Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework

You, Junyu; Ampomah, William; Sun, Qian

doi:10.1016/j.apenergy.2020.115695

Title: Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework

Journal Article · Sun Sep 06 00:00:00 EDT 2020 · Applied Energy

DOI:https://doi.org/10.1016/j.apenergy.2020.115695· OSTI ID:1849087

You, Junyu ^[1]; Ampomah, William ^[1]; Sun, Qian ^[1]

New Mexico Tech, Socorro NM (United States). Petroleum Recovery Research Center

In this report, a robust machine-learning-based computational framework that couples multi-layer neural network (MLNN) proxies and a multi-objective particle swarm optimizer (MOPSO) to design water-alternating-carbon dioxide injection (CO₂-WAG) projects is presented. The proposed optimization protocol considers various objectives, including oil recovery and CO₂ storage volume. Expert MLNN systems are trained and employed as surrogate models of the high-fidelity compositional simulator in the optimization workflow. When multiple objective functions are considered, two approaches are employed to treat the objectives: the weighted sum method and the Pareto-front-based scheme. A field-scale implementation focusing on tertiary recovery in the Morrow B formation at Farnsworth Unit (FWU) is presented. The developed Pareto-optimal solutions indicate the maximal available oil production can be 1.64 × 10⁷ barrels and maximal carbon storage can achieve 2.35 × 10⁷ tons. Trade-offs factor is defined to divide the constructed Pareto front into 4 sections with the trade-off factors’ value ranges from 0.35 to 49.9. This work also compares the optimum solution found by the aggregative objective function and the solution repository covered by the Pareto front that considers the physical and operational constraints and reduces uncertainties involved by the multi-objective optimization process. Our comparison indicates multiple solutions exist to satisfy the objective criteria of the WAG design, and these results cannot be found using the traditional weighted sum method. The Pareto front solution can provide more options for project designers, but decisions regarding necessary trade-offs must be made using the solution repository to balance the project economics and CO₂ storage amount.

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Research Organization:: New Mexico Institute of Mining and Technology, Socorro, NM (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy (FE)

Grant/Contract Number:: FC26-05NT42591

OSTI ID:: 1849087

Alternate ID(s):: OSTI ID: 1759140

Journal Information:: Applied Energy, Vol. 279, Issue C; ISSN 0306-2619

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (53)

