Integration of Static and Dynamic Data for Enhanced

Integration of Static and Dynamic Data for Enhanced Reservoir Characterization, Geological Modeling and Well Performance Studies Authors: Dr. Shouxiang M. Ma, Dr. Murat M. Zeybek and Dr. Fikri J. Kuchuk

ABSTRACT A new methodology is presented for reservoir characterization, geological modeling and well performance prediction by integrating a complete suite of petrophysical and pressure transient test data to build a detailed geological reservoir model (RM) with anisotropy. Data used include cores, open hole logs, wireline formation testing (WFT) pretests, vertical interference tests (VITs), production logs, and downhole pressure buildup and injection falloff tests. Core data were first integrated with open hole logs and WFT pretests to build a detailed geological model. Vertical and horizontal permeabilities derived from the VITs were then integrated to produce a geological model with anisotropy. Using this model, a numerical pressure transient analysis (PTA) for a single well was performed by simultaneously history matching the packer’s and the probe’s pressures, as well as pressure derivatives, to identify the presence of tight reservoir streaks and to quantify reservoir layer permeability ranges. The model was further refined and validated by comparisons with dynamic data derived from production logs, and downhole pressure buildup and injection falloff tests. This validated RM was used in single well reservoir simulation studies to predict well performance and infer in situ reservoir scale and reservoir condition petrophysical properties, such as relative permeability and capillary pressure.

INTRODUCTION Most carbonate reservoirs are layered and heterogeneous. The lithology (lith) and porosity ( ), derived from cores and logs, of a typical Arab-D carbonate reservoir are shown in Figs. 1a and 1b, respectively. Characterizing reservoir layering and heterogeneity is essential in reservoir engineering. For example, when addressing oil recovery by waterflood, the following equation is often referred to: E = EAEVEM

(1)

where E is oil recovery efficiency, subscript A is areal sweep efficiency (the ratio of area swept to total field area), subscript V is vertical conformance (the ratio of intervals swept to total pay thickness), and subscript M is the microscopic displacement 62

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Fig. 1a. Typical Arab-D carbonate reservoir volumetrics (blue is limestone, green is dolostone, and pink is anhydrite). Fig. 1b. Comparison between log and core porosities. Fig. 1c. Schematic of a WFT and VIT setup showing a packer-probe configuration applied to a section having four layers, including a low permeability streak.

efficiency (defined as the saturation change with respect to original oil saturation in the swept volume). Note that even though many factors (including pore structure and wettability) may affect E microscopically, it is the areal sweep efficiency and vertical conformance that dominate the efficiency of oil recovery. Consequently, detailed reservoir characterization is critical for better reservoir management. Petrophysical reservoir characterization consists of data acquisition, data processing and data distribution in space, or modeling. Petrophysical data in reservoir characterization usually include lith,  , water saturation (Sw), zone thickness (h) and permeability (k), with k being the most challenging to characterize, especially for carbonates due to the heterogeneous pore structure caused by depositional environments and diagenesis (such as dolomitization, compaction, cementation and/or fracturing). The most commonly used techniques for in situ reservoir permeability characterization are based on pressure transient analysis (PTA); either wireline formation testing (WFT) with measurements typically ranging from 10 ft to 50 ft away from the well, depending on formation properties and duration of production and buildup periods, or conventional well testing, with a depth of investigation ranging from hundreds to thousands SAUDI ARAMCO JOURNAL OF TECHNOLOGY

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of feet1, 2. It is a common understanding for almost all petrophysical measurements that the greater the depth of the investigation, the poorer the vertical resolution. The WFT has much better vertical resolution than conventional well tests. There are basically two modes in WFT for estimating reservoir permeability: a pretest with probes and a vertical interference test (VIT) with a combination of packers and probes, Fig. 1c. A pretest requires a drawdown volume of less than 20 cm3 of fluid, most likely mud filtrate. As a result, mobility estimated from a WFT pretest is a near wellbore mobility indicator; at remaining oil saturation (ROS) if water-based mud were used across an oil interval. On the other hand, during a VIT, hundreds of liters of reservoir fluid are pumped out (at a rate of 1 to 30 barrels per day (BPD) for up to 1 hour), providing a reservoir permeability estimation up to 50 ft into the reservoir, which is certainly much more representative of reservoir permeability (at connate Sw if measured across a pay zone). In addition, unlike other reservoir petrophysical properties mentioned above, permeability is directional. Currently, the only techniques that are used routinely for directional reservoir permeability characterization are based on PTA, such as a VIT. Details of a nonlinear regression analysis of VIT PTA data for determining formation parameters are given by Onur and Kuchuk (2000)3. The main objective of this article is to introduce a methodology to integrate static and dynamic petrophysical data to build a comprehensive reservoir model (RM) for reservoir characterization, geological modeling and well performance prediction. Results reported in this article are part of a larger project, and some of the details of the project have been published previously4, 5.

