Research on Interpretation Method of Oil–Water Two-Phase Production Profile Using Artificial Intelligence Algorithm

The oil field enters a low-production liquid and high-water-cut stage, where the oil–water two-phase flow becomes increasingly complex and diverse. Traditional production profile logging interpretation methods often face significant errors and limitations. To improve interpretation accuracy, this study begins by examining the impact of flow rate and water cut on the oil–water two-phase flow pattern (defined as the characteristic distribution and movement of oil and water phases in the flow, which varies depending on flow conditions such as flow rate and water cut) through numerical simulations and surface experimental observations. The flow characteristics of the oil–water two-phase flow are clarified. Next, the data from surface experiments are collected using a multi-component logging tool, and artificial intelligence algorithms are employed to identify flow patterns and provide data support for production profile interpretation. The genetic algorithm–backpropagation (GA-BP) algorithm is used for flow type classification, with the flow pattern recognition accuracy reaching 93.75% when compared to the experimental results. Finally, the surface experimental data and flow patterns are input into the grey wolf and falcon optimization algorithm–radial basis function (GHOA-RBF) algorithm for training and prediction. The results show that the GHOA-RBF algorithm, incorporating flow patterns, exhibits superior prediction accuracy. Specifically, the coefficient of determination (R2) for oil flow is 0.996, and for water flow, it is 0.993, outperforming traditional RBF neural networks and the GHOA-RBF algorithm without flow pattern incorporation. This demonstrates that this study provides new theoretical support for production profile logging interpretation, with significant practical implications. However, limitations include the reliance on experimental data, which may not fully capture all field conditions, and the computational efficiency of the algorithm, which may need optimization for large-scale applications.