrates the designed flowchart of the study. Between available image logs, only FMI and EMI are capable to measure all fracture parameters. OBMI (Oil Base Mud Imager) and UBI (Ultrasonic Borehole Imager), which usually applied in the OBM systems simultaneously, have less quality and merely used for detection of fractures, not fractures parameters. However, in the high pressure and temperature reservoir, application of oil based muds is inevitable to decrease the drilling risks and increase efficiency (Chen and Wang, 2017). So, a combination of these image logs with conventional logs is the best method for eliminating the main weak points of applied image logs in the OBM systems for fracture parameters detection. In addition, a combination of sonic, resistivity, porosity and gamma conventional logs may help petrophysicists and geologists to determine fractured zones and fracture parameters in the wells with no image logs and core data. 3
1
4. Results and Discussion
2 3 4 5 6 7 8 9 10 11 12 13
After analysis of electrical and sonic image logs in the 6 studied wells, several features such as fracture, bedding, induced fracture, borehole breakout and stylolite were identified. Considering to the importance of open fractures due to the aim of this study, all fracture parameters precisely calculated for these fractures in the WBM (water base mud) wells. Also, induced fractures and borehole breakouts were used to identify in situ stress direction in the studied fields (Fig. 1). It is also noteworthy that, between all fracture parameters; porosity, density and aperture are known as the most effective factors on the reservoir properties (Aghli et al., 2017). However, fracture porosity is too small value and only measurable using some electrical image logs such as FMI and EMI (Zerrouki et al., 2014). Due to its small values, the effect of fracture porosity is not detectable on the conventional logs, while fracture aperture may be considered as a dependable proof for fracture porosity. Results for identification of fracture parameters using conventional logs are presented in the following sections:
14
4.1. Determination of fractures and their parameters using image logs
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
The main aim of this research is to identify the effect of fracture parameters on the conventional logs to find a reliable way for determination of fracture parameters by conventional logs in the wells with no core and image logs data. Therefore, open fractures and their parameters should be precisely determined at first and some electrical image logs considered as the best tools for this purpose. Electrical image logs, i.e. FMI and EMI, are able to measure fracture aperture and fracture porosity as well as fracture density. Detection of fractures aperture is known as the biggest challenge for electrical image logs, while sonic image logs are not able to measure this parameter (Crain and Eng, 2006). Inability of sonic image logs for fracture aperture detection as well as their low resolution than electrical image logs heavily decreases their efficiency. However, as mentioned above, in the high pressure reservoirs, OBM drilling is better and safer than WBM. Therefore, in this situation, usually sonic and low quality electrical image logs such as OBMI-UBI have been run. So, it is necessary to find a method to cover the weak point of OBM image logs and detect fracture aperture in these wells. On the image logs, fractures are displayed as sinusoid features with dip more than structural dip (Schlumberger, 2003). Open fractures represent a conductive appearance on the electrical images, because their aperture is filled with the conductive drilling mud (in the WBM systems). However, the appearance for closed or filled fractures, if the filling materials be dense like calcite or anhydrite, is resistant (Serra, 1989). After hand-picking of all open fractures in the studied wells by FMI and EMI Image logs, fracture aperture was calculated using Luthi and Souhaite (1990) equation.
