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Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media
Open AccessArticle

Data-Driven Model Reduction for Coupled Flow and Geomechanics Based on DMD Methods

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Harold Vance Department of Petroleum Engineering, Texas A&M University, College Station, TX 77843, USA
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Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
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Author to whom correspondence should be addressed.
Fluids 2019, 4(3), 138; https://doi.org/10.3390/fluids4030138
Received: 3 May 2019 / Revised: 2 July 2019 / Accepted: 12 July 2019 / Published: 19 July 2019
Learning reservoir flow dynamics is of primary importance in creating robust predictive models for reservoir management including hydraulic fracturing processes. Physics-based models are to a certain extent exact, but they entail heavy computational infrastructure for simulating a wide variety of parameters and production scenarios. Reduced-order models offer computational advantages without compromising solution accuracy, especially if they can assimilate large volumes of production data without having to reconstruct the original model (data-driven models). Dynamic mode decomposition (DMD) entails the extraction of relevant spatial structure (modes) based on data (snapshots) that can be used to predict the behavior of reservoir fluid flow in porous media. In this paper, we will further enhance the application of the DMD, by introducing sparse DMD and local DMD. The former is particularly useful when there is a limited number of sparse measurements as in the case of reservoir simulation, and the latter can improve the accuracy of developed DMD models when the process dynamics show a moving boundary behavior like hydraulic fracturing. For demonstration purposes, we first show the methodology applied to (flow only) single- and two-phase reservoir models using the SPE10 benchmark. Both online and offline processes will be used for evaluation. We observe that we only require a few DMD modes, which are determined by the sparse DMD structure, to capture the behavior of the reservoir models. Then, we applied the local DMDc for creating a proxy for application in a hydraulic fracturing process. We also assessed the trade-offs between problem size and computational time for each reservoir model. The novelty of our method is the application of sparse DMD and local DMDc, which is a data-driven technique for fast and accurate simulations. View Full-Text
Keywords: sparsity promoting; dynamic mode decomposition; model order reduction; reservoir simulation; hydraulic fracturing sparsity promoting; dynamic mode decomposition; model order reduction; reservoir simulation; hydraulic fracturing
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Bao, A.; Gildin, E.; Narasingam, A.; Kwon, J.S. Data-Driven Model Reduction for Coupled Flow and Geomechanics Based on DMD Methods. Fluids 2019, 4, 138.

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