METHODOLOGY Petrophysical properties derived from open hole logs and WFT are calibrated with core analysis data before being distributed in space to build a geological model. The established model can be verified from borehole fluid flow profiles measured by a production log, as shown in Eqn. 2, even though layers with no flow or a low flow rate due to skin, low permeability or low pressure may not be detectable by a production log:

(


n i=1

)

kihi

Core, OH Logs,WFT

(


=

n i=1

)

kihi

PL

(2)

The cumulative of the borehole flow profiles can be calibrated from the total kavgH determined from a well test:

(


n i=1

) =(k H)

ki hi

PL

avg

WT

(3)

In Eqns. 2 and 3, H is the total reservoir thickness, h is the individual layer thickness, n is the total number of reservoir layers, subscript avg is the average of all layers, and subscript i is the ith reservoir layer. Details of the methodology introduced in this study for sin-

Fig. 2. Methodology for reservoir characterization, reservoir modeling and well performance prediction.

gle well data integration, reservoir characterization, reservoir modeling and well performance prediction are summarized below and illustrated in Fig. 2. 1. Data Preparation and Integration: • Core data are reviewed and quality controlled for geological features (such as depositional environments and layering), lith, pore types,  , k and grain density. • Open hole logs are reviewed, quality controlled, processed and interpreted for lith,  , grain density, Sw, zoning and zone thickness (h). • WFT pretest data are reviewed, quality controlled and processed for estimating mobility, then for qualitatively determining k. • Together with other geological information, the above core data, open hole logs and WFT pretests are integrated for a foot-by-foot formation evaluation and reservoir characterization. 2. Geological Model: • A layered, single well geological model is generated from the above detailed formation evaluation and reservoir characterization. • WFT and VIT data are analyzed to quantify vertical and SAUDI ARAMCO JOURNAL OF TECHNOLOGY

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horizontal permeabilities (kv and kh) for the layers selected for the VIT. • The geological model is updated with kv and kh determined from analyses of all VITs. • This layered anisotropic geological model is fine-tuned by integrating geological features and the range of permeabilities obtained from performing a single well numerical PTA, with the pressure and pressure derivatives as the history matching parameters, for each VIT. 3. Reservoir Model: • A RM is established by validating, iteratively, the finetuned geological model with
kh from a production log and the total KavgH from downhole pressure buildup and falloff tests, as shown in Eqns. 2 and 3. 4. Use of the RM: • By history matching downhole pressure and flow rate, the RM can be used in a single well reservoir simulation for well performance prediction or in any other reservoir characterization and management studies4, 5.

TEST OF THE METHODOLOGY IN A STUDY WELL The above methodology was developed in a joint research project between Saudi Aramco and Schlumberger, and some of the results of the project have been published4, 5. In this article, the focus will be on the methodology of integrating static and dynamic data for reservoir characterization and modeling. In the process, it will be demonstrated that the VIT is an extremely powerful tool for characterizing reservoir heterogeneity1, 6-8. Data Acquisition

As previously reported4, 5, a research well, Well-A, was drilled in 2001 across the Arab-D carbonate reservoir, and a complete set of petrophysical data was acquired in the following order: 1. Cores, open hole logs and WFT: • Conventional cores were taken from the top 250 ft of the target reservoir. Core description, petrographics, and routine and special core analyses were performed on selected core samples. • Open hole logs acquired included caliper, spectral gamma ray, bulk density, thermal neutron porosity, sonic, array induction resistivity, micro resistivity, resistivity imaging, mineralogy and nuclear magnetic resonance tests. • A total of 25 WFT pretests and eight VITs were conducted. 2. Baseline production log. Following completion, the well was allowed to produce oil for one day to clean out mud 64