37 38 39
1. where ‘W’ is fracture aperture (mm), ‘A’ is an access flow of each electrode, ‘Rm’ is the
ܹ = ܿ. ܣ. ܴ݉ . ܴݔଵି resistivity of drilling-mud (ohm), ‘Rxo’ indicates the resistivity of the invaded zone (ohm), c and b are constants, based on the tool and environmental properties. In general, 4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
ܣrepresents the additional current which may be injected through the formation divided by the voltage, integrated along a line perpendicular across the fracture trace. Parameter c and b are obtained from forward modeling numerically and their values are related to exact image tool feature. Coefficient c is related to a geometric factor, integrating the measured resistance to the actual formation resistivity (Rxo). Exponent b indicates the sensitivity of A to the resistivity contrast between formation and borehole mud and its value is primarily influenced by the amount of current focusing as a function of the resistivity contrast and the borehole diameter. In order to focus equally, b is assumed as b in all resistivity regimes and borehole sizes. Since the exponent b was found to be relatively close to 1, the added conductance A is primarily dominated by the aperture and the fluid in the fracture, and to a less degree by the resistivity of the formation in which the fracture occurs. Figure 3 shows the results of electrical image logs for determining of open fractures in some studied wells. The results of image logs entirely confirmed that the Asmari Formation in both studied fields is completely fractured with structural evidences. All detected fractures in Figure 3 are open and show very high aperture in some intervals. It is also noteworthy that hand-picking of fractures heavily increases the accuracy of results rather than automatic fracture detection methods. Systematic fracture analysis for studied wells indicated that open fractures can be classified in three groups. Longitudinal, oblique and transverse sets which longitudinal fractures are dominant and mainly open with high aperture (Fig. 4). Longitudinal fractures generally related to the folding and tensional systems (Fossen, 2010).
22
4.2. The response of open fractures on the sonic logs
23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
There are many published literature about the propagation of acoustic waves in the fractured rocks and effect of fractures on their pattern (Anselmetti and Eberli, 1999; Brie et al., 1985; Raymer et al., 1980). Unfortunately, most of these studies are theoretical and are not supported by experimental and real data and only discussed the effect of fracture density among all fracture parameters. However, the sonic logs are the best fracture detector in older wells due to the unavailability of new and more modern methods at the time. Moreover, these logs are a very valuable method in the new drilled wells due to the weak points of image logs and core data. Sonic log (DT) is a log that computes the transit time in the formations. Many equations and relationships have been introduced between travel time and porosity system which Wyllie equation is the most common one (Wyllie et al., 1956). DT only indicates the values of P-wave, therefore sonic waveforms which include shear, compressional and stoneley waves, would be very useful methods for fractures detection as well (Chen and Wang, 2017). Undoubtedly, presence of open fractures heavily reduce the formation compaction and density and increase the sonic transit time, especially those with high aperture (Aghli et al., 2016). Also, this decrease is possible due to the low sonic waves velocity in fluids –open fractures are main pathway for fluids- compared to rock matrixes (Tokhmechi et al., 2009). Moreover, cycle skipping is a valid indicator for presence of fractures. The DT shows the first arriving sonic wave (compressional or P wave), and the mentioned cycle skipping may happen where the open fractures are presence and attenuation is unusually high. This attenuation is completely clear in the trend of DT in the 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
studied wells, particularly in the zones with highest aperture (Fig. 5). Generally, DT shows more relation with fracture density than fracture aperture which is expectable, because sonic waves choose the easiest way for transit and this is independent from the value of fracture opening. Therefore, DT and other sonic logs are reliable methods for determination of the zone with more fracture density (Please see the caption of Figure 5 for more explanations) As mentioned above, full sonic waveforms are beneficial for fractures determination as well as DT. On the array sonic log, travel time and attenuation of the shear, compressional and Stoneley wave energies are measurable, in comparison to conventional DT which measures merely compressional wave slowness (Sibbit and Faivre, 1985). Theoretically, open fractures effect on the compressional transit time less than shear log as long as a free matrix path is present between receivers and transmitter. In contrast, energy of shear wave is, in theory, rapidly decreased by both vertical and horizontal fractures as shown clearly on Figure 6 (Crain and Eng, 2006). Practically, shear waves are not conductible through of fluids and open fractures which filled by fluids, hence fractured zones heavily attenuate the shear arrivals (Laongsakul and Dürrast, 2011). The results of this study show that the amplitude of Stoneley wave is low in the fractured zones (Fig. 6). Actually, compressional waves travel faster than shear and stoneley waves and theoretically separate on a sonic waveforms plot. The parameters that affected on the amplitude of all waves include the porosity, formation fluid, borehole rugosity, rock type, tool centralization and fracture size and fracture orientation. Hence, energy depending on many parameters, so absolute values mean little, while low values of amplitude often show fractures in the carbonate reservoirs. The effect of fractures dip on the conventional logs has been discussed by Aguilera (2010), however most of fractures in this study have dip more than 50 degree (Fig. 4) and nearly create same response on the logs.
