Co-optimization of CO₂-EOR and storage processes in mature oil reservoirs

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• 作者：William Ampomah ; Robert S. Balch ; Reid B. Grigg ; Brian McPherson ; Robert A. Will ; Si-Yong Lee ; Zhenxue Dai and Feng Pan
• 刊名：Greenhouse Gases: Science and Technology
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：7
• 期：1
• 页码：128-142
• 全文大小：1426K
• ISSN：2152-3878

文摘

This paper presents an optimization methodology for CO₂ enhanced oil recovery in partially depleted reservoirs. A field-scale compositional reservoir flow model was developed for assessing the performance history of an active CO₂ flood and for optimizing both oil production and CO₂ storage in the Farnsworth Unit (FWU), Ochiltree County, Texas. A geological framework model constructed from geophysical, geological, and engineering data acquired from the FWU was the basis for all reservoir simulations and the optimization method. An equation of state was calibrated with laboratory fluid analyses and subsequently used to predict the thermodynamic minimum miscible pressure (MMP). Initial history calibrations of primary, secondary and tertiary recovery were conducted as the basis for the study. After a good match was achieved, an optimization approach consisting of a proxy or surrogate model was constructed with a polynomial response surface method (PRSM). The PRSM utilized an objective function that maximized both oil recovery and CO₂ storage. Experimental design was used to link uncertain parameters to the objective function. Control variables considered in this study included: water alternating gas cycle and ratio, production rates and bottom-hole pressure of injectors and producers. Other key parameters considered in the modeling process were CO₂ purchase, gas recycle and addition of infill wells and/or patterns. The PRSM proxy model was ‘trained’ or calibrated with a series of training simulations. This involved an iterative process until the surrogate model reached a specific validation criterion. A sensitivity analysis was first conducted to ascertain which of these control variables to retain in the surrogate model. A genetic algorithm with a mixed-integer capability optimization approach was employed to determine the optimum developmental strategy to maximize both oil recovery and CO₂ storage. The proxy model reduced the computational cost significantly. The validation criteria of the reduced order model ensured accuracy in the dynamic modeling results. The prediction outcome suggested robustness and reliability of the genetic algorithm for optimizing both oil recovery and CO₂ storage. The reservoir modeling approach used in this study illustrates an improved approach to optimizing oil production and CO₂ storage within partially depleted oil reservoirs such as FWU. This study may serve as a benchmark for potential CO₂–EOR projects in the Anadarko basin and/or geologically similar basins throughout the world.