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invasion, and then the baseline flow profile was established from the production log. 3. Baseline buildup tests for KavgH at connate water saturation (Swc): • The well was then shut-in to perform a buildup test for total KavgH (at Swc) by using the downhole permanent pressure gauge located just above the top of the tested zone. • After producing the well for a while, another pressure buildup test was performed immediately before water injection to confirm the determined KavgH (at Swc). 4. Water injection tests: • A stepwise rate change was applied. Each injection rate usually lasted 3 to 5 hours, depending on the time required for the electrode resistivity array measurement4 and production log measurements. • The initial injection rate was 1,000 BPD. With an incremental of about 1,000 BPD, the final rate reached 8,200 BPD at the end of the eighth test. • A production log was run to obtain the injection profile during each test. 5. Falloff test for KavgH at ROS. • The well was then shut-in for a falloff test to determine the total KavgH at ROS and skin. 6. Final buildup test for KavgH at reduced Sw: • All of the injected water and some oil were produced back to the surface with a nitrogen lift for 14 days. • During this water and oil production period, a production log was run frequently to monitor fluids produced. • After the well stopped producing water, the well was shut-in for a final pressure buildup test to estimate KavgH at a reduced Sw close to, but usually larger than, the original Swc. Data Processing and Interpetation Core Data. Core description and petrographic analysis were conducted to extract information on reservoir depositional environment and rock typing, and to identify reservoir layers; an example of this analysis is shown in Fig. 3. Conventional core analysis under stress, Figs. 3a and 3b, on selected core samples was performed to provide data for log calibration and reservoir layering(1). On a subset of cores, adjacent twin plugs were taken, one horizontally and another vertically, for kh and kv (1) In using core data to calibrate logs and/or well tests, it is noted that core data may not be representative in very high and very low permeability rocks9. For rocks with very high permeabilities (such as measures in Darcies), cores may not be available or pluggable due to their weak mechanical integrity. On the other hand, conventional laboratory measurements on very low permeability rocks (such as measures in less than milli-Darcies) have large uncertainties.

Fig. 3. Example of core analysis data and associated core descriptions and petrographics.

measurements, Fig. 3b. From Fig. 3, the following are observed: 1. Correlations between permeability and  are strongly de pendent on rock type. 2. The difference between kh and kv is not obvious at the core plug scale. This may be attributed to the following: • Laboratory permeability measurements have relative large uncertainties, so the difference between kh and kv is probably within permeability measurement uncertainties. • To ensure a plug’s mechanical integrity, samples are typically taken in more homogeneous sections, where rock anisotropy is less. • Even though small-scale rock anisotropy can be observed, for example, in thin sections, it is probably true that the larger the scale, the more obvious the rock anisotropy. Open Hole Logs. As previously mentioned, a complete suite of open hole logs was run. These logs were quality controlled, processed and interpreted for lith,  and Sw. Correlations were also used to qualitatively predict reservoir permeability. Use of the processed logs and core data in geological modeling has been previously described5. WFT Pretests and VITs. As summarized in Fig. 2 describing a geological model built with geological and petrophysical data, 25 pretests were performed using probe 1, Fig. 1c, for formation pressure profiling. Eight VITs were conducted with a configuration of a dual packer and two observation probes, probes 1 and 2, as shown in Fig. 1c; 13 additional pretests were also performed using both probes during the VITs. A pump-out module was used for fluid withdrawal to create pressure transients in the formation, which were monitored by crystal quartz pressure gauges and strain gauges at the dual packer and observation probes. Figure 4 shows the acquired downhole data, including reservoir pressure (with an oil gradient of 0.32 psi/ft) from the probes, from the packer and during the interference

Fig. 4. Composite display of open hole logs, reservoir layering, WFT pressures, mobilities and VIT positions.

