ArticleEarly Access

An Intelligent Water Injection Decision-Making and Production Optimization Method Based on SAC Deep Reinforcement Learning

Tianyu Yang¹, Xiang Rao^1,^*, Shuhui Xu²

¹ College of Petroleum Engineering, Yangtze University, Wuhan, 430100, China.
²Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China.

^*Correspondence: Xiang Rao, Email: raoxiang0103@163.com

Received: 2026-04-21Accepted: 2026-05-22Published: 2026-06-25

AESIG 2026, 2(2), 17-33; https://doi.org/10.58244/aesig.263879

Abstract

To address the failure of static water injection strategies in late-stage oilfield development caused by strong reservoir heterogeneity, this paper proposes an intelligent water injection and dynamic production optimization method using Soft Actor-Critic (SAC) deep reinforcement learning. By formulating waterflooding optimization as a Markov Decision Process (MDP), a Finite Volume Method (FVM) simulator is dynamically coupled with a deep learning environment. Departing from traditional methods that rely solely on wellhead data, this model uses high-dimensional oil saturation images to capture the waterflood front’s topological evolution. A Convolutional Neural Network (CNN) extracts spatial features to output optimal real-time allocation weights for multiple injection wells, constrained by total injection volume. Tested on complex heterogeneous configurations—including “four-injector, five-producer” and “four-injector, nine-producer” patterns—the SAC agent demonstrated remarkable convergence stability and exploration efficiency. The model autonomously establishes an adaptive strategy that controls water cut, suppresses water channeling in high-permeability streaks, and intelligently redirects hydrodynamic energy to unswept zones. Compared to conventional uniform injection, this method significantly expands macroscopic sweep volume and reduces remaining oil saturation, offering a novel paradigm for the real-time, closed-loop management of complex reservoirs.

Keywords: Deep reinforcement learning; Soft Actor-Critic (SAC); Intelligent water injection decision-making; Closed-loop reservoir management; Spatial heterogeneity; Production optimization

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Funding

This research was no funding provided.

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