Discerning In-Situ Performance of an Enhanced-Oil-Recovery Agent in the Midst of Geological Uncertainty: II. Fluvial-Deposit Reservoir
- Seyed A. Fatemi (Delft University of Technology) | Jan-Dirk Jansen (Delft University of Technology) | William R. Rossen (Delft University of Technology)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- June 2019
- Document Type
- Journal Paper
- 1,076 - 1,091
- 2019.Society of Petroleum Engineers
- uncertainty in geological description, waterflood, polymer flood, in situ performance, uncertainty in EOR performance
- 2 in the last 30 days
- 117 since 2007
- Show more detail
- View rights & permissions
An enhanced-oil-recovery (EOR) pilot test has multiple goals, among them to be profitable (if possible), demonstrate oil recovery, verify the properties of the EOR agent in situ, and provide the information needed for scaleup to an economical process. Given the complexity of EOR processes and the inherent uncertainty in the reservoir description, it is a challenge to discern the properties of the EOR agent in situ in the midst of geological uncertainty. We propose a numerical case study to illustrate this challenge: a polymer EOR process designed for a 3D fluvial-deposit water/oil reservoir. The polymer is designed to have a viscosity of 20 cp in situ. We start with 100 realizations of the 3D reservoir to reflect the range of possible geological structures honoring the statistics of the initial geological uncertainties. For a population of reservoirs representing reduced geological uncertainty after 5 years of waterflooding, we select three groups of 10 realizations out of the initial 100, with similar water-breakthrough dates at the four production wells. We then simulate 5 years of polymer injection. We allow that the polymer process might fail in situ and viscosity could be 30% of that intended. We test whether the signals of this difference at injection and production wells would be statistically significant in the midst of geological uncertainty. Specifically, we compare the deviation caused by loss of polymer viscosity with the scatter caused by the geological uncertainty using a 95% confidence interval. Among the signals considered, polymer-breakthrough time, minimum oil cut, and rate of rise in injection pressure with polymer injection provide the most-reliable indications of whether a polymer viscosity was maintained in situ.
|File Size||1 MB||Number of Pages||16|
Arpat, G. B. and Caers, J. 2004. A Multiple-Scale, Pattern-Based Approach to Sequential Simulation. In Geostatistics Banff 2004, Quantitative Geology and Geostatistics series, Vol. 14, ed. O. Leuangthong and C. V. Deutsch, 255–264. Dordrecht, The Netherlands: Springer.
Brown, C. E. and Smith, P. J. 1984. The Evaluation of Uncertainty in Surfactant EOR Performance Prediction. Presented at the SPE Annual Technical Conference and Exhibition, Houston, 16–19 September. SPE-13237-MS. https://doi.org/10.2118/13237-MS.
Chen, Q., Gerritsen, M. G., and Kovscek, A. R. 2008. Effects of Reservoir Heterogeneities on the Steam-Assisted Gravity-Drainage Process. SPE Res Eval & Eng 11 (5): 921–932. SPE-109873-PA. https://doi.org/10.2118/109873-PA.
Craft, B. C., Hawkins, M., and Terry, R. E. 1991. Applied Petroleum Reservoir Engineering, second edition. Upper Saddle River, New Jersey: Prentice Hall.
Craig, D. P. and Jackson, R. A. 2017. Calculating the Volume of Reservoir Investigated During a Fracture-Injection/Falloff Test DFIT. Presented at the SPE Hydraulic Fracturing Technology Conference and Exhibition, The Woodlands, Texas, 24–26 January. SPE-184820-MS. https://doi.org/10.2118/184820-MS.
Dake, L. P. 2001. Practice of Reservoir Engineering, first edition. Developments in Petroleum Science, Vol. 36. New York City: Elsevier Science.
Denney, D. 2011. Uncertainty Management in a Major CO2 EOR Project. J Pet Technol 63 (7): 112–113. SPE-0711-0112-JPT. https://doi.org/10.2118/0711-0112-JPT.
