Petroleum Reservoir Evaluation and Development >
2025 , Vol. 15 >Issue 3: 373 - 381
DOI: https://doi.org/10.13809/j.cnki.cn32-1825/te.2025.03.004
Research and application of intelligent diagnosis and optimization technologies for multi-model oil and gas development
Received date: 2024-07-24
Online published: 2025-05-28
With the increasing difficulty in oil and gas development and insufficient replacement of resources, traditional development of oil and gas reservoirs faces multiple challenges, requiring intelligent analysis solutions for enhanced development efficiency. This study focused on the demand and application scenarios for efficient development in conventional oil and gas reservoirs and shale gas reservoirs and proposed an innovative intelligent technology for oil and gas development based on multi-model approaches. It enabled decision-making of production and efficiency allocation, comprehensive abnormal situation awareness, and intelligent balanced injection-production optimization. This effectively promoted the intelligent exploitation of reservoir resources, providing technical support for balanced injection-production and efficient development in multilayered complex waterflood reservoirs. A pressure prediction and capacity factor analysis technology for shale gas reservoirs was developed, along with an abnormality warning mechanism to push alerts about abnormal factors and their root causes. This achieved a transition from post-event analysis to early warning and pre-emptive intervention, thereby supporting the efficient development of gas reservoirs. Breakthroughs were made in establishing a multi-modal self-diagnosis and evaluation technology for oil wells, achieving intelligent diagnosis of pumping well operating conditions, self-diagnosis and intelligent evaluation of electric pumping well conditions, and real-time calculation of dynamic fluid levels in oil wells. These supported measure formulation, enabled refined management of oil wells, and made injection-production adjustments more timely and accurate, effectively improving the production time ratio of oil wells. The integrated technology application supported developing a new operational model featuring “comprehensive awareness, integrated coordination, early warning, and analysis and optimization” for the dynamic management and control of oil and gas reservoirs. These research technologies have been widely promoted among upstream companies of Sinopec, with practical application focusing on multi-model oil and gas development technologies. This study offers new ideas and technical approaches to address key challenges in the efficient development of oil and gas reservoirs, driving the digital and intelligent transformation of the oil and gas sector and facilitating the efficient and high-quality development of oil and gas fields.
JING Shuai , WU Jianjun , MA Chengjie . Research and application of intelligent diagnosis and optimization technologies for multi-model oil and gas development[J]. Petroleum Reservoir Evaluation and Development, 2025 , 15(3) : 373 -381 . DOI: 10.13809/j.cnki.cn32-1825/te.2025.03.004
1 | 匡立春, 刘合, 任义丽, 等. 人工智能在石油勘探开发领域的应用现状与发展趋势[J]. 石油勘探与开发, 2021, 48(1): 1-11. |
KUANG Lichun, LIU He, REN Yili, et al. Application and development trend of artificial intelligence in petroleum exploration and development[J]. Petroleum Exploration and Development, 2021, 48(1): 1-11. | |
2 | 李阳, 廉培庆, 薛兆杰, 等. 大数据及人工智能在油气田开发中的应用现状及展望[J]. 中国石油大学学报(自然科学版), 2020, 44(4): 1-11. |
LI Yang, LIAN Peiqing, XUE Zhaojie, et al. Application status and prospect of big data and artificial intelligence in oil and gas field development[J]. Journal of China University of Petroleum(Edition of Natural Science), 2020, 44(4): 1-11. | |
3 | 李剑峰. 智慧石化建设: 从信息化到智能化[J]. 石油科技论坛, 2020, 39(1): 34-42. |
LI Jianfeng. Construction of intelligent petrochemical industry: From information to intelligence[J]. Petroleum Science and Technology Forum, 2020, 39(1): 34-42. | |