test (track 1); pretest drawdown mobilities (track 2); image log and the positions of the VITs (track 3); reservoir porosity (track 4); and formation resistivity (track 5). Reservoir porosity and formation resistivity data provide quantitative information for reservoir layering, while the image log is used to check the reservoir layering qualitatively. Pretest Applications. As shown in Fig. 4, pretest data can be processed for formation pressure and fluid mobilities. Formation pressure derived from the probe pretest is as accurate as that obtained from a packer test or a well test (track 1 of Fig. 4); therefore, it is routinely used for reservoir fluid typing, fluid contacts identification and free water level determination. On the other hand, the probe pretest drawdown mobility is rather qualitative, due to its small volume drawdown (typically 5 cm3 to 20 cm3). It has a shallow depth of investigation, and it is affected by formation damage in the invaded zone and by near wellbore, small scale heterogeneity. Because of the small volume drawdown, the mobility determined typically does not include anisotropy. Consequently, pretest drawdown mobility can only be qualitatively used for reservoir rock and fluid characterization. VIT Applications. As described in Fig. 2, VIT data can be processed for reservoir rock anisotropy assessment. This VIT data processing workflow is expanded in Fig. 5. To process the VIT data, a robust geological model is essential to match the packer’s and probe’s pressures and pressure derivatives with predicted kv and kh. This matching is not only for one VIT, but for all VITs, so an iterative process is necessary. In a heterogeneous reservoir, pressure changes at the observation probes, especially the one with the farthest spacing, may be very small. For a VIT to be successful in this situation, very high precision pressure gauges are required, Fig. 6. Besides kv SAUDI ARAMCO JOURNAL OF TECHNOLOGY

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Layer

Fig. 5. Workflow for VIT data processing in a layered reservoir.

Fig. 6. Example of precise probe pressure measurement during a VIT.

H

Poro

kh

kv

kh/kv

1

8

0.16

7.2

4.03

1.8

2

2

0.05

3.2

0.96

3.3

3

45

0.25

940

432.4

2.2

4

2

0.12

2

0.18

11.1

5

4

0.25

736

58.88

12.5

6

1

0.2

0.15

0.02

7.5

7

7

0.25

497

49.7

10

8

2

0.24

2.9

3.19

0.9

9

7

0.24

160

16

10

10

1

0.1

3

0.06

50

11

6

0.23

463

96.45

4.8

12

1

0.15

3.5

0.07

50

13

13

0.2

980

490

2

14

1.5

0.1

16.2

15.39

1.1

15

14.5

0.25

164

22.08

7.4

16

5

0.12

4.5

9

0.5

17

11

0.17

55

11

5

18

1

0.13

8

0.06

133.3

19

6

0.23

139

34.75

4

20

3

0.1

5

0.55

9.1

21

15

0.25

716

27.72

25.8

22

5

0.1

3

2.5

1.2

23

11

0.12

114

2.05

55.6

24

11

0.1

48

7.97

6

25

10

0.1

8

5.28

1.5

26

2

0.05

1

0.1

10

27

6

0.09

3

0.3

10

28

7

0.07

2

0.2

10

29

2

0.12

14

2.8

5

30

27

0.05

2

0.2

10

31

10

0.07

1

0.1

10

32

1

0.03

1

0.1

10

33

8

0.07

58

4.64

12.5

34

60

0.03

0.5

0.15

3.3

Table 1. Final layered reservoir model with anisotropy

F

Fig. 7. Use of VITs in reservoir fluid flow regime identification and reservoir heterogeneity characterization.

and kh determination as described in Fig. 5, VITs can also be useful in reservoir fluid flow regime identification and detailed reservoir heterogeneity characterization, as demonstrated in Fig. 7, by examining the pressure and its derivative vs. buildup time. From Figs. 6 and 7, the following can be summarized: • Probe pressure changes of 0.1 psi are clearly observed, repeatedly, with the high resolution crystal quartz gauges. 66

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• Measured packer and probe pressures are matched or reproduced satisfactorily with the geological model. • Fluid flow regimes are identified from features of buildup pressure derivatives. The identified flow regime is consistent with the geological model. Final RM

By integrating geological information with data derived from core description and core analysis, open hole logs, WFT

CONCLUSIONS From this article, the following are concluded: • At core plug scale, permeability anisotropy may not be observable. • A VIT with advanced WFT is a powerful tool for reservoir heterogeneity characterization. • To build a robust geological model, integration of all geological and petrophysical data is critical. Fig. 8. Reservoir model validation using kh from production logs (a) and KavgH from downhole pressure and pressure derivatives (b).

