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In the traditional well log depth matching tasks, manual adjustments are required, which means significantly labor-intensive for multiple wells, leading to low work efficiency. This paper introduces a multi-agent deep reinforcement learning (MARL) method to automate the depth matching of multi-well logs. This method defines multiple top-down dual sliding windows based on the convolutional neural network (CNN) to extract and capture similar feature sequences on well logs, and it establishes an interaction mechanism between agents and the environment to control the depth matching process. Specifically, the agent selects an action to translate or scale the feature sequence based on the double deep Q-network (DDQN). Through the feedback of the reward signal, it evaluates the effectiveness of each action, aiming to obtain the optimal strategy and improve the accuracy of the matching task. Our experiments show that MARL can automatically perform depth matches for well-logs in multiple wells, and reduce manual intervention. In the application to the oil field, a comparative analysis of dynamic time warping (DTW), deep Q-learning network (DQN), and DDQN methods revealed that the DDQN algorithm, with its dual-network evaluation mechanism, significantly improves performance by identifying and aligning more details in the well log feature sequences, thus achieving higher depth matching accuracy. |