25
4.3. The response of open fractures on the resistivity logs
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Generally, all transmitters and receivers type instruments (i.e. sonic and resistivity tools) are pad contact tools that usually detect fractures successfully and better than other tool types. Hence, if the borehole is normal due to the fractures and washouts, most of them will be seen because they are considered as a cause of anisotropy in the reservoirs. Moreover, these types of devices are useful to identify the parameters of fractures, even though qualitatively. In recent studies, resistivity logs are widely used for fractures studying. Because they have higher resolution than other conventional logs (Lyu et al., 2016; Saboorian-Jooybari et al., 2015). Also, resistivity tools record a set of logs with different depth of investigations (DOI) which include shallow and deep logs and provide better data set for fractures evaluation. Generally, if the resistivity of mud (Rm) is lower than the formation resistivity (Rt), as it is true for all WBM systems, then the shallow resistivity usually read lower resistivity and cross over the deep resistivity in the fractured zones because of more invasion in these intervals (Fig. 7). The separation between deep and shallow resistivities can be used for quantification of fractures in the fractured reservoirs. In the WBM systems, where fractures are open, shallow and deep resistivity logs show same records. This is expectable due to invasion of mud through open 6
1 2 3 4 5 6 7 8 9 10 11
fractures, particularly where open fractures made a fracture system. This character is clearly observable in the Figures 7 and 8, where both of deep and shallow resistivity logs mostly show values less than 100Ω. Statistical parameters for shallow and deep resistivity logs may be useful for better evaluation of fracture response on the theses logs (Table 1). For example, maximum, minimum and median of deep resistivity values in the non-fractured zones for well A are 16784, 1.6 and 34 Ω respectively and for fractured zones are 305, 1.6 and 13. These parameters on the shallow resistivity log in this well are 166, 1.5 and 23 in the non-fractured zones and 121, 1.5 and 10 in the fractured zones. These statistical parameters help to find that the values of shallow and deep resistivity logs quickly decreased in the fractured zones especially those with high aperture. Also, they show more same records in the fractured intervals which increase their liner regression (represented in the figure 7) in these intervals (table 1).
12
4.4. Other important conventional logs for fractures evaluation
13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
During our study, we found that there is an interesting relationship between fracture parameters and porosity logs, especially neutron porosity (NPHI). Other conventional logs such as sonic, density and resistivity logs have shown more relation with fracture density, while NPHI log was completely a function of fracture aperture. The wells in this research were selected from an entirely fractured reservoir with active tectonic in the SW Iran. Hence, all detected fractures by image logs are mainly open with medium to high aperture (0.1mm). Therefore, open fractures filled by high hydrogen contents (hydrocarbons and formation water) heavily affect NPHI values. Figure 9 shows the sensitivity of NPHI log to the values of fracture aperture in the studied wells. So, NPHI log and its combination with sonic logs is the best method to detect open fractures. Also, in some cases, the separation between SGR and CGR gamma ray logs is a quick qualitative indicator for fractured zones detection to the presence of uranium in the reservoir fluid flow in open fractures, particularly in the dolomitic zones (Fig. 6). Regarding to the Figure 6, among carbonates, dolomite has highest potential for presence of uranium as well as occurrence of fractures.