Dickson, J. L., Leahy-Dios, A., and Wylie, P. L. 2010. Development of Improved Hydrocarbon Recovery Screening Methodologies. Presented at the SPE Improved Oil Recovery Symposium, Tulsa, 24–28 April. SPE-129768-MS. https://doi.org/10.2118/129768-MS.
Fanchi, J. R. 2005. Principles of Applied Reservoir Simulation, third edition. Burlington, Massachusetts: Elsevier.
Fatemi, S. A., Jansen, J. D., and Rossen, W. R. 2017. Discerning In-Situ Performance of an EOR Agent in the Midst of Geological Uncertainty I: Layer Cake Reservoir Model. J. Pet. Sci. Eng. 158 (September): 56–65. https://doi.org/10.1016/j.petrol.2017.08.021.
Jansen, J. D., Fonseca, R. M., Kahrobaei, S. et al. 2014. The Egg Model–A Geological Ensemble for Reservoir Simulation. Geosci. Data J. 1 (2): 192–195. https://doi.org/10.1002/gdj3.21.
Kumar, M., Hoang, V. T., Satik, C. et al. 2008. High-Mobility-Ratio Waterflood Performance Prediction: Challenges and New Insights. SPE Res Eval & Eng 11 (1): 186–196. SPE-97671-PA. https://doi.org/10.2118/97671-PA.
Lake, L. W., Johns, R., Rossen, W. R. et al. 2014. Fundamentals of Enhanced Oil Recovery. Richardson, Texas: Society of Petroleum Engineers.
Mantilla, C. A. and Srinivasan, S. 2011. Feedback Control of Polymer Flooding Process Considering Geologic Uncertainty. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 21–23 February. SPE-141962-MS. https://doi.org/10.2118/141962-MS.
Popov, Y., Spasennykh, M., Miklashevskiy, D. et al. 2010. Thermal Properties of Formations From Core Analysis: Evolution in Measurement Methods, Equipment, and Experimental Data in Relation to Thermal EOR. Presented at the Canadian Unconventional Resources and International Petroleum Conference, Calgary, 19–21 October. SPE-137639-MS. https://doi.org/10.2118/137639-MS.
Saleh, L. D., Wei, M., and Bai, B. 2014. Data Analysis and Updated Screening Criteria for Polymer Flooding Based on Oilfield Data. SPE Res Eval & Eng 17 (1): 15–25. SPE-168220-PA. https://doi.org/10.2118/168220-PA.
Seright, R. S., Seheult, J. M., and Talashek, T. 2009. Injectivity Characteristics of EOR Polymers. SPE J. 12 (5): 783–792. SPE-115142-PA. https://doi.org/10.2118/115142-PA.
Sheng, J. 2011. Modern Chemical Enhanced Oil Recovery: Theory and Practice. Burlington, Massachusetts: Elsevier.
Soleimani, A., Penney, R. K., Hegazy, O. et al. 2011. Impact of Fluvial Geological Characteristics on EOR Screening of a Large Heavy Oil Field. Presented at the SPE Enhanced Oil Recovery Conference, Kuala Lumpur, 19–21 July. SPE-143650-MS. https://doi.org/10.2118/143650-MS.
Stanley, B. 2014. Effect of Uncertainty in PVT Properties on CO2 EOR. Presented at the SPE Nigeria Annual International Conference and Exhibition, Lagos, 5–7 August. SPE-172430-MS. https://doi.org/10.2118/172430-MS.
Van Doren, J., Douma, S. G., Wassing, L. B. M. et al. 2011. Adjoint-Based Optimization of Polymer Flooding. Presented at the SPE Enhanced Oil Recovery Conference, Kuala Lumpur, 19–21 July. SPE-144024-MS. https://doi.org/10.2118/144024-MS.
van Essen, G., Zandvliet, M., Van den Hof, P. et al. 2009. Robust Waterflooding Optimization of Multiple Geological Scenarios. SPE J. 14 (1): 202–210. SPE-102913-PA. https://doi.org/10.2118/102913-PA.
Weiss, W. W. and Baldwin, R. W. 1985. Planning and Implementing a Large-Scale Polymer Flood. J Pet Technol 37 (4): 720–730. SPE-12637-PA. https://doi.org/10.2118/12637-PA.