Fast Analysis of Optimal Improved-Oil-Recovery Switch Time Using a Two-Factor Production Model and Least-Squares Monte Carlo Algorithm (includes associated errata)
- Aojie Hong (National IOR Centre of Norway and University of Stavanger) | Reidar B. Bratvold (National IOR Centre of Norway and University of Stavanger) | Larry W. Lake (University of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- August 2019
- Document Type
- Journal Paper
- 1,144 - 1,160
- 2019.Society of Petroleum Engineers
- IOR Switch Time, Optimization, Least-Squares Monte Carlo Algorithm, Learning Over Time, Uncertainty
- 5 in the last 30 days
- 174 since 2007
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Simple or proxy production models are potentially very useful and tractable because they are computationally attractive while still providing insights into the decisions at hand. Useful and tractable models are required for supporting high-quality decisions in uncertain, complex, and computationally demanding contexts. A key decision for development planning is determining the optimal time to begin an improved-oil-recovery (IOR) process. We aim to illustrate the implementation and application of a useful and tractable approach for analyzing the optimal IOR switch time using a two-factor production model and the least-squares Monte Carlo (LSM) algorithm.
The two-factor production model contains only two parameters for each recovery phase. One parameter describes how much recovery efficiency a recovery mechanism can ultimately achieve, and the other describes how fast the recovery efficiency increases. The simplicity of the model makes it computationally attractive. Our modified LSM algorithm is an approximate dynamic programming approach, which allows for learning over time. It provides a near-optimal solution for the IOR switch-time problem. The value-of-information (VOI) framework, which is a powerful decision-analysis tool, provides an estimate of the value of learning.
Closed-loop reservoir management (CLRM) is considered to be a state-of-the-art approach to solving for the optimal IOR switch time. However, the CLRM approach can produce a suboptimal solution because it considers only uncertainties and actions reflecting currently available information, but not those uncertainties and actions arising from future information. The dynamic programming approach used here considers both the effect of the information obtained before a decision is made and the effect of the information that might be obtained to support future decisions. We conclude that a dynamic programming approach, such as the modified LSM algorithm, can significantly improve both the timing and value of decisions, leading to a significant increase in a field’s economic performance. Furthermore, the two-factor model combined with the modified LSM algorithm is tractable and provides useful insights into the IOR switch-time problem.
The novelties provided by this work are developing and illustrating the structure of the IOR switch-time problem using a decision tree; demonstrating and discussing the suboptimality of the CLRM solution; developing and illustrating the detailed steps of applying the modified LSM algorithm for the IOR switch-time decision; and implementing the two-factor model combined with the modified LSM algorithm for analyzing the optimal IOR switch time.
Errata Notice: While in preprint, this paper was modified from its original version to include the changes detailed in erratum SPE-191327-ER (https://doi.org/10.2118/191327-ER). While all changes have been incorporated into the published paper, the errata will be included as supporting information to the paper as part of the version record.
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