|本期目录/Table of Contents|

[1]李 乾 张海山 邱 康 王孝山 黄 召 杜 鹏 苏志波 雷 磊.东海深部地层岩石可钻性预测方法研究[J].中国海上油气,2020,32(02):126-133.[doi:10.11935/j.issn.1673-1506.2020.02.015]
 LI Qian ZHANG Haishan QIU Kang WANG Xiaoshan HUANG Zhao DU Peng SU Zhibo LEI Lei.Research on the rock drillability prediction methods of deep strata in the East China Sea[J].China Offshore Oil and Gas,2020,32(02):126-133.[doi:10.11935/j.issn.1673-1506.2020.02.015]
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东海深部地层岩石可钻性预测方法研究()

《中国海上油气》[ISSN:1673-1506/CN:11-5339/TE]

卷:
第32卷
期数:
2020年02期
页码:
126-133
栏目:
钻采工程
出版日期:
2020-03-25

文章信息/Info

Title:
Research on the rock drillability prediction methods of deep strata in the East China Sea
文章编号:
1673-1506(2020)02-0126-08
作者:
李 乾1 张海山2 邱 康1 王孝山1 黄 召2 杜 鹏1 苏志波1 雷 磊2
(1. 中国石油化工股份有限公司上海海洋油气分公司 上海 200120; 2. 中海石油(中国)有限公司上海分公司 上海 200335)
Author(s):
LI Qian1 ZHANG Haishan2 QIU Kang1 WANG Xiaoshan1 HUANG Zhao2 DU Peng1 SU Zhibo1 LEI Lei2
(1. Offshore Petroleum Engineering Institute of Sinopec Shanghai Offshore Oil & Gas Company, Shanghai 200120, China; 2. CNOOC China Limited, Shanghai Branch, Shanghai 200335, China)
关键词:
东海 深部地层 预测岩石可钻性 非线性多元回归 神经网络
Keywords:
the East China Sea deep strata rock drillability prediction nonlinear multiple regression neural network
分类号:
TE21
DOI:
10.11935/j.issn.1673-1506.2020.02.015
文献标志码:
A
摘要:
东海深部地层中部分井PDC钻头与地层性质匹配性不足,导致机械钻速低、钻头磨损严重等问题发生。以室内岩心微钻实验数据为依据,首先建立了测井参数预测岩石可钻性的非线性多元回归模型,同时利用多种人工神经网络方法对岩石可钻性进行了预测,结果表明非线性多元回归模型预测岩石可钻性与常规BP神经网络、级联BP神经网络、径向基RBF神经网络、BP-RBF双级联神经网络模型预测结果均具有较高可信度,但BP-RBF双级联神经网络模型预测效果最好,更适合于东海深部地层岩石可钻性预测。本文研究结果可为东海深部地层岩石可钻性预测及钻头选型提供借鉴。
Abstract:
In view of the challenges in the deep strata drilling of some wells in the East China Sea, such as low ROP and serious bit wear caused by the poor matching between PDC bit and the formation properties, it is urgently needed to conduct research on rock drillability of deep strata in the East China Sea. Based on the indoor core micro-drilling experimental data, a nonlinear multiple regression model for predicting the rock drillability based on logging parameters was first established, and multiple artificial neural network methods were used to predict rock drillability. The results show that all the rock drillability predictions with the non-linear multiple regression model and the conventional BP neural network, cascade BP neural network, radial basis RBF neural network, and BP-RBF double cascade neural network can achieve good reliability. However, the BP-RBF double cascade neural network model can obtain the best prediction effect, which is more suitable for predicting the rock drillability of deep strata in the East China Sea. The research results in this paper can provide references for the drillability prediction and drill bit selection of deep strata in the East China Sea.

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备注/Memo

备注/Memo:
*“十三五”国家科技重大专项“深层高效钻井关键技术(编号:2016ZX05027-003-001)”部分研究成果。第一作者简介: 李乾,男,工程师, 2016毕业于中国石油大学(北京)油气井工程专业,获硕士学位,主要从事钻完井工艺研究。地址:上海市浦东新区商城路1225号(邮编:200120)。E-mail:liqian.shhy@sinopec.com。收稿日期:2018-12-17 改回日期:2019-04-20(编辑:孙丰成)
更新日期/Last Update: 2020-03-30