Modeling fracture transient flow using the Embedded Discrete Fracture Model with nested local grid refinement

Xu, Yifei; Sepehrnoori, Kamy

doi:10.1016/j.petrol.2022.110882

Cited by 10 publications

(1 citation statement)

References 39 publications

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“…In this paper, we propose a deep graph learning-based surrogate model for the numerical simulation of fractured reservoirs with a multi-scale fracture network. The numerical simulation model adopted in this work is the embedded discrete fracture model (EDFM) [13,14], which is popular due to its ability to strike a balance between computational efficiency and accuracy. This new surrogate model is referred to as the GAT-LSTM, in which a graph attention (GAT) model [15,16] and an LSTM model [17,18] cooperatively emulate the EDFM simulation process.…”

Section: Introductionmentioning

confidence: 99%

Deep Graph Learning-Based Surrogate Model for Inverse Modeling of Fractured Reservoirs

Ma,

Zhao,

Zhou

et al. 2024

Mathematics

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Inverse modeling can estimate uncertain parameters in subsurface reservoirs and provide reliable numerical models for reservoir development and management. The traditional simulation-based inversion method usually requires numerous numerical simulations, which is time-consuming. Recently, deep learning-based surrogate models have been widely studied as an alternative to numerical simulation, which can significantly improve the solving efficiency of inversion. However, for reservoirs with complex fracture distribution, constructing the surrogate model of numerical simulation presents a significant challenge. In this work, we present a deep graph learning-based surrogate model for inverse modeling of fractured reservoirs. Specifically, the proposed surrogate model integrates the graph attention mechanisms to extract features of fracture network in reservoirs. The graph learning can retain the discrete characteristics and structural information of the fracture network. The extracted features are subsequently integrated with a multi-layer recurrent neural network model to predict the production dynamics of wells. A surrogate-based inverse modeling workflow is then developed by combining the surrogate model with the differential evolutionary algorithm. Numerical studies performed on a synthetic naturally fractured reservoir model with multi-scale fractures illustrate the performance of the proposed methods. The results demonstrate that the proposed surrogate model exhibits promising generalization performance of production prediction. Compared with tens of thousands of numerical simulations required by the simulation-based inverse modeling method, the proposed surrogate-based method only requires 1000 to 1500 numerical simulations, and the solution efficiency can be improved by ten times.

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