Using the Least-Squares Monte Carlo Algorithm to Optimize IOR Initiation Time
NTNU, 2019
Online
Hochschulschrift
Zugriff:
The application of the Least-Squares Monte Carlo (LSM) algorithm in the oil and gas industry is increasing. Its use with a production model has been demonstrated to be insightful by Hong et al. (2018) in terms of optimizing the initiation time of an Improved Oil Recovery (IOR) process. This demonstration also reflects the application of decision analysis (DA) in solving the IOR initiation time problem, which is a sequential decision problem in reservoir management. In this context, DA provides a framework that can systematically address the sequential decision problem in reservoir management and generate insights in reservoir decision making. The production model used in Hong et al. (2018) was the two-factor production model, which is developed by Parra-Sanchez (2010). This production model is a decline curve-based model and thus, it is computationally attractive. Additionally, it is formulated in terms of the recovery factor of a recovery phase. In this context, for each phase, this model depends on two parameters, namely theoretical ultimate recovery factor and time constant (Parra-Sanchez, 2010). Aside from this, pertaining to the use of LSM algorithm, the state variables used are generally modeled as Markovian processes (Longstaff and Schwartz, 2001; Smith, 2005; Willigers and Bratvold, 2009). However, in Hong et al. (2018), the state variable cannot be modeled as a Markovian process (the measured oil production rate is used as a state variable in this case and the details will follow later). Therefore, Hong et al. (2018) have slightly modified the LSM algorithm to handle non-Markovian processes. The modified LSM algorithm used by Hong et al. (2018) as well as in this work is an approximate dynamic programming (ADP) approach that can provide a near-optimal solution to the IOR initiation time problem. The need for approximation stems from the fact that dynamic programming (DP) suffers from the curse of dimensionality when the state space grows. We call this ADP as a Sequential Reservoir Decision Making (SRDM) ...
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Using the Least-Squares Monte Carlo Algorithm to Optimize IOR Initiation Time
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Autor/in / Beteiligte Person: | Ng, Cuthbert Shang Wui ; Bratvold, Reidar Brumer ; Hong, Aojie |
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Veröffentlichung: | NTNU, 2019 |
Medientyp: | Hochschulschrift |
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