基于时空图卷积网络的页岩气产量预测与油井干扰

SPE Journal Pub Date : 2024-07-01 DOI:10.2118/215056-pa
Ziming Xu, Juliana Y. Leung
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引用次数: 0

摘要

大多数基于深度学习的数据驱动模型的核心假设之一是样本是独立的。然而,这一假设给产量预测带来了关键挑战--性能受到油井干扰和储层连通性的影响。大多数页岩气井都是水力压裂的,存在于复杂的裂缝系统中,在构建数据驱动预测模型时还应考虑邻井特征。研究人员已经探索使用图卷积网络(GCN)解决这一问题,将邻井特征纳入产量预测模型。然而,将 GCN 应用于油田规模的研究是有问题的,因为它需要对全批数据进行训练,从而导致巨大的缓存分配。此外,GCN 的传导性也给直接推广到未见节点带来了挑战。为了克服这些限制,我们采用了图采样和聚合(GraphSAGE)网络架构,该架构允许批量训练大型图,并对之前未见的节点进行泛化预测。通过利用门控递归单元(GRU)网络,所提出的空间-时间(ST)-GraphSAGE 模型可以捕捉目标井与邻井之间的跨时间关系,并为目标井生成有前景的预测时间序列,即使这些井是新钻井。利用来自 2240 口蒙特尼页岩气井的现场数据,包括地层属性、水力压裂参数、生产历史和运行数据,对所提出的方法进行了验证和测试。该算法汇总了每个时间步到目标节点的第一跳信息。利用编码器-解码器(ED)架构,通过油井 1 年的生产历史,生成对后续 3 年生产率的预测。经过训练的模型可对任何地点新开发油井的产量预测进行评估。我们使用测试数据集均方根误差 (RMSE) 的 P10、P50 和 P90 来评估模型的性能。我们的方法保留了油井的拓扑特征,并将预测泛化到未见的节点,同时大大降低了训练复杂度,使其适用于更大的数据集。通过整合相邻油井的信息和 ST 数据,我们的 ST-GraphSAGE 模型优于传统的 GRU-ED 模型,并显示出更强的可解释性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Shale Gas Production Forecasting with Well Interference Based on Spatial-Temporal Graph Convolutional Network
One of the core assumptions of most deep-learning-based data-driven models is that samples are independent. However, this assumption poses a key challenge in production forecasting—performance is influenced by well interference and reservoir connectivity. Most shale gas wells are hydraulically fractured and exist in complex fracture systems, and the neighboring well characteristics should also be considered when constructing data-driven forecast models. Researchers have explored using the graph convolutional network (GCN) to address this issue by incorporating neighboring well characteristics into production forecasting models. However, applying GCN to field-scale studies is problematic, as it requires training on a full batch, leading to gigantic cache allocation. In addition, the transductive nature of GCN poses challenges for direct generalization to unseen nodes. To overcome these limitations, we adopt the graph sampling and aggregation (GraphSAGE) network architecture, which allows training large graphs with batches and generalizing predictions for previously unseen nodes. By utilizing the gated recurrent unit (GRU) network, the proposed spatial-temporal (ST)-GraphSAGE model can capture cross-time relationships between the target and the neighboring wells and generate promising prediction time series for the target wells, even if they are newly drilled wells. The proposed approach is validated and tested using the field data from 2,240 Montney shale gas wells, including formation properties, hydraulic fracture parameters, production history, and operational data. The algorithm aggregates the first-hop information to the target node for each timestep. The encoder-decoder (ED) architecture is used to generate forecasts for the subsequent 3-year production rate by using the 1-year production history of the wells. The trained model enables the evaluation of production predictions for newly developed wells at any location. We evaluate the model’s performance using P10, P50, and P90 of the test data set’s root mean square error (RMSE). Our method preserves the topological characteristics of wells and generalizes the prediction to unseen nodes while significantly reducing training complexity, making it applicable to larger data sets. By incorporating information from adjacent wells and integrating ST data, our ST-GraphSAGE model outperforms the traditional GRU-ED model and shows enhanced interpretability.
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