Automated Reservoir History Matching Framework: Integrating Graph Neural Networks, Transformer, and Optimization for Enhanced Interwell Connectivity Inversion

Understanding interwell connectivity during water-flooding reservoir development is crucial for analyzing the characteristics of remaining oil and optimizing technical measures. The key lies in establishing an inversion method to identify interwell connectivity. However, traditional history matching...

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Bibliographic Details
Published in:Processes Vol. 13; no. 5; p. 1386
Main Authors: Liu, Botao, Xu, Tengbo, Xu, Yunfeng, Zhao, Hui, Li, Bo
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.05.2025
Subjects:
Accuracy
Adaptability
Algorithms
Capacitance
Computational linguistics
Connectivity
Deep learning
Efficiency
Electric transformers
Embedding
Evolution
Evolutionary computation
Geology
Graph neural networks
Graphical representations
Identification methods
Language processing
Li Bai
Lithology
Matching
Mathematical models
Mathematical optimization
Methods
Natural language interfaces
Neural networks
Numerical analysis
Optimization
Particle swarm optimization
Reservoirs
Simulation
Technology application
Water flooding
ISSN:2227-9717, 2227-9717
Online Access:Get full text
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Summary:Understanding interwell connectivity during water-flooding reservoir development is crucial for analyzing the characteristics of remaining oil and optimizing technical measures. The key lies in establishing an inversion method to identify interwell connectivity. However, traditional history matching methods based on numerical simulation suffer from high computational costs and limited adaptability to complex spatiotemporal dependencies in production data. To address these challenges, this study combines a surrogate model trained using a graph neural network (GNN) and Transformer encoder with a differential evolution particle swarm optimization (DEPSO) algorithm for automated reservoir history matching. The surrogate model is constructed by embedding the capacitance–resistance model (CRM) into a graph structure, where wells are represented as nodes and interwell connectivity parameters as edge features. When applied to the conceptual model, the coefficient of determination (R2) was found to be approximately 0.95 during the training phase by comparing the production data predicted by the surrogate model with the actual observed data. The DEPSO algorithm aimed to minimize the differences between surrogate predictions and observed data, achieving good fitting results. When applied to a complex case study, the average water-cut fitting rate for each production well in its well group reached 87.8%. The results indicate that this method significantly improves fitting accuracy and has substantial practical value.
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr13051386