28
5. Evaluation of the results in an oil base mud well (well E)
29 30 31 32 33 34 35 36 37 38 39
As previously mentioned, image logs for OBM systems, have usually low quality and reliability than those for WBM systems. Furthermore, OBM image tools don’t have ability to measure fractures and reservoir parameters, especially fracture porosity and aperture. This shortcoming considered as the main weak point of OBM image logs. In this study, well E is chosen for evaluating the results of this research as an OBM well with OBMI-UBI image logs where only fracture density was determinable using these image logs. So, we tried to evaluate the reliability of detected fracture density and determine the opening of fractures in this well using a combination of sonic waveforms and gamma ray logs (Fig. 6), NPHI porosity and DT logs (Fig. 10-a) and resistivity logs (Fig. 10-b). Therefore, evaluation of fracture parameters in this well is explained below in the five detected zones in Figure 10. Zone 1: based on the separation between SGR and CGR (this well located in the same field as wells A and D and this separation is mainly 7
1 2 3 4 5 6 7 8 9 10 11 12 13 14
observed at the top of Asmari Formation), fluctuation of the waveforms (Fig. 6), trend of DT and NPHI (Fig. 10-a) and propinquity of deep and shallow resistivity logs (Fig. 10-b), this zone is completely fractured with high aperture (dolomitic zone). Zone2: DT and waveforms logs show that this interval has a high fracture density potential, however NPHI and resistivity logs indicate that these fractures would be shown low aperture (calcitic zone). Zone 3: the values of all conventional logs for this interval confirm that the real fracture density in this zone is much more than detected density by image log and these fractures are potentially open (mostly dolomitic). Zone 4: this zone is a fractured zone with very low aperture (calcitic zone). Zone 5: the separation between SGR and CGR again observed in this zone and irregularity in the trend of other logs confirm that this zone is a fractured zone with good to high aperture (dolomitic zone). Generally, results indicate that the measured fracture density for this well is much less than the real values (in comparison with other wells), due to the low quality of OBMI-UBI image logs. Also, most of detected fractures, particularly in the dolomitic zones, are open and their effect on the conventional logs is obviously detectable.
15
6. Conclusion
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Between direct and indirect methods of fractures evaluation, image log interpretation is the best and reliable choice. Nevertheless, image logs are known as an expensive and new method which practically available in a few drilled wells. Furthermore, designed image logs for oil base mud wells e.g. OBMI-UBI, have poor accuracy and ability to detect fractures and measuring their parameters. However, in the heterogeneous carbonate reservoirs, the effect of fractures on the reservoir quality should be evaluated and for this reason, measuring of fracture parameters, especially aperture is necessary. On the other hand, identification of fracture opening is more important than other parameters in the fractured reservoirs. In the wells with no image logs and core, a combination of conventional logs –not a specific log- is the paramount way in the detection of fracture parameters. Particularly array logs which come from a set of one transmitter and several receivers such as some sonic and resistivity tools. Also, this combination is applicable for OBM systems where designed image logs are not able to measure fracture aperture. Sonic transit times, waveforms and resistivity logs can precisely determine fractured zones. Moreover, their combination with NPHI porosity and gamma ray logs clearly increase the reliability of their results, especially for identification of fracture aperture. Among conventional logs, neutron porosity shows the highest relevance with fracture aperture due to the concentration of high hydrogen content fluids in the open fractures. It seems that sonic transit time and all waveforms have depended on the fracture density more than fracture aperture due to their moving traits. However, sudden decreases on the resistivity logs in the fractured zones was observed and it is clearly visible that the values of shallow and deep resistivity logs approach to each other (less than 100Ω in this case) on their cross plot. Also, their behavior shows a relative relationship with fracture aperture due to the concentration of less resistive fluids in the open fractures. Finally, results indicate that detected fracture density by OBMI-UBI image logs in an OBM well is less than the real value because of their lower quality than WBM image tools. Also, combination of all studied conventional logs successfully identifies the zones with highest 8
1 2 3
fracture aperture in this OBM well. The results of our study confirmed that fractures concentration in the dolomitic zones is more than the limestone zones and they also showed more aperture as well.
