基于 PSO-BP 神经网络的裂缝-岩浆储层产能预测方法

Energies Pub Date : 2024-07-15 DOI:10.3390/en17143482
Kunming Tian, Zhihong Kang, Zhijiang Kang
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引用次数: 0

摘要

储层产能预测是油气田开发的关键环节,快速准确地评价储层产能对评价油田开发潜力、提高油田开发效率具有重要作用。裂缝-岩浆储层具有异质性强、分布复杂、发育不规则等特点,给裂缝-岩浆储层产能的高效预测带来了很大困难。因此,本文提出了一种通过特征优化的 PSO-BP 裂缝-岩浆储层产能预测模型。利用 Chatterjee 相关系数选择合适的地震属性组合作为预测模型的输入,并将 PSO-BP 模型应用于中国塔河油田典型裂缝-凹陷油藏区的油井产量预测,预测结果与 BP 神经网络、线性支持向量机和多元线性回归的预测结果进行了比较。基于测试集的四种模型的预测结果表明,与其他三种模型相比,PSO-BP 模型的 MSE 增加了 23% 至 62%,RMSE 增加了 12% 至 38%,MAE 增加了 18% 至 44%,SSE 增加了 23% 至 62%,R 方值增加了 2% 至 13%。通过对比证明,本文提出的PSO-BP神经网络模型适用于裂缝-岩性储层的产能预测,具有较好的性能,对裂缝-岩性储层的开发生产具有指导意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Productivity Prediction Method of Fracture-Vuggy Reservoirs Based on the PSO-BP Neural Network
Reservoir productivity prediction is a key component of oil and gas field development, and the rapid and accurate evaluation of reservoir productivity plays an important role in evaluating oil field development potential and improving oil field development efficiency. Fracture-vuggy reservoirs are characterized by strong heterogeneity, complex distribution, and irregular development, causing great difficulties in the efficient prediction of fracture-vuggy reservoirs’ productivity. Therefore, a PSO-BP fracture-vuggy reservoir productivity prediction model optimized by feature optimization was proposed in this paper. The Chatterjee correlation coefficient was used to select the appropriate combination of seismic attributes as the input of the prediction model, and we applied the PSO-BP model to predict oil wells’ production in a typical fracture-vuggy reservoir area of Tahe Oilfield, China, with the selected seismic attributes and compared the accuracy with that provided by the BP neural network, linear support vector machine, and multiple linear regression. The prediction results using the four models based on the test set showed that compared with the other three models, the MSE of the PSO-BP model increased by 23% to 62%, the RMSE increased by 12 to 38 percent, the MAE increased by 18 to 44 percent, the SSE increased by 23 to 62 percent, and the R-square value increased by 2 to 13 percent. This comparison proves that the PSO-BP neural network model proposed in this paper is suitable for the productivity prediction of fracture-vuggy reservoirs and has better performance, which is of guiding significance for the development and production of fracture-vuggy reservoirs.
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