Title: Co-Optimization of CO₂-EOR and Storage Processes in Mature Oil Reservoirs

Abstract

Abstract This paper presents an optimization methodology for CO ₂ enhanced oil recovery in partially depleted reservoirs. A field‐scale compositional reservoir flow model was developed for assessing the performance history of an active CO ₂ flood and for optimizing both oil production and CO ₂ 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 CO ₂ 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 processmore » were CO ₂ 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 CO ₂ 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 CO ₂ storage. The reservoir modeling approach used in this study illustrates an improved approach to optimizing oil production and CO ₂ storage within partially depleted oil reservoirs such as FWU. This study may serve as a benchmark for potential CO ₂ –EOR projects in the Anadarko basin and/or geologically similar basins throughout the world. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd.« less

Authors:

Ampomah, William ^[1]; Balch, Robert S. ^[1]; Grigg, Reid B. ^[1]; McPherson, Brian ^[2]; Will, Robert A. ^[3]; Lee, Si-Yong ^[3]; Dai, Zhenxue  ^[4];

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Pan, Feng ^[2]

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+ Show Author Affiliations

1. Petroleum Recovery Research Center, Socorro, NM (United States)
2. Univ. of Utah, Salt Lake City, UT (United States). Energy and Geoscience Inst.
3. Schlumberger Carbon Services, Cambridge, MA (United States)
4. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Publication Date:

Tue Aug 02 00:00:00 EDT 2016

Research Org.:

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Org.:

USDOE

OSTI Identifier:

1345931

Alternate Identifier(s):

OSTI ID: 1401867

Report Number(s):

LA-UR-16-25808
Journal ID: ISSN 2152-3878

Grant/Contract Number:  

AC52-06NA25396; FC26-05NT42591

Resource Type:

Accepted Manuscript

Journal Name:

Greenhouse Gases: Science and Technology

Additional Journal Information:

Journal Volume: 7; Journal Issue: 1; Journal ID: ISSN 2152-3878

Publisher:

Society of Chemical Industry, Wiley

Country of Publication:

United States

Language:

English

Subject:

58 GEOSCIENCES; 02 PETROLEUM; Environmental Protection; CO2 storage, proxy model, response surface, co-optimization, reservoir simulation, enhanced oil recovery