4 | 张凯, 赵兴刚, 张黎明, 等. 智能油田开发中的大数据及智能优化理论和方法研究现状及展望[J]. 中国石油大学学报(自然科学版), 2020, 44(4): 28-38. |
ZHANG Kai, ZHAO Xinggang, ZHANG Liming, et al. Current status and prospect for the research and application of big data and intelligent optimization methods in oilfield development[J]. Journal of China University of Petroleum(Edition of Natural Science), 2020, 44(4): 28-38. | |
5 | 刘伟, 闫娜. 人工智能在石油工程领域应用及影响[J]. 石油科技论坛, 2018, 37(4): 32-40. |
LIU Wei, YAN Na. Application and influence of artificial intelligence in petroleum engineering area[J]. Oil Forum, 2018, 37(4): 32-40. | |
6 | 于金彪. 油藏数值模拟历史拟合分析方法[J]. 油气地质与采收率, 2017, 24(3): 66-70. |
YU Jinbiao. History matching analysis method on reservoir numerical simulation[J]. Petroleum Geology and Recovery Efficiency, 2017, 24(3): 66-70. | |
7 | 王鸣川, 段太忠, 孙红军, 等. 油藏自动历史拟合研究进展[J]. 科技导报, 2016, 34(18): 236-245. |
WANG Mingchuan, DUAN Taizhong, SUN Hongjun, et al. Research progress in reservoir automatic history matching[J]. Science & Technology Review, 2016, 34(18): 236-245. | |
8 | 钟仪华, 王淑宁, 罗兰, 等. 用深度学习挖掘油田开发指标预测模型的知识[J]. 西南石油大学学报(自然科学版), 2020, 42(6): 63-74. |
ZHONG Yihua, WANG Shuning, LUO Lan, et al. Knowledge mining for oilfield development index prediction model using deep learning[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 63-74. | |
9 | 谷建伟, 周梅, 李志涛, 等. 基于数据挖掘的长短期记忆网络模型油井产量预测方法[J]. 特种油气藏, 2019, 26(2): 77-81. |
GU Jianwei, ZHOU Mei, LI Zhitao, et al. Oil well production forecast with long-short term memory network model based on data mining[J]. Special Oil & Gas Reservoirs, 2019, 26(2): 77-81. | |
10 | 侯春华. 基于长短期记忆神经网络的油田新井产油量预测方法[J]. 油气地质与采收率, 2019, 26(3): 105-110. |
HOU Chunhua. New well oil production forecast method based on long-term and short-term memory neural network[J]. Petroleum Geology and Recovery Efficiency, 2019, 26(3): 105-110. | |
11 | 阳晓燕. 非均质油藏水驱开发效果研究[J]. 特种油气藏, 2019, 26(2): 152-156. |
YANG Xiaoyan. Waterflood development effect study of heterogeneous reservoir[J]. Special Oil & Gas Reservoirs, 2019, 26(2): 152-156. | |
12 | 黄帅, 彭彩珍. 基于灰色关联的产量递减因素分析[J]. 油气藏评价与开发, 2018, 8(4): 33-35. |
HUANG Shuai, PENG Caizhen. Study on production decline factors based on gray correlation[J]. Reservoir Evaluation and Development, 2018, 8(4): 33-35. | |
13 | 张茂林, 廖洪, 杨龙, 等. 页岩气藏储量计算方法分析[J]. 油气藏评价与开发, 2017, 7(3): 67-73. |
ZHANG Maolin, LIAO Hong, YANG Long, et al. Reserve calculating method of shale gas reservoir[J]. Reservoir Evaluation and Development, 2017, 7(3): 67-73. | |
14 | 杨耀忠, 谭绍泉, 孙业恒, 等. 油气勘探开发综合研究数字平台建设及应用[J]. 油气藏评价与开发, 2021, 11(4): 628-634. |
YANG Yaozhong, TAN Shaoquan, SUN Yeheng, et al. Construction and application of digital platform for comprehensive research of oil and gas exploration and development[J]. Petroleum Reservoir Evaluation and Development, 2021, 11(4): 628-634. | |
15 | 梁文福. 油田开发智能应用系统建设成果及展望[J]. 大庆石油地质与开发, 2019, 38(5): 283-289. |
LIANG Wenfu. Constructed achievements and prospects of the intelligent application system for the oilfield development[J]. Petroleum Geology & Oilfield Development in Daqing, 2019, 38(5): 283-289. | |
16 | 任燕龙, 谷建伟, 崔文富, 等. 基于改进果蝇算法和长短期记忆神经网络的油田产量预测模型[J]. 科学技术与工程, 2020, 20(18): 7245-7251. |
REN Yanlong, GU Jianwei, CUI Wenfu, et al. Oilfield production prediction model based on improved fruit fly algorithm and long-short term memory neural network[J]. Science Technology and Engineering, 2020, 20(18): 7245-7251. | |
17 | 刘巍, 刘威, 谷建伟. 基于机器学习方法的油井日产油量预测[J]. 石油钻采工艺, 2020, 42(1): 70-75. |
LIU Wei, LIU Wei, GU Jianwei. Oil production prediction based on a machine learning method[J]. Oil Drilling & Production Technology, 2020, 42(1): 70-75. | |
18 | 吴君达, 李治平, 孙妍, 等. 基于神经网络的剩余油分布预测及注采参数优化[J]. 油气地质与采收率, 2020, 27(4): 85-93. |
WU Junda, LI Zhiping, SUN Yan, et al. Neural network-based prediction of remaining oil distribution and optimization of injection-production parameters[J]. Petroleum Geology and Recovery Efficiency, 2020, 27(4): 85-93. | |
