Reservoir and Economic-Uncertainties Assessment for Recovery-Strategy Selection Using Stochastic Decision Trees
- Philippe Vincent (Neptune Energy) | Thomas Schaaf (Storengy)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- July 2019
- Document Type
- Journal Paper
- 2019.Society of Petroleum Engineers
- recovery strategy, uncertainty management, joint technical and economic evaluation, decision making process, risk analysis
- 6 in the last 30 days
- 31 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
The field-development phase before investment approval is characterized by relatively large uncertainties at the time important decisions have to be made. For instance, it is crucial to select an appropriate recovery strategy (depletion or injection) to obtain optimal hydrocarbon cumulative production while ensuring good profitability of the project. Evaluation of the reservoir along with economic uncertainties and quantification of their impact are needed before the field-development concept selection.
In this paper we describe how to stochastically assess reservoir and economic uncertainties and the screening process used to select the best recovery strategy. Our chosen methodology is a combination of uncertainty studies, including continuous, discrete, and controllable parameters. The different screened scenarios are combined in a stochastic decision tree, built up through decision and chance nodes, to establish a distribution of recoverable volumes and rank the recovery strategies given a chosen criterion. A second uncertainty study is performed by adding economic uncertainties to the initial set of reservoir uncertain parameters. Eventually, a new decision tree is established, and scenarios are ranked using economic criteria.
We present an application of this methodology to an oil field from the Norwegian continental shelf and how recovery strategies are ranked in this paper. The described methodology has exhibited the risks and uncertainties carried by the project, as it was possible to rank the different solutions on the basis of the dispersion of the recoverable volumes distribution and/or on the net present value (NPV). In the context of a marginal- or large-capital-expenditure (CAPEX) project, a robust P90 case is required, and this might, therefore, influence the choice of the recovery strategy. For instance, a scenario yielding the largest hydrocarbon volume might not be selected because it requires too many wells and/or too large an investment if one of these criteria is defined as the most important. In addition, the combination of uncertainty studies enabled a full economic evaluation covering the entire recoverable-volumes distribution whereas in many projects, economic evaluation is focused on the P90, mean, and P10 scenarios.
The two-step integrated approach allows a decision to be made while taking into account both reservoir and economic aspects. A combined stochastic approach to the reservoir and economic uncertainties avoids a biased decision. All cases are stochastically covered and screened using a systematic and unified methodology that gives the same weight to each scenario.
|File Size||1 MB||Number of Pages||18|
Adeyinka, A., Olatunde, F., and Bodunrin, A. 2017. Deepwater Infill Drilling Evaluation Using Experimental Design: The Agbami Case Study. Presented at the Nigeria Annual International Conference and Exhibition, Nigeria, 31 July–2 August. SPE-189103-MS. https://doi.org/10.2118/189103-MS.
Beicip-Franlap, IFP Energies Nouvelles. 2015a. User Manual COUGAR™ Software. France: Beicip-Franlap, IFP Energies Nouvelles.
Beicip-Franlap, IFP Energies Nouvelles. 2015b. Reference Manual COUGAR™ Software. France: Beicip-Franlap, IFP Energies Nouvelles.
Chorn, L. G. and Croft, M. 1998. Resolving Reservoir Uncertainty To Create Value. Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 27–30 September. SPE-49094-MS. https://doi.org/10.2118/49094-MS.
Damsleth, E., Hage, A., and Volden, R. 1992. Maximum Information at Minimum Cost: A North Sea Field Development Study With an Experimental Design. J Pet Technol 44 (12): 1350–1356. SPE-23139-PA. https://doi.org/10.2118/23139-PA.
Egeland, T., Holden, L., and Larsen, E. A. 1992. Designing Better Decisions. Presented at the European Petroleum Computer Conference, Stavanger, Norway, 24–27 May. SPE-24275-MS. https://doi.org/10.2118/24275-MS.
He, J., Xie, J., Sarma, P. et al. 2016. Proxy-Based Work Flow for A Priori Evaluation of Data-Acquisition Programs. SPE J. 21 (4): 1400–1412. SPE-173229-PA. https://doi.org/10.2118/173229-PA.
