Open Access

ARTICLE

SGP-GCN: A Spatial-Geological Perception Graph Convolutional Neural Network for Long-Term Petroleum Production Forecasting

Xin Liu^1,*, Meng Sun¹, Bo Lin², Shibo Gu¹

1 Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
2 Offshore Oil Production Plant, Shengli Oilfield Branch Company, SINOPEC, Dongying, 257237, China

* Corresponding Author: Xin Liu. Email:

Energy Engineering 2025, 122(3), 1053-1072. https://doi.org/10.32604/ee.2025.060489

Received 02 November 2024; Accepted 21 January 2025; Issue published 07 March 2025

Abstract

Long-term petroleum production forecasting is essential for the effective development and management of oilfields. Due to its ability to extract complex patterns, deep learning has gained popularity for production forecasting. However, existing deep learning models frequently overlook the selective utilization of information from other production wells, resulting in suboptimal performance in long-term production forecasting across multiple wells. To achieve accurate long-term petroleum production forecast, we propose a spatial-geological perception graph convolutional neural network (SGP-GCN) that accounts for the temporal, spatial, and geological dependencies inherent in petroleum production. Utilizing the attention mechanism, the SGP-GCN effectively captures intricate correlations within production and geological data, forming the representations of each production well. Based on the spatial distances and geological feature correlations, we construct a spatial-geological matrix as the weight matrix to enable differential utilization of information from other wells. Additionally, a matrix sparsification algorithm based on production clustering (SPC) is also proposed to optimize the weight distribution within the spatial-geological matrix, thereby enhancing long-term forecasting performance. Empirical evaluations have shown that the SGP-GCN outperforms existing deep learning models, such as CNN-LSTM-SA, in long-term petroleum production forecasting. This demonstrates the potential of the SGP-GCN as a valuable tool for long-term petroleum production forecasting across multiple wells.

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Keywords

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