19 | 刘巍, 刘威, 谷建伟, 等. 利用卡尔曼滤波和人工神经网络相结合的油藏井间连通性研究[J]. 油气地质与采收率, 2020, 27(2): 118-124. |
LIU Wei, LIU Wei, GU Jianwei, et al. Research on interwell connectivity of oil reservoirs based on Kalman filter and artificial neural network[J]. Petroleum Geology and Recovery Efficiency, 2020, 27(2): 118-124. | |
20 | 石玉江, 刘国强, 钟吉彬, 等. 基于大数据的测井智能解释系统开发与应用[J]. 中国石油勘探, 2021, 26(2): 113-126. |
SHI Yujiang, LIU Guoqiang, ZHONG Jibin, et al. Development and application of intelligent logging interpretation system based on big data[J]. China Petroleum Exploration, 2021, 26(2): 113-126. | |
21 | 段友祥, 李根田, 孙歧峰. 卷积神经网络在储层预测中的应用研究[J]. 通信学报, 2016, 37(): 1-9. |
DUAN Youxiang, LI Gentian, SUN Qifeng. Research on convolutional neural network for reservoir parameter prediction[J]. Journal on Communications, 2016, 37(): 1-9. | |
22 | 林年添, 张栋, 张凯, 等. 地震油气储层的小样本卷积神经网络学习与预测[J]. 地球物理学报, 2018, 61(10): 4110-4125. |
LIN Niantian, ZHANG Dong, ZHANG Kai, et al. Predicting distribution of hydrocarbon reservoirs with seismic data based on learning of the small-sample convolution neural network[J]. Chinese Journal of Geophysics, 2018, 61(10): 4110-4125. | |
23 | 但松林, 刘尚奇, 罗艳艳, 等. 基于BP神经网络预测高含水层对SAGD开发效果的影响[J]. 大庆石油地质与开发, 2019, 38(2): 73-80. |
DAN Songlin, LIU Shangqi, LUO Yanyan, et al. Predicted SAGD development effects by BP neural network for the high-watercut reservoir[J]. Petroleum Geology & Oilfield Development in Daqing, 2019, 38(2): 73-80. | |
24 | 宋辉, 陈伟, 李谋杰, 等. 基于卷积门控循环单元网络的储层参数预测方法[J]. 油气地质与采收率, 2019, 26(5): 73-78. |
SONG Hui, CHEN Wei, LI Moujie, et al. A method to predict reservoir parameters based on convolutional neural network-gated recurrent unit(CNN-GRU)[J]. Petroleum Geology and Recovery Efficiency, 2019, 26(5): 73-78. | |
25 | 程翊珊, 李治平, 许龙飞, 等. 预测油层无机积垢的BP神经网络方法[J]. 大庆石油地质与开发, 2021, 40(3): 84-93. |
CHENG Yishan, LI Zhiping, XU Longfei, et al. BP neural network method for predicting the inorganic scaling in the reservoir[J]. Petroleum Geology & Oilfield Development in Daqing, 2021, 40(3): 84-93. | |
26 | 黄家宸, 张金川. 机器学习预测油气产量现状[J]. 油气藏评价与开发, 2021, 11(4): 613-620. |
HUANG Jiachen, ZHANG Jinchuan. Overview of oil and gas production forecasting by machine learning[J]. Petroleum Reservoir Evaluation and Development, 2021, 11(4): 613-620. | |
27 | 王相, 杨耀忠, 何岩峰, 等. 基于深度学习的油井工况智能诊断技术研究及应用[J]. 油气地质与采收率, 2022, 29(1): 181-189. |
WANG Xiang, YANG Yaozhong, HE Yanfeng, et al. Research and application of intelligent diagnosis technology of oil well working conditions based on deep learning[J]. Petroleum Geology and Recovery Efficiency, 2022, 29(1): 181-189. | |
28 | 郭健. 基于特征值提取与神经网络的抽油井故障诊断[J]. 电子设计工程, 2014, 22(2): 41-43. |
GUO Jian. Fault diagnosis of pumping well based on the eigenvalue extraction and neural network[J]. Electronic Design Engineering, 2014, 22(2): 41-43. | |
29 | 曲文尧, 王春华. 人工神经网络法用于抽油机井故障诊断[J]. 油气田地面工程, 2013, 32(8): 14-15. |
QU Wenyao, WANG Chunhua. Application of artificial neural network method to fault diagnosis of pumping wells[J]. Oil-Gasfield Surface Engineering, 2013, 32(8): 14-15. | |
30 | 仲志丹, 赵斐, 李鹏辉. 深度信念网在油井功图识别中的应用[J]. 西安石油大学学报(自然科学版), 2017, 32(3): 89-93. |
ZHONG Zhidan, ZHAO Fei, LI Penghui. Application of deep belief network in identification of indicator diagram types[J]. Journal of Xi’an Shiyou University(Natural Science Edition), 2017, 32(3): 89-93. | |
31 | 刘宝军. 基于CNN卷积神经网络的示功图诊断技术[J]. 西安石油大学学报(自然科学版), 2018, 33(5): 70-75. |
LIU Baojun. Research on diagnostic technique of indicator diagram based on CNN convolution neural network[J]. Journal of Xi’an Shiyou University(Natural Science Edition), 2018, 33(5): 70-75. | |
32 | 杜娟, 刘志刚, 宋考平, 等. 基于卷积神经网络的抽油机故障诊断[J]. 电子科技大学学报, 2020, 49(5): 751-757. |
DU Juan, LIU Zhigang, SONG Kaoping, et al. Fault diagnosis of pumping unit based on convolutional neural network[J]. Journal of University of Electronic Science and Technology of China, 2020, 49(5): 751-757. |
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