Abstract:

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.

Key words: water flooding reservoir, inter-well connectivity, numerical model, graph neural network, development plan adjustment

摘要：

井间连通性已成为指导水驱油藏开发的重要依据之一。传统井间连通性预测方法，如示踪剂分析、试井分析、数值模拟等，存在计算困难、过程繁琐、费用高等问题，而基于深度学习的方法又存在数据敏感、适应性差等问题。为应对上述挑战，本文提出了一种融合数值模型与图神经网络的井间连通性预测方法。该方法一方面充分考虑了生产过程中与注采井网相关的物理参数，推导了考虑多项因素的井间连通性数值模型，解决了以往数值模型形式单一的问题；另一方面合理利用了井网结构与图结构的相似性，设计了以长短期记忆神经网络为基础模型的图神经网络模型，并提出数值模型与深度学习模型的融合方法，解决了传统人工智能方法忽略物理参数的问题，并在长短期记忆神经网络框架下引入自注意力机制优化模型，应用所建融合模型，结合机理模型与油藏实际生产数据，完成了井间连通性及产液量的同步预测，并据此制定新的开发方案。多组实验验证表明，该模型对井间连通性的预测准确率高，基于连通性结果进一步计算的产液量预测值准确率可达98%，证实了模型的可靠性。用融合模型预测油藏不同小层的井间连通性，发现模型在不同规模的井网上的预测准确率都达到了95%以上，具有较强的适用性。最后基于连通性预测结果对生产方案进行调整，对连通性高的井降液，对连通性较低的井增液。对比发现，调整后的开发方案相较于原始方案，预测10年后的采出程度提高了6.8%。该方法兼顾物理可解释性与计算效率，为水驱油藏开采效果判断和二次开发方案设计提供了技术参考。

关键词: 水驱油藏, 井间连通性, 数值模型, 图神经网络, 开发方案调整

CLC Number: 

• P618.13

• TE3