Artificial Intelligence-Based Oil Well Condition Diagnosis and Production Parameter Optimization

To address the challenges of low production efficiency, high energy consumption, and frequent equipment failures in low-producing wells, this study proposes an intelligent production parameter optimization method based on deep learning and multi-indicator fusion. First, a Long short-term memory (LSTM)-based prediction model was developed using the displacement–load characteristics of pumping-unit dynamometer cards. The results show that the model achieves an average prediction metric Q = 0.08, outperforming Back Propagation and Recurrent Neutral Network (BP and RNN) models. Second, a Convolutional Neutral Network (CNN) was employed to extract the fluid supply capability features, achieving a recognition accuracy exceeding 98%, thereby validating the model’s effectiveness. Combined with three types of time series data (liquid supply degree, dynamic liquid level, and liquid production rate), a multi-index fusion parameter optimization method is proposed. A comprehensive decision-making model is constructed based on the Analytic Hierarchy Process (AHP), which takes long-term, short-term, and overall feature changes as the basis, and forms a hierarchical framework by decomposing objectives and quantifying feature weights. The consistency ratio (CR) of the model is less than 0.1, meeting the requirement of logical consistency and enabling the output of standardized regulation suggestions. Consequently, a closed-loop system of “data preprocessing—condition prediction—state identification—parameter optimization” was constructed, enabling early dynamometer card prediction, accurate fluid supply fluctuation identification, and automatic generation of optimization schemes. This system effectively enhances production efficiency and equipment stability in low-producing wells, providing a technical foundation for intelligent oilfield development.