• To test the internal consistency of a built RM, numerical history matching of measured properties, such as pressure and its derivatives, is a proved best practice. • Total KavgH from a well test and ȃkh from a production log flow profile are useful in validating RMs. • A methodology is introduced that integrates static and dynamic petrophysical data for reservoir characterization, geological modeling and well performance studies.

ACKNOWLEDGMENTS

Fig. 9. History matching of downhole pressure and flow rate during injection and falloff4.

pretests and VITs, the RM in this study well was established, following the methodology of Fig. 2, as shown in Table 1. This model is considered accurate not only because it integrates all relevant data, but more importantly because it is internally consistent with VIT pressure and pressure derivatives. The established RM, Table 1, is further validated in terms of its production behaviors by comparing its data with the fluid flow profile derived from production logs and the total KavgH derived from numerical analyses of well test pressure as well as pressure derivative, Fig. 8. Results show that the model matches the well dynamic behavior very well, Fig. 8. Application of the Validated Geological Model

The RM, Table 1, can be used in well performance studies, as shown in Fig. 9, by matching and predicting the bottom-hole pressure and flow rate. It has also been used in this study well for identifying and characterizing reservoir heterogeneity, inverting reservoir scale and reservoir condition relative permeability and capillary pressure, assessing oil recovery by waterflooding, and monitoring water movement in situ in connection with measurements of a specially designed electrode resistivity array and permanent downhole pressure gauges4, 5.

The authors would like to thank the management of Saudi Aramco and Schlumberger for their permission to publish this article. This article was prepared for presentation at the SPE Annual Technical Conference and Exhibition, New Orleans, LA, September 30 - October 2, 2013.

REFERENCES 1. Ayan, C., Hafez, H., Hurst, S., Kuchuk, F.J., O’Callaghan, A., Peffer, J., et al.: “Characterizing Permeability with Formation Testers,” Oilfield Review, Vol. 13, No. 3, October 2001, pp. 2-23. 2. Kuchuk, F.J.: “Radius of Investigation for Reserve Estimation from Pressure Transient Well Tests,” SPE paper 120515, presented at the SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, March 15-18, 2009. 3. Onur, M. and Kuchuk, F.J.: “Nonlinear Regression Analysis of Well Test Pressure Data with Uncertain Variance,” SPE paper 62918, presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 1-4, 2000. 4. Kuchuk, F.J., Zhan, L., Ma, S.M., Al-Shahri, A.M., Ramakrishnan, T.S., Altundas, B., et al.: “Determination of In-Situ Two-Phase Flow Properties through Downhole Fluid Movement Monitoring,” SPE paper 116068, presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, September 21-24, 2008.

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5. Zhan, L., Kuchuk, F.J., Al-Shahri, A.S., Ma, S.M., Ramakrishnan, T.S., Altundas, B., et al.: “Characterization of Reservoir Heterogeneity through Fluid Movement Monitoring with Deep Electromagnetic and Pressure Measurements,” SPE Reservoir Evaluation & Engineering, Vol. 13, No. 3, June 2010, pp. 509-522. 6. Kuchuk, F.J.: “Pressure Behavior of the MDT Packer Module and DST in Crossflow-Multilayer Reservoirs,” Journal of Petroleum Science and Engineering, Vol. 11, No. 2, June 1994, pp. 123-135. 7. Kuchuk, F.J., Halford, F., Hafez, H. and Zeybek, M.: “The Use of Vertical Interference Testing to Improve Reservoir Characterization,” SPE paper 87236, presented at the Abu Dhabi International Petroleum Conference and Exhibition, Abu Dhabi, U.A.E., October 13-15, 2000. 8. Zeybek, M., Kuchuk, F.J. and Hafez, H.: “Fault and Fracture Characterization Using 3D Interval Pressure Transient Tests,” SPE paper 78506, presented at the Abu Dhabi International Petroleum Conference and Exhibition, Abu Dhabi, U.A.E., October 13-16, 2002. 9. Ma, S.M., Belowi, A., Pairoys, F. and Zoukani, A.: “Quality Assurance of Carbonate Rock Special Core Analysis — Lesson Learnt from a Multi-Year Research Project,” IPTC paper 16768, presented at the 6th International Petroleum Technology Conference, Beijing, China, March 26-28, 2013.

