Research on Pore Pressure Prediction Technology of HTHP Wells in South China Sea Based on Machine Learning

Dongsheng Xu, Jin Yang, Yuhang Zhao, Jianchun Fan, Yanjun Li, Xun Liu, Kejin Chen, Zehua Song, Xun Zhang, Hong Zhu
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引用次数: 1

Abstract

The Yingqiong Basin in the South China Sea is located at the intersection of the Eurasian and Indo-Chinese plates, with complex geology and often accompanied by abnormally high pressure. In this paper, we analyze the causes of anomalous high pressure in the South China Sea and analyze the commonly used machine learning methods, support vector machine and BP neural network, and use both methods to predict a block in Yingqiong Basin. The field application was carried out using this method, and the application showed that the prediction accuracy exceeded 95%, the complexity was reduced by 42%, and the drilling efficiency was improved by more than 53%, which played a good guiding effect to the field.
基于机器学习的南海高温高压井孔隙压力预测技术研究
南海英琼盆地位于欧亚板块和印支板块的交汇处,地质条件复杂,常伴有异常高压。本文分析了南海异常高压的成因,分析了常用的机器学习方法、支持向量机和BP神经网络,并用这两种方法对莺琼盆地某区块进行了预测。应用该方法进行了现场应用,应用结果表明,预测精度超过95%,复杂性降低42%,钻井效率提高53%以上,对现场起到了良好的指导作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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