4 5
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Table 1. Statistical parameters for resistivity logs of two WBM studied wells A and B. Results indicate that, both deep and shallow resistivity logs represent a great decrease in the fractured zones and their values are more same in these zones. Well A
Fractured zone RLA2
Maximum Median Minimum
Well B
121 10 1.5
305 13 1.6
Fractured zone RLA2
Maximum Median Minimum
RLA5
913 54 8
Non-fractured zone RLA2
RLA5
166 23 1.5
16782 34 1.6
Non-fractured zone
RLA5
RLA2
RLA5
6790 88 8
2000 111 8
24410 266 11
C
B A
D
Figure 1: Location map of the Zagros Basin and UGC map of studied wells. Location of Dezful Embayment in the SW Iran (A), location of studied fields in the Dezful Embayment (B), UGC map of studied fields with their active faults (C and D). Rose diagrams show the strike of induced fractures in the location of studied wells, which they indicate the direction of maximum stress. The direction of maximum stress in some wells is not compatible with Zagros trend (NE-SW).
Figure 2: Flow chart for evaluation of sonic and resistivity conventional logs for fractures parameters identification, designed in this study.
Well A
Well B
Well C
Well D
Figure 3: Detected open fractures and their parameters in the 4 of studied wells with EMI and FMI image logs. Well A with 650, well B 1490, well C 550 and well D by 300 open fractures are completely fractured. Track 1 is depth per meters, track 2 shows fractures tadpoles, track 3 shows fracture logs (Blue fracture density (between 0 to 60) and Red fracture aperture (less than 0.5 cm)) and DT log (green).
Figure 4: Display of histogram and strike of open fractures (blue points) and bedding (green points) in the some studied wells. This is clear that longitudinal fractures (Red circles) are dominant and show same strike with bedding. These fractures considers as folding related fractures and tensional systems.
Well A
Well C
Well B
Well D
2800
1650 2400
1550
2750
1700 2450
1600
1750
2700
2500
1650
1800
2650
2550
1700
1850 2600
2600
1750
1900 2550
2650
1800
1950
50
60
10
20
0.02
0.06
60
80
20
40
0.1
0.2
50
60
6
14
0.1
0.2
50
70
2
8
0.01
Figure 5: The effect of fracture density and fracture aperture on the DT log for studied wells. For each well, first log is DT (µs/f), second log is fracture density (1/m) and third one is fracture aperture (mm). In the well A, DT shows fluctuation in the depth 1620 to 1700 and 1800 to 1850 which these intervals are the main fractured zones. With same explanation, intervals 2400 to 2450 and 2500 to 2600 in well B, 2575 to 2675 and 2700 to 2750 in well C, 1530 to 1650 and 1700 to 1750 in well D. It seems that, the trend of DT is a function of fracture density and, accordingly fracture aperture shows less relation with this log.
0.02
Well A
Well B
Well E
Figure 6: Display of waveforms and transit times in some studied wells. It is clear that waveforms show an irregularity in the fractured intervals, especially zones with high fracture density (FVDC) and aperture (FVAH). This fluctuation is visible on the stoneley, shear and compressional waves. Also, the separation between SGR and CGR logs is consequential and remarkable in some fractured intervals (not all zones). For example, in the well A, separation between SGR and CGR is clear in the depth 1622 to 1700, also the fluctuation of waveforms are obvious in this interval and 1800 to 1850. Same events for well B observed in the depths 2360 to 2420 and 2500 to 2600. For well E, 1500 to 1620 and 1650 to 1750. Well E is an OBM well with OBMI-UBI image logs which fracture aperture has not been measured using these tools and it seems that the detected fracture density is less than the real value, due to the low quality of this tools, particularly for zone 1500 to 1620 (this well located in the same field with well A). Furthermore, in the three wells, dolomitic zones show more values for both fracture density and fracture aperture than calcitic zones.
