Production Disentanglement and Exogenous Analysis Transformer for Petroleum Production Prediction

Petroleum production prediction is a crucial task in reservoir monitoring and management, providing an indispensable basis for development scheme optimization. Most existing production prediction methods learn a joint representation for production dynamics using deep neural networks. However, these methods ignore the special temporal patterns of production dynamics and do not quantitatively analyze the impact of exogenous engineering measures on production. This may lead to insufficient generalization and guidance of the model in practical applications. To address these limitations, we propose a novel production analysis transformer for petroleum production prediction. Specifically, we leverage position enhancement and long-term interaction to explicitly capture the distinctive temporal patterns of production dynamics including local fluctuation and trend features. Furthermore, we establish covariate-level attention to evaluate the dynamic contribution of exogenous engineering measures to production. In this process, we design an adaptive aggregate mechanism that enables multi-head attention to generate a single attention response, thereby improving the legibility of attention maps in engineering applications while maintaining the diversity of feature learning. With this task-specific design, our model learns disentangled production patterns and quantified exogenous influences, thereby enabling accurate and insightful production prediction. Comprehensive experiments on two real-world datasets demonstrate the advantages of our method in production prediction and its significance in engineering guidance.