A method for analyzing interwell connectivity based on gated recurrent network with knowledge interaction
Year: 2025
Type: article
Abstract: Traditional interwell connectivity analysis methods for water-flooding reservoirs suffer from two major limitations: insufficient integration of seepage physics, leading to poor interpretability, and inadequate temporal modeling, which fails to capture the dynamic evolution of injection–production relationships. To overcome these issues, this study proposes a Knowledge-Interactive Gated Recurrent Unit (KIGRU) model that integrates physical constraints with temporal deep learning. The model adopts a dual-subnet architecture: Net-INJ encodes injection rates and interwell connectivity through gate functions and connection matrices, while Net-VOL characterizes reservoir volume changes. By embedding material balance equations into the network design, the model ensures physical consistency, while GRU modules effectively capture long-term temporal dependencies. Numerical experiments on synthetic reservoir cases demonstrate that KIGRU outperforms conventional neural networks and the Capacitance-Resistance Model (CRM) in both history matching and production forecasting. The model accurately identifies high-permeability channels, quantifies non-equilibrium flow, and yields more reliable predictions of liquid production rates. These results confirm that KIGRU achieves a balance between physical interpretability and predictive accuracy, offering a practical and theoretically sound tool for interwell connectivity analysis.
Source: Frontiers in Earth Science
Authors: Yingchun Ji, Liming Zhang
Institutions: Sinopec (China), China University of Petroleum, East China
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4415959498