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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:
 [1];  [1];  [1];  [2];  [3];  [3]; ORCiD logo [4];  [2]
  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:
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:
Journal Article: 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

Citation Formats

Ampomah, William, Balch, Robert S., Grigg, Reid B., McPherson, Brian, Will, Robert A., Lee, Si-Yong, Dai, Zhenxue, and Pan, Feng. Co-Optimization of CO2-EOR and Storage Processes in Mature Oil Reservoirs. United States: N. p., 2016. Web. doi:10.1002/ghg.1618.
Ampomah, William, Balch, Robert S., Grigg, Reid B., McPherson, Brian, Will, Robert A., Lee, Si-Yong, Dai, Zhenxue, & Pan, Feng. Co-Optimization of CO2-EOR and Storage Processes in Mature Oil Reservoirs. United States. https://doi.org/10.1002/ghg.1618
Ampomah, William, Balch, Robert S., Grigg, Reid B., McPherson, Brian, Will, Robert A., Lee, Si-Yong, Dai, Zhenxue, and Pan, Feng. 2016. "Co-Optimization of CO2-EOR and Storage Processes in Mature Oil Reservoirs". United States. https://doi.org/10.1002/ghg.1618. https://www.osti.gov/servlets/purl/1345931.
@article{osti_1345931,
title = {Co-Optimization of CO2-EOR and Storage Processes in Mature Oil Reservoirs},
author = {Ampomah, William and Balch, Robert S. and Grigg, Reid B. and McPherson, Brian and Will, Robert A. and Lee, Si-Yong and Dai, Zhenxue and Pan, Feng},
abstractNote = {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 process 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.},
doi = {10.1002/ghg.1618},
url = {https://www.osti.gov/biblio/1345931}, journal = {Greenhouse Gases: Science and Technology},
issn = {2152-3878},
number = 1,
volume = 7,
place = {United States},
year = {Tue Aug 02 00:00:00 EDT 2016},
month = {Tue Aug 02 00:00:00 EDT 2016}
}

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Works referenced in this record:

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conference, April 2013


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Pre-site Characterization Risk Analysis for Commercial-Scale Carbon Sequestration
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Comparison of Probabilistic Techniques in the Study of Fluid Effects on Seismic Properties
conference, January 2014


Uncertainty Quantification for CO2 Sequestration and Enhanced Oil Recovery
journal, January 2014


Geological storage of CO2: Application, feasibility and efficiency of global sensitivity analysis and risk assessment using the arbitrary polynomial chaos
journal, November 2013


Mobility and Conformance Control for CO2 EOR via Thickeners, Foams, and Gels -- A Literature Review of 40 Years of Research and Pilot Tests
conference, April 2013


CO2-EOR and storage design optimization
journal, June 2014


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Economic Co-optimization of Oil Recovery and CO2 Sequestration
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Farnsworth Field CO2-EOR Project: Performance Case History
conference, April 2016


Experimental Design as a Framework for Reservoir Studies
conference, April 2013


Proxy Modeling in Production Optimization
conference, April 2013


Co-optimization of CO2 EOR and Sequestration Process in a North Sea Chalk Reservoir
conference, January 2009


Effect of Sampling Strategies on Prediction Uncertainty Estimation
conference, February 2007


Analysis of Upscaling Algorithms in Heterogeneous Reservoirs with Different Recovery Processes
conference, March 2015


Works referencing / citing this record:

Two risk metrics for CO 2 storage reservoirs with varying domain size, heterogeneity, and injection rate
journal, June 2019


Environmental concern-based site screening of carbon dioxide geological storage in China
journal, August 2017