Machine Learning Assisted Reservoir Management for Injection Projects

dc.contributor.advisorThakur, Ganesh C.
dc.contributor.committeeMemberFarouq Ali, S. M.
dc.contributor.committeeMemberAminzadeh, Fred
dc.contributor.committeeMemberLee, Kyung Jae
dc.contributor.committeeMemberWesley, Avinash
dc.creatorSelveindran, Anand 2021 2021
dc.description.abstractReservoir management is critical for optimal hydrocarbon reservoir performance. A key component of reservoir management is decision making utilizing numerical reservoir models. One downside of these models is the large computational footprint for development and deployment. Recent developments in machine learning provide technologies that can augment reservoir management workflows. In this work, machine learning algorithms are used to optimize selection of injector well location, perform fluid production prediction and optimize well controls. Selection of optimized water and gas injection well locations is a key challenge in secondary and tertiary recovery projects. Traditional approaches using numerical simulation require a high number of simulation runs with optimization algorithms. This is further complicated if there is geological uncertainty (e.g., multiple geological models are required to capture the uncertainty range). An alternative approach is proposed, using machine learning algorithms trained on a geological ensemble. Well level aggregations are proposed to efficiently reduce the number of injector well location evaluations. Proxies were trained with several target variables including oil recovery and CO2 storage. These proxies provide rapid evaluations of injector locations within seconds at accuracies between 0.94 - 0.98 (R2 score comparing machine learning proxy predictions and numerical simulation results). Blind tests with different geological realizations yielded results comparable to numerical simulation. Posterior sampling was used to determine optimal injector locations across a large geomodel ensemble. To predict time varying production without numerical simulation, deep learning models including a Convolutional Neural Network (CNN) were trained with well level data. These algorithms successfully predicted production from water and miscible flooded patterns. Models trained on several wells could predict production from a neighbor well within the same pattern area. Finally, a Graph Neural Network (GNN) proxy was used within a Closed Loop Reservoir Management (CLRM) workflow to optimize oil production of a waterflood. Kriging was used to initialize the adjacency matrix, resulting in improved network performance. The GNN proxy was able to perform monthly optimization over a period of one year within eight minutes, compared to 6 hours using traditional simulation.
dc.description.departmentPetroleum Engineering, Department of
dc.format.digitalOriginborn digital
dc.identifier.citationPortions of this document appear in: Selveindran, A., Zargar, Z., Razavi, S. M., & Thakur, G. (2021). Fast Optimization of Injector Selection for Waterflood, CO2-EOR and Storage Using an Innovative Machine Learning Framework. Energies, 14(22), 7628.
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectReservoir Management
dc.subjectPetroleum Production prediction
dc.subjectMachine Learning
dc.subjectInjector Selection
dc.subjectInjector optimization
dc.subjectCO2 EOR
dc.subjectCarbon Storage
dc.subjectGraph neural network
dc.subjectClosed loop reservoir management
dc.subjectproduction forecasting
dc.subjectoil and gas
dc.titleMachine Learning Assisted Reservoir Management for Injection Projects
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
local.embargo.terms2023-12-01 College of Engineering Engineering, Department of Engineering of Houston of Philosophy


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