Helton, J. C. and Davis, F. 2003. Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems. Reliab. Eng. Syst. Saf. 81 (1): 23–69. https://doi.org/10.1016/S0951-8320(03)00058-9.
Itotoi, I. H., Ojeke, A., Nnamdi, D. et al. 2010. Managing Reservoir Uncertainty in Gas Field Development Using Experimental Design. Presented at the Nigeria Annual International Conference and Exhibition, Tinapa–Calabar, Nigeria, 31 July–7 August. SPE-140619-MS. https://doi.org/10.2118/140619-MS.
Kullawan, K., Bratvold, R. B., and Nieto, C. M. 2017. Decision-Oriented Geosteering and the Value of Look-Ahead Information: A Case-Based Study. SPE J. 22 (3): 767–782. SPE-184392-PA. https://doi.org/10.2118/184392-PA.
Litvak, M., Onwunalu, J., Baxter, J. 2011. Field Optimization With Subsurface Uncertainties. Presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, 30 October–2 November. SPE-146512-MS. https://doi.org/10.2118/146512-MS.
Manceau, E., Feraille, M., Zabalza-Mezghani, I. et al. 2005. Advanced Risk-Analysis Approach for Optimization of a Water-Injection Program: Illustration on a Brazilian Field Case. Presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Rio de Janeiro, Brazil, 20–23 June. SPE-94845-MS. https://doi.org/10.2118/94845-MS.
Manceau, E., Mezghani, M., Zabalza-Mezghani, I. et al. 2001. Combination of Experimental Design and Joint Modelling Methods for Quantifying the Risk Associated With Deterministic and Stochastic Uncertainties—An Integrated Test Study. Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 30 September–3 October. SPE-71620-MS. https://doi.org/10.2118/71620-MS.
Myers, R. H. and Montgomery, D. C. 1995. Response Surface Methodology—Process and Product Optimization Using Designed Experiments. New York: John Wiley & Sons.
Narayanan, K., Cullick, A. S., and Bennett, M. 2003. Better Field Development Decisions From Multi-Scenario, Interdependent Reservoir, Well, and Facility Simulations. Presented at the SPE Reservoir Simulation Symposium, Houston, Texas, 3–5 February 2003. SPE-79703-MS. https://doi.org/10.2118/79703-MS.
Passone, S. and McRae, G. J. 2007. Probabilistic Field Development in Presence of Uncertainty. Presented at the International Petroleum Technology Conference, Dubai, UAE, 4–6 December. IPTC-11294-MS. https://doi.org/10.2523/IPTC-11294-MS.
Peaceman, D. W. 1983. Interpretation of Well-Block Pressures in Numerical Reservoir Simulation With Nonsquare Grid Blocks and Anisotropic Permeability. SPE J. 23 (3): 531–543. SPE-10528-PA. http://dx.doi.org/10.2118/10528-PA.
Rodriguez, R., Solano, K., Guevara, S. et al. 2007. Integration of Subsurface, Surface and Economics Under Uncertainty in Orocual Field. Presented at the Latin American and Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, 15–18 April. SPE-107259-MS. https://doi.org/10.2118/107259-MS.
Saltelli, A., Tarantola, S., Campolongo, F. et al. 2004. Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Hoboken, New Jersey: Wiley.
Saputelli, L., Cherian, B., Gregoriadis, K. et al. 2000. Integration of Computer-Aided High-Intensity Design With Reservoir Exploitation of Remote and Offshore Locations. Presented at the International Oil and Gas Conference and Exhibition in China, Beijing, 7–10 November. SPE-64621-MS. https://doi.org/10.2118/64621-MS.
Scheidt, C., Zabalza-Mezghani, I., Feraille, M. et al. 2007. Toward a Reliable Quantification of Uncertainty on Production Forecasts: Adaptive Experimental Designs. Oil Gas Sci Technol Rev l’IFP 62 (2): 207–224. https://doi.org/10.2516/ogst:2007018.
Yeten, B., Castellini, A., Guyaguler, B. et al. 2005. A Comparison Study on Experimental Design and Response Surface Methodologies. Presented at the SPE Reservoir Simulation Symposium, Houston, Texas, 31 January–2 February. SPE-93347-MS. https://doi.org/10.2118/93347-MS.