Multiple-Point Geostatistical Lithofacies Simulation of Fluvial Sand-Rich Depositional Environment: A Case Study From Zubair Formation/South Rumaila Oil Field
- Watheq J. Al-Mudhafar (Louisiana State University and Basrah Oil Company)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- February 2018
- Document Type
- Journal Paper
- 39 - 53
- 2018.Society of Petroleum Engineers
- Fluvial Depositional Environement, South Rumaila Oil Field, Kernal Support Vector Machine, Spatial lithofacies posterior distribution, Multiple-Point Geostatistics
- 5 in the last 30 days
- 307 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
Modeling accurate geological structure is an important step in reservoir simulation. The multiple-point facies geostatistics (MPS) has been successfully used as an efficient approach to capture complex geological structures, in comparison with the sequential indicator simulation. Specifically, MPS creates the most realistic depositional environments because it mainly depends on using training images that reflect actual geological architecture, rather than variogram. Therefore, the MPS algorithm was adopted to model the 3D lithofacies distribution of the fluvial sand-rich depositional environment of the main pay in South Rumaila Oil Field in Iraq.
The fluvial depositional system was created through a 2D user-defined training image that was sampled to create a surface map. Then, a neural-networks algorithm was adopted to train the surface map to create the discrete template, which then was used with the 3D grid construction for pattern generation of 3D facies distribution. The resulting training image represents a numerical geological model that contains 3D information about the reservoir’s geological heterogeneity and structure. The facies pattern was then considered for 3D lithofacies modeling conditioning to upscaled discrete facies distribution for 19 wells in the reservoir.
The discrete lithofacies distributions of sand, shaly sand, and shale, obtained from core-analysis data, have been modeled as a function of well-log interpretations in one well and then were predicted for all 19 wells distributed around the reservoir crest. The trainingimage body and channels were pointed to the three aforementioned facies to create the 3D facies system. The resulting integrated model, through MPS, has much more reasonable facies architecture than indicator simulation because it is clearly noticeable how sand channels are distributed with continuous direction toward the sea shoreface. To validate our model and to capture the true depositional environment, many realizations were generated on the basis of the leave-one-out cross-validation technique. The sand-channels continuity was achieved through spatial posterior probability of lithofacies distribution that was incorporated in the MPS approach as a 3D trend model. However, including the geometrical trend models of thickness or depth has no effect on the channels continuity. The spatial posterior probability trends were modeled from the predicted probability distributions of lithofacies, which have been created by the kernel support vector machine (KSVM) for all 19 wells in the formation.
The resulting MPS model for the main pay of South Rumaila oil field corresponds to the description of the fluvial depositional environment described in the literature. This reflects how MPS is efficient to reconstruct complex geological reservoirs. In addition, it preserves the reservoir heterogeneity and connectivity of flow paths, and it provides a realistic reservoir description for petrophysicalproperty modeling and reservoir flow simulation.
|File Size||2 MB||Number of Pages||15|
Al-Ameri, T. K., Al-Khafaji, A. J., and Zumberge, J. 2009. Petroleum System Analysis of the Mishrif Reservoir in the Ratawi, Zubair, North and South Rumaila Oil Fields, Southern Iraq. GeoArabia 14 (4): 91–108.
Al-Ansari, R. 1993. The Petroleum Geology of the Upper Sandstone Member of the Zubair Formation in the Rumaila South. Geological Study, Ministry of Oil, Baghdad, Iraq.
Al-Mudhafar, W. J. and Mohamed, L. 2015. Incorporating Lithofacies Classification and Well Logs into Statistical Learning Algorithms for Comparative Multisource Permeability Modelling. Presented at the SPE North Africa Technical Conference and Exhibition, Cairo, Egypt, 14–16 September. SPE-175776-MS. https://doi.org/10.2118/175776-MS.
Al-Mudhafar, W. 2016. Statistical Reservoir Characterization, Simulation, and Optimization of Field Scale-Gas Assisted Gravity Drainage (GAGD) Process with Uncertainty Assessments. PhD dissertation. Louisiana State University (May 2016). http://digitalcommons.lsu.edu/gradschool_dissertations/3097/.
Al-Muhailan, M., Hussain, I., Maliekkal, H. et al. 2013. New HTHP Cutter Technology Coupled With FEA-Based Bit Selection System Improves ROP by 60% in Abrasive Zubair Formation. Presented at the International Petroleum Technology Conference, Beijing, 26–28 March. IPTC-17122-MS. https://doi.org/10.2523/IPTC-17122-MS.
Al-Naqib, K. M. 1967. Geology of the Arabian Peninsula Southwestern Iraq. Washington: U. S. Geological Survey Professional. United States Government Printing Office. Paper 560-G.
Al-Obaidi, R. Y. 2009. Identification of Palynozones and Age Evaluation of Zubair Formation, Southern Iraq. Journal of Al-Nahrain University 12 (3): 16–22.