1000
10000
Well A
Well B
RLA5
RLA5
1000
100
100
10
Non-fractured zones Fractured zones
Non-fractured zones Fractured zones 10
10
100
RLA2
10
100
1000
RLA2
Figure 7: The cross plot between shallow (RLA2) and deep resistivity (RLA5) logs in the studied wells A and B. Fracture response on the resistivity logs is usually less than 100Ω for both shallow and deep logs. Also, these resistivity logs show more linear regression (same records) in the fractured zones.
1000
Well B
Resistivity
RLA2 RLA3 RLA4 RLA5
100
10
1 2400
2450
2500
2550
Well A
2650
RLA2 RLA3 RLA4 RLA5
10000
Resistivity
2600
Depth
1000
100
10 1650
1700
1750
1800
1850
1900
Depth
Figure 8: Schematic of resistivity logs (Ω) for wells A and B per depth (meter). Deep (RLA5) and shallow (RLA2) resistivity logs show a same values in the fractured intervals (1600 to 1700 and 1800 to 1850 in the well A and 1500 to 1600 in the well B) in contrast of other zones. The presence of separation between these logs shows non-fractured intervals or very low aperture fractured zones. Fracture parameters for these wells presented in the figure 3.
1950
Well A
Well B
Well D
Well F 2350
1650 2400
1600
1700
2400 2450
1750
2450
1800
D e p th
2500
1800
2550
2500
2600
2550
1850
2000
1900
2650
2600 1950
0.05
0.2
10
20
0.02 0.06
0.05
0.2
2
8
0.01
0.02
0.1
0.3
20
40
0.1 0.2
0.1
0.3
2
8
0.02
0.08
Figure 9: The effect of fracture density and fracture aperture on the NPHI log for studied wells. For each well, firs log is NPHI (fraction), second log is fracture density (1/m) and third one is fracture aperture (mm). It is clear that NPHI shows more relation with fracture aperture than fracture density. For example, in well A, fracture density in depth 1800 to 1850 is more than 1620 to 1700, while NPHI indicates more values in depth 1620 to 1700 as well as fracture aperture. This explanation is also extensible for well D in depth 1520 to 1600, well B in depth 2500 to 2600 and initial depths of well F.
a DT
NPHI
FVDC
1550
Zone 1
1600
Depth
Zone 2 1650
Zone 3
1700
Zone 4
Zone 5 1750
45
50
55
60
65
700.000.050.100.150.200.250.300.35 0
10
20
30
40
b AE20 AE30 AE60 AE90
Zone 1
Resistivity
1000
Zone 4
100
Zone 2
Zone 5
Zone 3
10
1 1550
1600
1650
1700
1750
Depth
Figure 10: Display of conventional logs for well E, NPHI (fraction) and DT (us/f) and fracture density (1/m) log (FVDC) (figure a) and resistivity (Ω) logs (figure b). Regarding to the trend of conventional logs, five zones with different properties were detected on the conventional logs. Results of conventional logs indicated that zones 1 and 3 are the main fractured zones in this well which it seems that detected fracture density by OBMI-UBI image logs is less than the real fracture density for these zones. High values of NPHI and DT, trend of resistivity logs and High separation for SGR and CGR logs (presented in the figure 6) indicate that these zones are main fractured zones in this well with high aperture.
Integration of Sonic and Resistivity Conventional Logs for Identification of Fracture Parameters in the Carbonate Reservoirs (A case study, carbonate Asmari Formation, Zagros Basin, SW Iran)
Highlights: 1. Conventional logs are used for fractures evaluation as an economical and available data in all wells. 2. Fracture aperture, as the most effective fracture parameter on the reservoir properties, is determined using conventional logs. 3. The presented approach can be applied in Oil Based Mud image logs for determination of fracture aperture.
Declaration of interests ☐ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: