Assessment of Enhanced Oil Recovery and CO2 Storage Capacity Using Machine Learning and Optimization Framework
- Junyu You (Petoleum Recovery Research Center) | William Ampomah (Petoleum Recovery Research Center) | Eusebius Junior Kutsienyo (Petoleum Recovery Research Center) | Qian Sun (Petoleum Recovery Research Center) | Robert Scott Balch (Petoleum Recovery Research Center) | Wilberforce Nkrumah Aggrey (KNUST) | Martha Cather (Petoleum Recovery Research Center)
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- Society of Petroleum Engineers
- SPE Europec featured at 81st EAGE Conference and Exhibition, 3-6 June, London, England, UK
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- 5.5 Reservoir Simulation, 6.1 HSSE & Social Responsibility Management, 5.4 Improved and Enhanced Recovery, 5.7.2 Recovery Factors, 7.6 Information Management and Systems, 5 Reservoir Desciption & Dynamics, 5.5.8 History Matching, 5.7 Reserves Evaluation, 6.1.5 Human Resources, Competence and Training, 5.1.10 Reservoir Geomechanics, 7.6.6 Artificial Intelligence, 7 Management and Information, 0.2 Wellbore Design, 7.6.7 Neural Networks, 5.4 Improved and Enhanced Recovery, 6 Health, Safety, Security, Environment and Social Responsibility, 5.1 Reservoir Characterisation
- Compositional Simulation, Carbon dioxide Sequestration, Water Alternating Gas (WAG), Proxy Models, Optimization
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- 149 since 2007
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This paper presents an optimization methodology on field-scale numerical compositional simulations of CO2 storage and production performance in the Pennsylvanian Upper Morrow sandstone reservoir in the Farnsworth Unit (FWU), Ochiltree County, Texas. This work develops an improved framework that combines hybridized machine learning algorithms for reduced order modeling and optimization techniques to co-optimize field performance and CO2 storage.
The model's framework incorporates geological, geophysical, and engineering data. We calibrated the model with the performance history of an active CO2 flood data to attain a successful history matched model. Uncertain parameters such as reservoir rock properties and relative permeability exponents were adjusted to incorporate potential changes in wettability in our history matched model.
To optimize the objective function which incorporates parameters such as oil recovery factor, CO2 storage and net present value, a proxy model was generated with hybridized multi-layer and radial basis function (RBF) Neural Network methods. To obtain a reliable and robust proxy, the proxy underwent a series of training and calibration runs, an iterative process, until the proxy model reached the specified validation criteria. Once an accepted proxy was realized, hybrid evolutionary and machine learning optimization algorithms were utilized to attain an optimum solution for pre-defined objective function. The uncertain variables and/or control variables used for the optimization study included, gas oil ratio, water alternating gas (WAG) cycle, production rates, bottom hole pressure of producers and injectors. CO2 purchased volume, and recycled gas volume in addition to placement of new infill wells were also considered in the modelling process.
The results from the sensitivity analysis reflect impacts of the control variables on the optimum results. The predictive study suggests that it is possible to develop a robust machine learning optimization algorithm that is reliable for optimizing a developmental strategy to maximize both oil production and storage of CO2 in aqueous-gaseous-mineral phases within the FWU.
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