Al-Obaidi, R. Y. 2010. Determination of Palynofacies to Assess Depositional Environments and Hydrocarbons Potential, Lower Cretaceolls, Zubair Formation South Iraq. Journal of College of Education 5: 163–174. https://iasj.net/iasj?func=article&aId=53721.
Arpat, G. B. and Caers, J. 2005. A Multiple-Scale, Pattern-Based Approach to Sequential Simulation in Geostatistics Ban_ 2004, ed. O. Leuanthong and C. V. Deutsch, pp. 255–264. Dordrecht, Netherlands: Springer.
Caers, J. and Zhang, T. 2004. Multiple-Point Geostatistics: A Quantitative Vehicle for Integrating Geologic Analogs Into Multiple Reservoir Models. AAPG Mem 80: 383–394.
Deutsch, C. V. and Journel, A. G. 1998. GSLIB. Geostatistical Software Library and User’s Guide. New York.
De Vries, L., Carrera, J., Falivene, O. et al. 2009. Application of Multiple Point Geostatistics to Non-Stationary Images. Math. Geosci. 41 (1): 2942.
Gomez, J. and Srivastava, R. 1990. ISIM3D: An ANSI-C Three-Dimensional Multiple Indicator Conditional Simulation. Computer and Geosciences 16 (4): 395–440. https://doi.org/10.1016/0098-3004(90)90010-Q.
Harris, G. D., Wellner, R. W., Catterall, V. et al. 2012. Stratigraphy and Depositional Environment of the Upper Zubair Sandstone (Main Pay), West Qurna 1 Field, Iraq. Presented at the EAGE Workshop on Iraq: Hydrocarbon Exploration and Field Development, Istanbul, Turkey, 29 April–2 May. Paper IR17.
Isaaks, E. 1990. The Application of Monte Carlo Methods to the Analysis of Spatially Correlated Data. PhD thesis, Stanford University, Stanford, California, USA.
Kitching, D., Farmer, R., and Abuzaid, M. 2013. In Search of the Remaining Oil in the “Main Pay” Member of the Zubair Formation through Surveillance Oil Mapping, Rumaila Field, Southern Iraq. Presented at the Second EAGE Workshop in Iraq, Dead Sea, Jordan, 15–18 September. https://doi.org/10.3997/2214-4609.20131456.
Liu, Y., Harding, A., Abriel, W. et al. 2004. Multiple-Point Simulation Integrating Wells, Three-Dimensional Seismic Data, and Geology. AAPG Bulletin 88 (7): 905–921.
Liu, Y., Harding, A., Journel, A. G. et al. 2005. A Workflow for Multiple-Point Geostatistical Simulation. In Geostatistics Banff, 2004, ed. O. Leuangthong and C. V. Deutsch, Vol. 14, pp. 245–254. Dordrecht: Springer.
Liu, Y. and Journel, A. G. 2005. Improving Sequential Simulation With a Structured Path Guided by Information Content. Math. Geol. 38: 945–964.
Mohammed, W. J., Al Jawad, M. S., and Al-Shamaa, D. A. 2010. Reservoir Flow Simulation Study for a Sector in Main Pay-South Rumaila Oil Field. Presented at the SPE Oil and Gas India Conference and Exhibition, Mumbai, India, 20–22 January. SPE-126427-MS. https://doi.org/10.2118/126427-MS.
Overeem, I. 2008. Geological Modeling Introduction: Lecture, Community Surface Dynamic Modeling System. University of Colorado at Boulder. Petrel Software Manual. 2013. Schlumberger Information Solutions.
Ringrose, P. and Bentley, M. 2015. Reservoir Model Design: A Practitioner’s Guide, Chapter 2. Springer. https://doi.org/10.1007/978-94-007-5497-3.
Strebelle, S. and Journel, A. G. 2001. Reservoir Modeling Using Multiple-Point Statistics. Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, 30 September–3 October. SPE-71324-MS. https://doi.org/10.2118/71324-MS.
Strebelle, S. 2003. New Multiple-Point Statistics Simulation Implementation To Reduce Memory and CPU-Demand. In Proc., IAMG, Portsmouth, UK, 7–12 September.
Wells, M., Kitching, D., Finucane, D. et al. 2013. An Integrated Description of the Stratigraphy and Depositional Environment of the “Main Pay” Member of the Zubair Formation, Rumaila, Iraq. Presented at the Second EAGE Workshop on Iraq, Dead Sea, 15–18 September. Paper IR21.
White, C. D. and Royer, S. A. 2003. Experimental Design as a Framework for Reservoir Studies. Presented at the Reservoir Simulation Symposium, Houston, 3–5 February. SPE-79676-MS. https://doi.org/10.2118/79676-MS.
Wu, J., Zhang, T., and Boucher, A. 2007. Non-Stationary Multiple-Point Geostatistical Simulations With Region Concept. In Proc., 20th SCRF Meeting, Stanford, California, USA.
Zhang, T. 2008. Incorporating Geological Conceptual Models and Interpretations Into Reservoir Modeling Using Multiple-Point Geostatistics. Earth Science Frontiers 15 (1): 26–35. https://doi.org/10.1016/S1872-5791(08)60016-0.