Journal of Southwest Petroleum University(Science & Technology Edition) ›› 2025, Vol. 47 ›› Issue (6): 1-14.DOI: 10.11885/j.issn.1674-5086.2023.10.29.31
• SPECIALIST FORUM • Next Articles
ZHANG Zhi1, WANG Xianghui1, DING Jian1, ZHAO Jie2, WU Linfang2, HOU Zhenyong2
Received:2023-10-29
Published:2026-01-12
CLC Number:
ZHANG Zhi, WANG Xianghui, DING Jian, ZHAO Jie, WU Linfang, HOU Zhenyong. Application and Development of Big Data in Well Engineering[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2025, 47(6): 1-14.
| [1] 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. doi: 10.3969/j.issn.1673-5005.2020.04.001 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, 2020, 44(4): 1-11. doi: 10.3969/j.issn.1673-5005.2020.04.001 [2] 谢坤,吴湛奇,李彦阅,等. 机器学习在油气开发领域的应用及展望[J]. 西安石油大学学报(自然科学版), 2023, 38(5): 58-67. doi: 10.3969/j.issn.1673-064X.2023.05.008 XIE Kun, WU Zhanqi, LI Yanyue, et al. Application of machine learning in oil and gas development field and its prospect[J]. Journal of Xi'an Shiyou University (Natural Science Edition), 2023, 38(5): 58-67. doi: 10.3969/j.issn.1673-064X.2023.05.008 [3] 闵超,文国权,李小刚,等. 可解释机器学习在油气领域人工智能中的研究进展与应用展望[J]. 天然气工业, 2024, 44(9): 114-126. doi: 10.3787/j.issn.1000-0976.2024.09.011 MIN Chao, WEN Guoquan, LI Xiaogang, et al. Research progress and application prospect of interpretable machine learning in artificial intelligence of oil and gas industry[J]. Natural Gas Industry, 2024, 44(9): 114-126. doi: 10.3787/j.issn.1000-0976.2024.09.011 [4] 李道伦,查文舒,刘旭亮,等. 深度学习网络在非常规油气开发中的应用研究[J]. 非常规油气, 2024, 11(6): 17. doi: 10.19901/j.fcgyq.2024.06.01 LI Daolun, ZHA Wenshu, LIU Xuliang, et al. Research on application of deep learning network in unconventional oil and gas development[J]. Unconventional Oil & Gas, 2024, 11(6): 1-7. doi: 10.19901/j.fcgyq.2024.06.01 [5] 徐楷,苏堪华,李猛,等. 机器学习在油气钻井工程中的应用[J]. 非常规油气, 2023, 10(5): 817. doi: 10.19901/j.fcgyq.2023.05.02 XU Kai, SU Kanhua, LI Meng, et al. Application and development of machine learning in oil and gas drilling engineering[J]. Unconventional Oil & Gas, 2023, 10(5): 8-17. doi: 10.19901/j.fcgyq.2023.05.02 [6] 杨晨睿,沈鸿雁,车晗,等. 基于位移窗口自注意力网络和迁移学习的地震面波分离[J]. 西安石油大学学报(自然科学版), 2024, 39(6): 39-50. doi: 10.3969/j.issn.1673-064X.2024.06.005 YANG Chenrui, SHEN Hongyan, CHE Han, et al. Separation of surface wave from seismic data by swin transformer and transfer learning[J]. Journal of Xi'an Shiyou University (Natural Science Edition), 2024, 39(6): 39-50. doi: 10.3969/j.issn.1673-064X.2024.06.005 [7] 张伯虎,胡尧,王燕,等. 基于MLR-ANN算法的地应力场反演与裂缝预测[J]. 西南石油大学学报(自然科学版), 20-24, 46(3): 112. doi: 10.11885/j.issn.1674-5086.2022.08.20.01 ZHANG Bohu, HU Yao, WANG Yan, et al. Ground stress field inversion and fracture prediction based on MLRANN Algorithm[J]. Journal of Southwest Petroleum University (Science & Technology Edition), 2024, 46(3): 1-12. doi: 10.11885/j.issn.1674-5086.2022.08.20.01 [8] 汪敏,杨桃,唐洪明,等. 迁移深度神经网络的页岩总孔隙度预测[J]. 西南石油大学学报(自然科学版), 2023, 45(6): 69-79. doi: 10.11885/j.issn.1674-5086.2021.06.11.03 WANG Min, YANG Tao, TANG Hongming, et al. Prediction for total porosity of shale based on transfer deep neural network[J]. Journal of Southwest Petroleum University (Science & Technology Edition), 2023, 45(6): 69-79. doi: 10.11885/j.issn.1674-5086.2021.06.11.03 [9] 李辉,阎建国,陈榆桂,等. CNN算法的损失函数优化及在低信噪比资料中的应用[J]. 