BIOGRAPHIES Dr. Shouxiang M. Ma is a Senior Petrophysical Consultant in the Reservoir Description Division and serves in the Petroleum Engineering Technologist Development Program actively as a mentor and a member of its Technical Review Committee. He member of the Upstream Professional was a founding mem Development Center as the petrophysics job family Professional Development Advisor from 2009 to 2012. Before joining Saudi Aramco in 2000, he worked as a Lecturer at Changjiang University, Jingzhou City, China, and as a Lab Petrophysicist at the New Mexico Petroleum Recovery Research Center, the Wyoming Western Research Institute and Exxon’s Production Research Company. Mark received his B.S. degree from China University of Petroleum, Beijing, China, and his M.S. and Ph.D. degrees from the New Mexico Institute of Mining and Technology, Socorro, NM, all in Petroleum Engineering. He is a member of the Society of Core Analysts and the Society of Petroleum Engineers (SPE), and served on the SPE’s Formation Evaluation Award Committee (as Chairman in 2012) and the AIME/SPE Robert Earll McConnell Award Committee. Mark has more than 60 publications and several patents in petrophysics. He was awarded the 2003 Department Individual Achievement Award and 2011 SPE Saudi Arabia Section Active Technical Involvement Award, and is a technical journal reviewer for SPE Reservoir Evaluation and Engineering (SPERE&E), Journal of Canadian Petroleum Technology (JCPT), Journal of Petroleum Science & Engineering (JPS&E) and the Arabian Journal for Science and Engineering. Dr. Murat M. Zeybek is a Schlumberger Reservoir Engineering Advisor and Reservoir and Production Domain Champion for the Middle East Region. He works on analysis interpretation of wireline formation testers, pressure transient analysis, numerical i l modeling d li of fluid flow, water control, production logging and reservoir monitoring. He is a technical review committee member for the Society of Petroleum Engineers (SPE) journal Reservoir Evaluation and Engineering. Murat also served as a committee member for the SPE Annual Technical Conference and Exhibition, 1999-2001. He has been a discussion leader and a committee member in a number of SPE Applied Technology Workshops (ATWs), including a technical committee member for the SPE Saudi Technical Symposium, and he is a global mentor in Schlumberger. Murat received his B.S. degree in Petroleum Engineering from the Technical University of Istanbul, Istanbul, Turkey. He received his M.S. degree in 1985 and his Ph.D. degree in 1991, both from the University of Southern California, Los Angeles, CA, also in Petroleum Engineering.

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He has published more than 50 papers on analysis/ interpretation of wireline formation testers, pressure transient analysis, numerical modeling of fluid flow, fluid flow porous media, water control, production logging and reservoir monitoring. Dr. Fikri J. Kuchuk, a Schlumberger Fellow, is currently Chief Reservoir Engineer for Schlumberger Testing Services. He was a consulting professor at the Petroleum Engineering Department of Stanford University from 1988 to 1994, teaching Advanced Well Testing. Before joining Schlumberger in Ad dW ll T ti 1982, Fikri worked on reservoir performance and management for BP/Sohio Petroleum Company. He is a Distinguished and Honorary Member of the Society of Petroleum Engineers (SPE), the Society for Industrial and Applied Mathematics, the Russian Academy of Natural Sciences and the American National Academy of Engineering. Fikri received the SPE 1994 Reservoir Engineering Award, the SPE 2000 Formation Evaluation Award and the SPE 2001 Regional Service Award; the Henri G. Doll Award in 1997 and 1999; and the Nobel Laureate Physicist Kapitsa Gold Medal. He has been very active in professional societies, serving as SPE International Director-at-Large and SPE Northern Emirates Section Director, and he is a member of the SPE Forum Series Implementation Committee, the Middle East Oil Show & Conference Executive and Program Committees, and many SPE award, editorial, membership and technical committees. Fikri has published and presented more than 150 papers on fluid flow in porous media, formation evaluation, pressure transient well testing, production logging, wireline formation testers, horizontal and multilateral well placement and performance, permanent reservoir monitoring, water conformance and control, and reservoir engineering and management. He received his B.S. and M.S. degrees from the Technical University of Istanbul, Istanbul, Turkey, and his M.S. and Ph.D. degrees from Stanford University, Palo Alto, CA, all in Petroleum Engineering.

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