Stochastic Oilfield Optimization Under Uncertain Future Development Plans
- Atefeh Jahandideh (University of Southern California) | Behnam Jafarpour (University of Southern California)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- August 2019
- Document Type
- Journal Paper
- 1,526 - 1,551
- 2019.Society of Petroleum Engineers
- Stochastic Optimization, Uncertainty, Future Development, Oilfield
- 31 in the last 30 days
- 89 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
Reservoir simulation is a valuable tool for performance prediction, production optimization, and field-development decision making. In recent years, significant progress has been made in developing automated workflows for optimization of production and field development by combining reservoir simulation with numerical optimization schemes. Although optimization under geologic uncertainty has received considerable attention, the uncertainty associated with future development activities has not yet been considered in field-development optimization. In practice, reservoirs undergo extensive development activities throughout their life cycle. Disregarding the possibility of future developments can lead to field-performance predictions and optimization results that might be far from optimal. This paper presents a stochastic optimization formulation to account for the uncertainty in future development activities while optimizing current decision variables (e.g., well controls and locations). A motivating example is presented first to demonstrate the significance of including the uncertainty in future drilling plans in oilfield-development optimization. Because future decisions might not be implemented as planned, a stochastic optimization framework is developed to incorporate future drilling activities as uncertain (random) variables. A multistage stochastic programming framework is introduced, in which the decision maker selects an optimal strategy for the current stage decisions while accounting for the uncertainty in future development activities. For optimization, a sequential approach is adopted whereby well locations and controls are repeatedly optimized until improvements in the objective function fall below a threshold. Case studies are presented to demonstrate the advantages of treating future field-development activities as uncertain events in the optimization of current decision variables. In developing real fields, where various unpredictable external factors can cast uncertainty regarding future drilling activities, the proposed approach provides solutions that are more robust and can hedge against changes/uncertainty in future development plans better than conventional workflows.
|File Size||2 MB||Number of Pages||26|
Badru, O. and Kabir, C. S. 2003. Well Placement Optimization in Field Development. Presented at the SPE Annual Technical Conference and Exhibition, Denver, 5–8 October. SPE-84191-MS. https://doi.org/10.2118/84191-MS.
Bangerth, W., Klie, H., Wheeler, M. F. et al. 2006. On Optimization Algorithms for the Reservoir Oil Well Placement Problem. Computat Geosci 10 (3): 303–319. https://doi.org/10.1007/s10596-006-9025-7.
Beckner, B. L. and Song, X. 1995. Field Development Planning Using Simulated Annealing—Optimal Economic Well Scheduling and Placement. Presented at the SPE Annual Technical Conference and Exhibition, Dallas, 22–25 October. SPE-30650-MS. https://doi.org/10.2118/30650-MS.
Bellman, R. 1961. Adaptive Control Processes: A Guided Tour. Princeton, New Jersey: Princeton University Press.
Brouwer, D. R. and Jansen, J. D. 2002. Dynamic Optimization of Water Flooding With Smart Wells Using Optimal Control Theory. Presented at the European Petroleum Conference, Aberdeen, 29–31 October. SPE-78278-MS. https://doi.org/10.2118/78278-MS.
Centilmen, A., Ertekin, T., and Grader, A. S. 1999. Applications of Neural Networks in Multiwell Field Development. Presented at the SPE Annual Technical Conference and Exhibition, Houston, 3–6 October. SPE-56433-MS. https://doi.org/10.2118/56433-MS.
Chen, Y., Oliver, D. S., and Zhang, D. 2009. Efficient Ensemble-Based Closed-Loop Production Optimization. SPE J. 14 (4): 634–645. SPE-112873-PA. https://doi.org/10.2118/112873-PA.
Diwekar, U. 2008. Introduction to Applied Optimization. New York City: Springer Optimization and Its Application Series, Springer.
Emerick, A. A., Silva, E., Messer, B. et al. 2009. Well Placement Optimization Using a Genetic Algorithm With Nonlinear Constraints. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 2–4 February. SPE-118808-MS. https://doi.org/10.2118/118808-MS.
Forouzanfar, F. and Reynolds, A. C. 2014. Joint Optimization of Number of Wells, Well Locations and Controls Using A Gradient-Based Algorithm. Chem Eng Res Des 92 (7): 1315–1328. https://doi.org/10.1016/j.cherd.2013.11.006.
Guyaguler, B. and Horne, R. N. 2001. Uncertainty Assessment of Well Placement Optimization. Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, 30 September–3 October. SPE-71625-MS. https://doi.org/10.2118/71625-MS.
Hanea, R. G., Casanova, P., Hustoft, L. et al. 2017. Drill and Learn: A Decision Making Workflow to Quantify Value of Learning. Presented at the SPE Reservoir Simulation Conference, Montgomery, Texas, 20–22 February. SPE-182719-MS. https://doi.org/10.2118/182719-MS.
Humphries, T. D., Haynes, R. D., and James, L. A. 2014. Simultaneous and Sequential Approaches to Joint Optimization of Well Placement and Control. Computat Geosci 18 (3–4): 433–448. https://doi.org/10.1007/s10596-013-9375-x.
