Shale Gas Production Forecasting with Well Interference Based on Spatial-Temporal Graph Convolutional Network

Deep-learning models have been employed for production forecasting in oil and gas reservoirs, but they often assume that each well operates independently, neglecting the connectivity and dynamic interactions between wells. This simplification can significantly compromise prediction accuracy. Therefore, graph convolutional networks (GCNs) have been applied to incorporate data from neighbouring wells. However, existing spatial-temporal GCN (ST-GCN) methods are mainly used for autoregressive tasks and face limitations in predicting newly developed wells and fully utilizing temporal neighbour interactions. This study introduces an ST-graph- level feature embedding (ST-GFE) method that provides accurate production forecasting for newly developed wells. It enhances forecasting by aggregating the historical data from neighbouring wells into a single feature vector. This aggregated vector, merging local and contextual information, contains richer information about the studied region. We evaluate ST-GFE using a dataset of 6,605 Montney shale gas wells, incorporating formation properties, fracture parameters, and production history. The ST-GFE is integrated with a non-autoregressive encoder-decoder structure to do production forecasting. The findings demonstrate that ST-GFE significantly improves prediction accuracy for newly developed wells compared to the purely temporal models, such as recurrent neural network (RNN)-based and Transformer models. ST-GFE adapts to production changes in adjacent wells, providing accurate predictions across various application scenarios, including shut-in and in-fill drilling activities. Additionally, while traditional GCNs require a full-batch training approach that leads to scalability issues, the ST-GFE model treats each well and its surrounding wells as a graph, enabling batch training and significantly reducing memory usage. Furthermore, the model dynamically updates its forecasts with real-time production data, enhancing precision and relevance. Experimental results confirm that ST-GFE effectively leverages spatio-temporal dynamics and interactions between adjacent wells, further improving production forecasting accuracy. This method enhances predictions and generalization capabilities for new developing locations, broadening its applicability to various drilling and production scenarios.

show abstract

Dynamic Real-Time Production Forecasting Model for Complex Subsurface Flow Systems with Variable Length Input Sequences

Xu,

Leung

2024

SPE Journal

View full text Add to dashboard Cite

Summary Production time-series forecasting for newly drilled wells or those with limited flow and pressure historical data poses a significant challenge, and this problem is exacerbated by the complexities and uncertainties encountered in fractured subsurface systems. While many existing models rely on static features for prediction, the production data progressively offer more informative insights as production unfolds. Leveraging ongoing production data can enhance forecasting accuracy over time. However, effectively integrating the production stream data presents significant model training and updating complexities. We propose two innovative methods to address this challenge: masked recurrent alignment (MRA) and masked encoding decoding (MED). These methods enable the model to continually update its predictions based on historical data. In addition, by incorporating sequence padding and masking, our model can handle inputs of varying lengths without trimming, thereby avoiding the potential loss of valuable training samples. We implement these models with gated recurrent unit (GRU) and evaluate their performance in a case study involving 6,154 shale gas wells in the Central Montney Region. The data set encompasses 39 production-related features, including reservoir properties, completion, and wellhead information. Performance evaluation is based on root mean square error (RMSE) to predict 36-month production from 200 wells during testing. Empirical findings highlight the efficacy of the proposed models in handling challenges associated with variable-length input sequences, showcasing their superior performance. Our research emphasizes the value of including shorter time-series segments, often overlooked, to improve predictive accuracy, especially in scenarios with limited training samples.

show abstract

Shale Gas Production Forecasting with Well Interference Based on Spatial-Temporal Graph Convolutional Network

Cited by 3 publications

References 20 publications

A novel formulation of RNN-based neural network with real-time updating – An application for dynamic hydraulic fractured shale gas production forecasting

A novel formulation of RNN-based neural network with real-time updating – An application for dynamic hydraulic fractured shale gas production forecasting

Graph-Level Feature Embedding with Spatial– Temporal GCN Method for Interconnected Well Production Forecasting

Dynamic Real-Time Production Forecasting Model for Complex Subsurface Flow Systems with Variable Length Input Sequences

Contact Info

Product

Resources

About