断块油气田, 2023, 30(1): 107-113. doi: 10.6056/dkyqt202301015 LI Hui, YAN Jianguo, CHEN Yugui, et al. Optimization of loss function in CNN algorithm and its application in low signal-to-noise ratio data[J]. Fault-Block Oil & Gas Field, 2023, 30(1): 107-113. doi: 10.6056/dkyqt202301015 [10] 陶静,张宝辉,杨博,等. 基于机器学习的油水层解释新方法——以新安边油田南部长7 油层组为例[J]. 西安石油大学学报(自然科学版), 2023, 38(2): 89-95. doi: 10.3969/j.issn.1673-064X.2023.02.012 TAO Jing, ZHANG Baohui, YANG Bo, et al. A new method for oil/water layer interpretation based on machine learning: Taking Chang 7 oil layer group in south of Xin'anbian Oilfield as an Example[J]. Journal of Xi'an Shiyou University (Natural Science Edition), 2023, 38(2): 89-95. doi: 10.3969/j.issn.1673-064X.2023.02.012 [11] 张景越,肖小玲,王鹏飞,等. 基于多信息融合的层次聚类测井曲线自动分层方法[J]. 断块油气田, 2024, 31(1): 42-49. doi: 10.6056/dkyqt202401006 ZHANG Jingyue, XIAO Xiaoling, WANG Pengfei, et al. Automatic stratification method of hierarchical clustering logging curve based on multi-information fusion[J]. FaultBlock Oil & Gas Field, 2024, 31(1): 42-49. doi: 10.6056/dkyqt202401006 [12] 张凤博,马雪玲,董珍珍,等. 基于CNN和LSTM的机器学习模型在测井岩性识别的应用[J]. 西安石油大学学报(自然科学版), 2024, 39(5): 96-103. doi: 10.3969/j.issn.1673-064X.2024.05.012 ZHANG Fengbo, MA Xueling, DONG Zhenzhen, et al. Application of machine learning model based on CNN and LSTM in well logging lithology identification[J]. Journal of Xi'an Shiyou University (Natural Science Edition), 2024, 39(5): 96-103. doi: 10.3969/j.issn.1673-06-4X.2024.05.012 [13] 王森,向杰,冯其红,等. 基于深度学习加速的油藏数值模拟自动历史拟合方法[J]. 中国石油大学学报(自然科学版), 2024, 48(5): 103-114. doi: 10.3969/j.issn.1673-5005.2024.05.011 WANG Sen, XIANG Jie, FENG Qihong, et al. Deeplearning-based acceleration method for automatic history matching of reservoir numerical simulation[J]. Journal of China University of Petroleum (Edition of Natural Science), 2024, 48(5): 103-114. doi: 10.3969/j.issn.1673-5005.2024.05.011 [14] 宋兆杰,何吉祥,宋宜磊,等. 基于深度学习的页岩油生产井最终可采储量预测模型——以吉木萨尔凹陷芦草沟组为例[J]. 非常规油气, 2025, 12(1): 95-105.doi: 10.19901/j.fcgyq.2025.01.10 SONG Zhaojie, HE Jixiang, SONG Yilei, et al. Deep learning-based estimated ultimate recovery prediction model for production wells in shale oil reservoirs: A case study of Lucaogou Formation, Jimsar Sag[J]. Unconventional Oil & Gas, 2025, 12(1): 91-105. doi: 10.19901/j.fcgyq.2025.01.10 [15] 樊冬艳,杨灿,孙海,等. 基于时间序列相似性与机器学习方法的页岩气井产量预测[J]. 中国石油大学学报(自然科学版), 2024, 48(3): 119-126. doi: 10.3969/j.issn.1673-5005.2024.03.013 FAN Dongyan, YANG Can, SUN Hai, et al. Shale gas well production forecasting based on time sequence similarity and machine learning methods[J]. Journal of China University of Petroleum (Edition of Natural Science), 2024, 48(3): 119-126. doi: 10.3969/j.issn.1673-5005.2024.03.013 [16] 许玉强,何保伦,王! 舒,等. 深度学习与Eaton法联合驱动的地层孔隙压力预测方法[J]. 