Research on inter-well connectivity analysis method based on the fusion of numerical models and graph neural networks

Inter-well connectivity has become one of the important criteria for guiding the development of water drive reservoirs. Traditional methods for predicting inter-well connectivity, such as tracer analysis, well testing analysis, numerical simulation, etc, have problems such as computational difficulties, cumbersome processes, and high costs. However, deep learning based methods suffer from issues such as data sensitivity and poor adaptability. To address the aforementioned challenges and problems, this paper proposes a prediction method for predicting inter well connectivity by fusing numerical models with graph neural networks. On the one hand, this method fully considers the physical parameters related to the injection and production well network in the production process, which derives a numerical model of inter-well connectivity considering multiple factors, and solves the problem of single form in previous numerical models. On the other hand, the similarity between well network structure and graph structure was reasonably utilized, a graph neural network model based on long and short-term memory neural network was designed. Then, a fusion method with deep learning network model was proposed, which was fused with numerical model. This solves the problem of traditional artificial intelligence methods ignoring physical parameters, and introduces self attention mechanism to optimize the model under the long and short-term memory neural network framework. The mechanism model and actual reservoir production data were used to predict inter-well connectivity and fluid production on the established model, and new development plans were formulated based on the predicted results. The model’s performance was verified through several sets of experiments. The results show that when the model established in this paper is used to predict inter-well connectivity, and the liquid production is calculated based on the connectivity prediction results, the prediction accuracy is as high as 98%, indicating that the model’s prediction results are reliable. Using the fusion model to predict inter-well connectivity of different sub-layers in the reservoir, it is found that the model’s prediction accuracy reaches over 95% in well patterns of different scales, showing strong applicability. Finally, the production plan was adjusted based on the connectivity prediction results: liquid reduction was implemented for both wells with high connectivity and those with low connectivity. Comparison shows that the predicted recovery degree after 10 years of the adjusted development plan is 6.8% higher than that of the original plan. This method balances physical interpretability and computational efficiency, providing reliable technical support for judging the development effect of water-flooded reservoirs and designing secondary development plans.