What Can History Teach Us? A Retrospective Examination of Long-Term Energy Forecasts for the United States Craig, Paul P.; Gadgil, Ashok; Koomey, Jonathan G. Annual Review of Energy and the Environment, Vol. 27, Issue 1 https://doi.org/10.1146/annurev.energy.27.122001.083425	journal	November 2002
Optimization using surrogate models and partially converged computational fluid dynamics simulations Forrester, Alexander I. J.; Bressloff, Neil W.; Keane, Andy J. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 462, Issue 2071 https://doi.org/10.1098/rspa.2006.1679	journal	February 2006
Global optimization of multiphase flow networks using spline surrogate models Grimstad, Bjarne; Foss, Bjarne; Heddle, Richard Computers & Chemical Engineering, Vol. 84 https://doi.org/10.1016/j.compchemeng.2015.08.022	journal	January 2016
Factors That Control Condensate Production From Shales: Surrogate Reservoir Models and Uncertainty Analysis Panja, Palash; Deo, Milind SPE Reservoir Evaluation & Engineering, Vol. 19, Issue 01 https://doi.org/10.2118/179720-PA	journal	December 2015
Developing a Robust Surrogate Model of Chemical Flooding Based on the Artificial Neural Network for Enhanced Oil Recovery Implications Ahmadi, Mohammad Ali Mathematical Problems in Engineering, Vol. 2015 https://doi.org/10.1155/2015/706897	journal	January 2015
Recurrent neural networks and robust time series prediction Connor, J. T.; Martin, R. D.; Atlas, L. E. IEEE Transactions on Neural Networks, Vol. 5, Issue 2 https://doi.org/10.1109/72.279188	journal	March 1994
The role of CO2 capture and utilization in mitigating climate change Mac Dowell, Niall; Fennell, Paul S.; Shah, Nilay Nature Climate Change, Vol. 7, Issue 4 https://doi.org/10.1038/nclimate3231	journal	April 2017
Support Vector Machines Framework for Predicting the PVT Properties of Crude Oil Systems El-Sebakhy, Emad Ahmed; Sheltami, Tarek; Al-Bokhitan, Said Y. SPE Middle East Oil and Gas Show and Conference, All Days https://doi.org/10.2118/105698-MS	conference	March 2007
Determinants of carbon dioxide emissions: Empirical evidence from 69 countries Sharma, Susan Sunila Applied Energy, Vol. 88, Issue 1 https://doi.org/10.1016/j.apenergy.2010.07.022	journal	January 2011
Co-optimization of enhanced oil recovery and carbon sequestration Leach, Andrew; Mason, Charles F.; Veld, Klaas van ‘t Resource and Energy Economics, Vol. 33, Issue 4 https://doi.org/10.1016/j.reseneeco.2010.11.002	journal	November 2011
A multi-objective optimization for green supply chain network design Wang, Fan; Lai, Xiaofan; Shi, Ning Decision Support Systems, Vol. 51, Issue 2 https://doi.org/10.1016/j.dss.2010.11.020	journal	May 2011
IEA World Energy Outlook 2011—A comment Khatib, Hisham Energy Policy, Vol. 48 https://doi.org/10.1016/j.enpol.2012.06.007	journal	September 2012
Handling multiple objectives with particle swarm optimization Coello, C. A. C.; Pulido, G. T.; Lechuga, M. S. IEEE Transactions on Evolutionary Computation, Vol. 8, Issue 3 https://doi.org/10.1109/TEVC.2004.826067	journal	June 2004
Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion Ali Ahmadi, Mohammad; Golshadi, Mohammad Journal of Petroleum Science and Engineering, Vol. 98-99 https://doi.org/10.1016/j.petrol.2012.08.011	journal	November 2012
An algorithm for the use of surrogate models in modular flowsheet optimization Caballero, José A.; Grossmann, Ignacio E. AIChE Journal, Vol. 54, Issue 10 https://doi.org/10.1002/aic.11579	journal	October 2008
Optimum design of CO2 storage and oil recovery under geological uncertainty Ampomah, W.; Balch, R. S.; Cather, M. Applied Energy, Vol. 195 https://doi.org/10.1016/j.apenergy.2017.03.017	journal	June 2017
Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network Kim, Youngmin; Jang, Hochang; Kim, Junggyun Applied Energy, Vol. 185 https://doi.org/10.1016/j.apenergy.2016.10.012	journal	January 2017
Energy consumption and greenhouse gas emissions in the recovery and extraction of crude bitumen from Canada’s oil sands Nimana, Balwinder; Canter, Christina; Kumar, Amit Applied Energy, Vol. 143 https://doi.org/10.1016/j.apenergy.2015.01.024	journal	April 2015
