[1]邓少清,许 鹏,华晓莉,等.基于支持向量机的非欠压实成因砂砾岩地层压力预测方法——以BZ-X气田为例[J].华南地震,2025,(02):182-191.[doi:10.13512/j.hndz.2025.02.20]
 DENG Shaoqing,XU Peng,HUA Xiaoli,et al.Prediction Method of Formation Pressure of Non-Undercom?pacting Glutenite Based on Support Vector Machine:A Case Study of BZ-X Gas Field[J].,2025,(02):182-191.[doi:10.13512/j.hndz.2025.02.20]
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基于支持向量机的非欠压实成因砂砾岩地层压力预测方法——以BZ-X气田为例()

华南地震[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年02期
页码:
182-191
栏目:
海洋地球物理
出版日期:
2025-06-30

文章信息/Info

Title:
Prediction Method of Formation Pressure of Non-Undercom?pacting Glutenite Based on Support Vector Machine:A Case Study of BZ-X Gas Field
文章编号:
1001-8662(2025)02-0182-10
作者:
邓少清许 鹏华晓莉何 玉
中海石油(中国)有限公司天津分公司渤海石油研究院,天津 300459
Author(s):
DENG ShaoqingXU PengHUA XiaoliHE Yu
Bohai Petroleum Institute , Tianjin Branch of CNOOC China Ltd., Tianjin 300459, China
关键词:
砂砾岩地层分频反演支持向量机地层压力预测
Keywords:
Glutenite formation Frequency division inversion Support vector machine Formation pressure predic?tion
分类号:
P618.13
DOI:
10.13512/j.hndz.2025.02.20
文献标志码:
A
摘要:
位于海湾盆地的BZ-X气田在古近系孔店组首次揭示了巨厚裂缝—孔隙型砂砾岩含气储层,这表明渤中凹陷深层孔店组砂砾岩地层也具有巨大的勘探潜力,同时钻探研究证实该层系异常高压特征明显。对于保障钻井安全而言得到精确的钻前压力预测结果在油气田勘探开发中具有十分重要的意义,随着人工智能非线性数学理论的发展,作者在研究区引入分频反演中振幅随频率变化(即AVF)信息,以已钻井电缆测压数据为质控条件,通过机器学习中的支持向量机算法建立测井资料与不同分频地震属性的非线性映射关系,该预测方法能够较为准确地揭示砂砾岩地层的压力分布特征,并且得到的地层压力预测结果相比线性统计方法精度更高、可靠性更强,这也为渤海湾盆地特殊岩性地层异常压力预测指明了一个新的方向。
Abstract:
The BZ-X Gas Field located in the Bohai Bay Basin first revealed a fracture-pore glutenite gas reservoir with huge thickness in Paleogene Kongdian Formation,which indicates that the glutenite formation in deep Kongdi?an Formation in Bozhong Sag also has great exploration potential. Meanwhile,drilling studies have confirmed that the abnormal pressure characteristics of the formation are obvious. It is of great significance to obtain accurate pre-drilling pressure prediction results in oil and gas field exploration and development to ensure drilling safety. With the development of the nonlinear mathematical theory of artificial intelligence,the paper introduced the amplitude variation with frequency(AVF)information in frequency division inversion in the research area. By taking the pres?sure measurement data of the drilled cable as the quality control condition,the nonlinear mapping relationship be?tween logging data and different frequency division seismic attributes was established by the support vector machine algorithm in machine learning. This prediction method could accurately reveal the pressure distribution characteris?tics of glutenite formation, and the formation pressure prediction results obtained are more accurate and reliable than the linear statistical method,which also points out a new direction for the prediction of abnormal pressure of special lithologic formation in the Bohai Bay Basin.

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备注/Memo

备注/Memo:
收稿日期:2024-08-22
基金项目:十三五”国家科技重大专项课题(2016ZX05024-003);中海石油(中国)有限公司“七年行动计划”重大科技专项课题(CNOOC-KJ 135 ZDXM 36 TJ 08 TJ)联合资助
作者简介:邓少清(1987-),男,硕士研究生,工程师,研究方向为油气田储层预测。E-mail:342839755@qq.com
更新日期/Last Update: 2025-06-30