Summary
Accurate prediction of oil production is crucial for formulating oilfield development strategies. With the rapid development of artificial intelligence, research on utilizing deep learning to construct oil production prediction models has been growing, which has partially compensated for the low computational efficiency of numerical simulators. Although the well-trained source domain model maintains high prediction accuracy on target blocks with similar production conditions, the prediction accuracy of the model declines in scenarios where substantial disparities exist between the production conditions of the target block and the source domain. This discrepancy makes the prediction results unreliable and causes a domain shift issue. We propose a multisource model fine-tuning approach, which leverages a limited amount of target domain data to fine-tune the existing source domain model, enabling it to rapidly converge in the target domain while maintaining superior prediction performance. Based on a heterogeneous low-permeability CO2-flooding reservoir development model, we established a series of source domain data sets, encompassing numerous types of well patterns and permeability fields, and specifically prepared various target domain data sets to verify the effectiveness of the model fine-tuning. Experimental outcomes demonstrate that our proposed model fine-tuning approach facilitates the rapid convergence of the existing model on target domain data. Following testing, the fine-tuned model, which attained a prediction accuracy exceeding 97% in the target domain, significantly improved upon the accuracy compared with the unfine-tuned model. The time required is significantly lower than retraining a new model, and it significantly reduces the need for data in the target domain. This provides support for the rapid generation of new prediction models using existing source domain models and limited target domain data.