The weighted sum method for multi-objective optimization: new insights Marler, R. Timothy; Arora, Jasbir S. Structural and Multidisciplinary Optimization, Vol. 41, Issue 6 https://doi.org/10.1007/s00158-009-0460-7	journal	December 2009
Opportunities for Using Anthropogenic CO ₂ for Enhanced Oil Recovery and CO ₂ Storage Godec, Michael L.; Kuuskraa, Vello A.; Dipietro, Phil Energy & Fuels, Vol. 27, Issue 8 https://doi.org/10.1021/ef302040u	journal	March 2013
Large CO2 Sinks: Their role in the mitigation of greenhouse gases from an international, national (Canadian) and provincial (Alberta) perspective Gunter, W. D.; Wong, S.; Cheel, D. B. Applied Energy, Vol. 61, Issue 4 https://doi.org/10.1016/S0306-2619(98)00042-7	journal	December 1998
A review of physical modelling and numerical simulation of long-term geological storage of CO2 Jiang, Xi Applied Energy, Vol. 88, Issue 11 https://doi.org/10.1016/j.apenergy.2011.05.004	journal	November 2011
Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption Azadeh, A.; Tarverdian, S. Energy Policy, Vol. 35, Issue 10 https://doi.org/10.1016/j.enpol.2007.04.020	journal	October 2007
Multiobjective Optimization of Industrial Petroleum Processing Units Using Genetic Algorithms Ivanov, Stanislav Y.; K. Ray, Ajay Procedia Chemistry, Vol. 10 https://doi.org/10.1016/j.proche.2014.10.003	journal	January 2014
Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth Say, Nuriye Peker; Yücel, Muzaffer Energy Policy, Vol. 34, Issue 18 https://doi.org/10.1016/j.enpol.2005.08.024	journal	December 2006
The ghost of unbalanced species distribution data in geographical model predictions Jimenez-Valverde, A.; Lobo, J. M. Diversity Distributions, Vol. 12, Issue 5 https://doi.org/10.1111/j.1366-9516.2006.00267.x	journal	September 2006
Heterogeneity-assisted carbon dioxide storage in marine sediments Dai, Zhenxue; Zhang, Ye; Bielicki, Jeffrey Applied Energy, Vol. 225 https://doi.org/10.1016/j.apenergy.2018.05.038	journal	September 2018
Review: Multi-objective optimization methods and application in energy saving Cui, Yunfei; Geng, Zhiqiang; Zhu, Qunxiong Energy, Vol. 125 https://doi.org/10.1016/j.energy.2017.02.174	journal	April 2017
Multi-objective global optimization for hydrologic models Yapo, Patrice Ogou; Gupta, Hoshin Vijai; Sorooshian, Soroosh Journal of Hydrology, Vol. 204, Issue 1-4 https://doi.org/10.1016/S0022-1694(97)00107-8	journal	January 1998
Forecasting of CO2 emissions from fuel combustion using trend analysis Köne, Aylin Çiğdem; Büke, Tayfun Renewable and Sustainable Energy Reviews, Vol. 14, Issue 9 https://doi.org/10.1016/j.rser.2010.06.006	journal	December 2010
Non-Dominated Ranked Based Genetic Algorithm Multi-Objective Well Placement Optimization Rostamian, A.; Jamshidi, S.; Kamari, M. 81st EAGE Conference and Exhibition 2019 https://doi.org/10.3997/2214-4609.201900746	conference	January 2019
Gradient-based multi-objective optimization with applications to waterflooding optimization Liu, Xin; Reynolds, Albert C. Computational Geosciences, Vol. 20, Issue 3 https://doi.org/10.1007/s10596-015-9523-6	journal	September 2015
Surrogate modeling-based optimization for the integration of static and dynamic data into a reservoir description Queipo, Nestor V.; Pintos, Salvador; Rincón, Néstor Journal of Petroleum Science and Engineering, Vol. 35, Issue 3-4 https://doi.org/10.1016/S0920-4105(02)00238-3	journal	August 2002
Building Surrogate Models Based on Detailed and Approximate Simulations Qian, Zhiguang; Seepersad, Carolyn Conner; Joseph, V. Roshan Journal of Mechanical Design, Vol. 128, Issue 4 https://doi.org/10.1115/1.2179459	journal	June 2005
Geologic Carbon Storage Through Enhanced Oil Recovery Hill, Bruce; Hovorka, Susan; Melzer, Steve Energy Procedia, Vol. 37 https://doi.org/10.1016/j.egypro.2013.06.614	journal	January 2013
Scenario analysis of CO2 emissions from China’s civil aviation industry through 2030 Zhou, Wenji; Wang, Tao; Yu, Yadong Applied Energy, Vol. 175 https://doi.org/10.1016/j.apenergy.2016.05.004	journal	August 2016
Prospects of carbon capture and storage (CCS) in India’s power sector – An integrated assessment Viebahn, Peter; Vallentin, Daniel; Höller, Samuel Applied Energy, Vol. 117 https://doi.org/10.1016/j.apenergy.2013.11.054	journal	March 2014
