Recently, the team of Zhang Kai has made new progress in the field of waterflooding reservoir flow disequilibrium analysis, and the related research has been published in Water. The paper is titled " An Interpretable Recurrent Neural Network for Waterflooding Reservoir Flow Disequilibrium Analysis ".
Innovation: Traditional production optimization methods evaluate the quality of a solution by calling a reservoir numerical reservoir simulator and iteratively optimizes the optimal solution. The main limitation is that the number of numerical simulations evaluations is large and the optimization speed is difficult to meet the engineering needs. In this study, we model the reservoir production optimization problem as a Markovian decision process to build a deep reinforcement learning-based production optimization framework to address these challenges. The deep reinforcement learning algorithm is used to train an agent to continuously improve the solution decision making capability by dynamically interacting with the reservoir environment in a trial-and-error learning manner. The method achieves good results in terms of both efficiency and performance of optimization.
Abstract: Waterflooding is one of the most common reservoir development programs, driving the oil in porous media to the production wells by injecting high-pressure water into the reservoir. In the process of oil development, identifying the underground flow distribution, so as to take measures such as water plugging and profile control for high permeability layers to prevent water channeling, is of great importance for oilfield management. However, influenced by the heterogeneous geophysical properties of porous media, there is strong uncertainty in the underground flow distribution. In this paper, we propose an interpretable recurrent neural network (IRNN) based on the material balance equation, to characterize the flow disequilibrium and to predict the production behaviors. IRNN is constructed using two interpretable modules, where the inflow module aims to compute the total inflow rate from all injectors to each producer, and the drainage module is designed to approximate the fluid change rate among the water drainage volume. On the spatial scale, IRNN takes a self-attention mechanism to focus on the important input signals and to reduce the influence of the redundant information, so as to deal with the mutual interference between the injection–production groups efficiently. On the temporal scale, IRNN employs the recurrent neural network, taking into account the impact of historical injection signals on the current production behavior. In addition, a Gaussian kernel function with boundary constraints is embedded in IRNN to quantitatively characterize the inter-well flow disequilibrium. Through the verification of two synthetic experiments, IRNN outperforms the canonical multilayer perceptron on both the history match and the forecast of productivity, and it effectively reflects the subsurface flow disequilibrium between the injectors and the producers.
Water covers all aspects of water, including water science, technology, management and governance. Topics include: water resources management; water governance; hydrology and hydraulics; water scarcity; flood risk; water quality; water and wastewater treatment; urban water management; water footprint assessment; water-food; water-energy; water-human development; water-ecosystems, etc. The latest impact factor of the journal is 3.530, and the average impact factor IF in the past 5 years is 3.628. This journal currently appears in the JCR Q2 or the Chinese Academy of Sciences ranking 3.
Paper link:
https://doi.org/10.3390/w15040623
Citation:
Jiang Y, Shen W, Zhang H, et al. An Interpretable Recurrent Neural Network for Waterflooding Reservoir Flow Disequilibrium Analysis [J]. Water, 2023, 15(4), 623.