Isebor, O. J., Echeverría Ciaurri, D., and Durlofsky, L. J. 2014. Generalized Field-Development Optimization With Derivative-Free Procedures. SPE J. 19 (5): 891–908. SPE-163631-PA. https://doi.org/10.2118/163631-PA.
Jahandideh, A. and Jafarpour, B. 2016. Optimization of Hydraulic Fracturing Design Under Spatially Variable Shale Fracability. J Pet Sci Eng 138 (February): 174–188. https://doi.org/10.1016/j.petrol.2015.11.032.
Jaikumar, R. and Bohn, R. E. 1992. A Dynamic Approach to Operations Management: An Alternative to Static Optimization. Int J Prod Econ 27 (3): 265–282. https://doi.org/10.1016/0925-5273(92)90101-C.
Jesmani, M., Jafarpour, B., Bellout, M. C. et al. 2016. Application of Simultaneous Perturbation Stochastic Approximation to Well Placement Optimization Under Uncertainty. Presented at the ECMOR XV–15th European Conference on the Mathematics ofOil Recovery, Amsterdam, 29August–1 September.
Li, L. and Jafarpour, B. 2012. A Variable-Control Well Placement Optimization for Improved Reservoir Development. Computat Geosci 16 (4): 871–889. https://doi.org/10.1007/s10596-012-9292-4.
Li, L., Jafarpour, B., and Mohammad-Khaninezhad, M. R. 2013. A Simultaneous Perturbation Stochastic Approximation Algorithm for Coupled Well Placement and Control Optimization Under Geologic Uncertainty. Computat Geosci 17 (1): 167–188. https://doi.org/10.1007/s10596-012-9323-1.
Montes, G., Bartolome, P., and Udias, A. L. 2001. The Use of Genetic Algorithms in Well Placement Optimization. Presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Buenos Aires, 25–28 March. SPE-69439-MS. https://doi.org/10.2118/69439-MS.
MATLAB Reservoir Simulation Toolbox (MRST). 2016. MRST Version 2016b, www.sintef.no/MRST. MATLAB is a registered trademark of The MathWorks, Inc.
Navabi, S., Khaninezhad, R., and Jafarpour, B. 2016. A Unified Formulation for Generalized Oilfield-Development Optimization. Computat Geosci 21 (1): 47–74. https://doi.org/10.1007/s10596-016-9594-z.
Sarma, P., Durlofsky, L. J., Aziz, K. et al. 2006. Efficient Real-Time Reservoir Management Using Adjoint-Based Optimal Control and Model Updating. Computat Geosci 10 (1): 3–36. https://doi.org/10.1007/s10596-005-9009-z.
Scott, A. J. 1971. Dynamic Location-Allocation Systems: Some Basic Planning Strategies. Environ Plan A 3 (1): 73–82. https://doi.org/10.1068%2Fa030073.
Shapiro, A. 2011. Topics in Stochastic Programming. Louvain-la-Neuve, Belgium: CORE Lecture Series, Catholic University of Louvain.
Shirangi, M. G. and Durlofsky, L. J. 2015. Closed-Loop Field Development Under Uncertainty by Use of Optimization With Sample Validation. SPE J. 20 (5): 908–922. SPE-173219-PA. https://doi.org/10.2118/173219-PA.
Siraj, M. M., Van den Hof, P. M. J., and Jansen, J. D. 2015. Risk Management in Oil Reservoir Water-Flooding Under Economic Uncertainty. Proc., 2015 54th IEEE Conference on Decision and Control, Osaka, Japan, 15–18 December, 7542–7547. https://doi.org/10.1109/CDC.2015.7403410.
Siraj, M. M., Van den Hof, P. M. J., and Jansen, J. D. 2016. Robust Optimization of Water-Flooding in Oil Reservoirs Using Risk Management Tools. IFAC-Papers OnLine 49 (7): 133–138. https://doi.org/10.1016/j.ifacol.2016.07.229.
Siraj, M. M., Van den Hof, P. M. J., and Jansen, J.-D. 2017. Handling Geological and Economic Uncertainties in Balancing Short-Term and Long-Term Objectives in Waterflooding Optimization. SPE J. 22 (4): 1313–1325. SPE-185954-PA. https://doi.org/10.2118/185954-PA.
Society of Petroleum Engineers (SPE). 2000. 10th SPE Comparative Solution Project: Description of Model 2, https://www.spe.org/web/csp/datasets/set02.htm (accessed 2 May 2019).
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.
Wang, C., Li, G., and Reynolds, A. C. 2009. Production Optimization in Closed-Loop Reservoir Management. SPE J. 14 (3): 506–523. SPE-109805-PA. https://doi.org/10.2118/109805-PA.
Yeten, B., Durlofsky, L. J., and Aziz, K. 2002. Optimization of Nonconventional Well Type, Location and Trajectory. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 29 September–2 October. SPE-77565-MS. https://doi.org/10.2118/77565-MS.