Generation of a Pareto front for a bi-objective water flooding optimization problem using approximate ensemble gradients Fonseca, Rahul M.; Reynolds, Albert C.; Jansen, Jan Dirk Journal of Petroleum Science and Engineering, Vol. 147 https://doi.org/10.1016/j.petrol.2016.06.009	journal	November 2016
Assessment of Enhanced Oil Recovery and CO2 Storage Capacity Using Machine Learning and Optimization Framework You, Junyu; Ampomah, William; Kutsienyo, Eusebius Junior SPE Europec featured at 81st EAGE Conference and Exhibition, Day 4 Thu, June 06, 2019 https://doi.org/10.2118/195490-MS	conference	June 2019
Numerical Simulation of Carbon Dioxide Injection in the Western Section of the Farnsworth Unit White, M. D.; McPherson, B. J.; Grigg, R. B. Energy Procedia, Vol. 63 https://doi.org/10.1016/j.egypro.2014.11.825	journal	January 2014
Experimental investigation of miscible and immiscible Water-Alternating-Gas (WAG) process performance Kulkarni, Madhav M.; Rao, Dandina N. Journal of Petroleum Science and Engineering, Vol. 48, Issue 1-2 https://doi.org/10.1016/j.petrol.2005.05.001	journal	July 2005
The Modeling and Projection of Primary Energy Consumption by the Sources Aydin, G. Energy Sources, Part B: Economics, Planning, and Policy, Vol. 10, Issue 1 https://doi.org/10.1080/15567249.2013.771716	journal	July 2014
Ensemble-Based Optimization of the Water-Alternating-Gas-Injection Process Chen, Bailian; Reynolds, Albert C. SPE Journal, Vol. 21, Issue 03 https://doi.org/10.2118/173217-PA	journal	June 2016
An integrated simulation, inference, and optimization method for identifying groundwater remediation strategies at petroleum-contaminated aquifers in western Canada He, Li; Huang, Guo-he; Zeng, Guang-ming Water Research, Vol. 42, Issue 10-11 https://doi.org/10.1016/j.watres.2008.01.012	journal	May 2008
Evaluation of CO2-EOR Performance and Storage Mechanisms in an Active Partially Depleted Oil Reservoir Kutsienyo, Eusebius Junior; Ampomah, William; Sun, Qian SPE Europec featured at 81st EAGE Conference and Exhibition, Day 4 Thu, June 06, 2019 https://doi.org/10.2118/195534-MS	conference	June 2019
Production Modeling in the Oil and Natural Gas Industry: An Application of Trend Analysis Aydin, G. Petroleum Science and Technology, Vol. 32, Issue 5 https://doi.org/10.1080/10916466.2013.825271	journal	January 2014
Handling conflicting multiple objectives using Pareto-based evolutionary algorithm during history matching of reservoir performance Park, Han-Young; Datta-Gupta, Akhil; King, Michael J. Journal of Petroleum Science and Engineering, Vol. 125 https://doi.org/10.1016/j.petrol.2014.11.006	journal	January 2015
Optimization of petroleum production networks – Through proxy models and structural constraints Shamlou, Sheri S.; Gunnerud, Vidar; Conn, Andrew IFAC Proceedings Volumes, Vol. 45, Issue 15 https://doi.org/10.3182/20120710-4-SG-2026.00021	journal	January 2012
A study on the impact of SO2 on CO2 injectivity for CO2 storage in a Canadian saline aquifer Wang, Zhiyu; Wang, Jinsheng; Lan, Christopher Applied Energy, Vol. 184 https://doi.org/10.1016/j.apenergy.2016.09.067	journal	December 2016
Scenario analysis of urban energy saving and carbon abatement policies: A case study of Beijing city, China Feng, Y. Y.; Zhang, L. X. Procedia Environmental Sciences, Vol. 13 https://doi.org/10.1016/j.proenv.2012.01.055	journal	January 2012
The Development and Validation of Regression Models to Predict Energy-related CO ₂ Emissions in Turkey Aydin, G. Energy Sources, Part B: Economics, Planning, and Policy, Vol. 10, Issue 2 https://doi.org/10.1080/15567249.2013.830662	journal	September 2014
CO ₂ Accounting and Risk Analysis for CO ₂ Sequestration at Enhanced Oil Recovery Sites Dai, Zhenxue; Viswanathan, Hari; Middleton, Richard Environmental Science & Technology, Vol. 50, Issue 14 https://doi.org/10.1021/acs.est.6b01744	journal	June 2016
Co-optimization of CO ₂ -EOR and storage processes in mature oil reservoirs : Original Research Article: Co-optimization of CO Ampomah, William; Balch, Robert S.; Grigg, Reid B. Greenhouse Gases: Science and Technology, Vol. 7, Issue 1 https://doi.org/10.1002/ghg.1618	journal	August 2016

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