中国石油大学学报(自然科学版), 2023, 47(6): 50-59. doi: 10.3969/j.issn.1673-5005.2023.06.006 XU Yuqiang, HE Baolun, WANG Yanshu, et al. A novel prediction method of formation pore pressure driven by deep learning and Eaton method[J]. Journal of China University of Petroleum (Edition of Natural Science), 2023, 47(6): 50-59. doi: 10.3969/j.issn.1673-5005.2023.06.006 [17] 冯义,朱亮,杨立军,等. 基于LSTM神经网络深度序列机械钻速实时预测[J]. 西安石油大学学报(自然科学版), 2024, 39(1): 122-128. doi: 10.3969/j.issn.1673-064X.2024.01.015 FENG Yi, ZHU Liang, YANG Lijun, et al. Real-time prediction of ROP based on LSTM neural network deep sequence[J]. Journal of Xi'an Shiyou University (Natural Science Edition), 2024, 39(1): 122-128. doi: 10.3969/j.issn.1673-064X.2024.01.015 [18] 刘阳,陈思彤,向幸运,等. 基于多维时序LSTM的超深井机械钻速预测方法[J]. 西南石油大学学报(自然科学版), 2025, 47(5): 121-133. doi: 10.11885/j.issn.1674-5086.2024.01.08.01 LIU Yang, CHEN Sitong, XIANG Xingyun, et al. Prediction of penetration rate method for ultra-deep well based on multi-dimensional time series LSTM[J]. Journal of Southwest Petroleum University(Science & Technology Edition) , 2025, 47(5): 121-133. doi: 10.11885/j.issn.1674-5086.2024.01.08.01 [19] 王彬,徐英卓,刘烨,等. 基于 Attention-LSTM时序模型的机械钻速预测方法研究[J]. 西安石油大学学报(自然科学版), 2024, 39(5): 85-95. doi: 10.3969/j.issn.1673-064X.2024.05.011 WANG Bin, XU Yingzhuo, LIU Ye, et al. Application of temporal modeling based on Attention-LSTM in prediction of mechanical drilling speed[J]. Journal of Xi'an Shiyou University (Natural Science Edition), 2024, 39(5): 85-95. doi: 10.3969/j.issn.1673-064X.2024.05.011 [20] 王贺强,郭海涛,马翠岩,等. 智能钻井系统在赵东油田的应用[J]. 世界石油工业, 2024, 31(3): 59-67. doi: 10.20114/j.issn.1006-0030.20240319001 WANG Heqiang, GUO Haitao, MA Cuiyan, et al. Field application of automated drilling system in Zhaodong Oilfield[J]. World Petroleum Industry, 2024, 31(3): 59-67. doi: 10.20114/j.issn.1006-0030.20240319001 [21] 熊惠. 基于AI的油气集输管道安全管控平台构建探究[J]. 西安石油大学学报(自然科学版), 2023, 38(4): 81-87. doi: 10.3969/j.issn.1673-064X.2023.04.010 XIONG Hui. Discussion on construction of AI-based safety control platform for oil and gas gathering and transportation pipeline[J]. Journal of Xi'an Shiyou University (Natural Science Edition), 2023, 38(4): 81-87. doi: 10.3969/j.issn.1673-064X.2023.04.010 [22] 张伟亚,宋保靓,陈向阳,等. 基于互信息和贝叶斯算法的天然气合成润滑油鉴别技术[J]. 石油与天然气化工, 2023, 52(5): 115-120. doi: 10.3969/j.issn.1007-3426.2023.05.017 ZHANG Weiya, SONG Baoliang, CHEN Xiangyang, et al. Gas-to-liquid lubricant identification technology based on mutual information and Bayesian algorithm[J]. Chemical Engineering of Oil & Gas, 2023, 52(5): 115-120. doi: 10.3969/j.issn.1007-3426.2023.05.017 [23] 刘广孚,于建宗,郭亮,等. 基于SVM的双辅助永磁体Halbach阵列潜油永磁同步电机优化设计[J]. 中国石油大学学报(自然科学版), 2023, 47(3): 164-172. doi: 10.3969/j.issn.1673-5005.2023.03.019 LIU Guangfu, YU Jianzong, GUO Liang, et al. Optimal design of submersible permanent magnet synchronous motor with double-assisted permanent magnet Halbach array based on SVM[J]. Journal of China University of Petroleum (Edition of Natural Science), 2023, 47(3): 164-172. doi: 10.3969/j.issn.1673-5005.2023.03.019 [24] 彭龙,韩国庆,邬书豪,等. 基于机器学习算法的CO2腐蚀速率预测[J]. 西安石油大学学报(自然科学版), 2023, 38(2): 113-121. doi: 10.3969/j.issn.1673-064X.2023.02.015 PENG Long, HAN Guoqing, WU Shuhao, et al. Prediction of CO2 corrosion rate based on machine learning algorithms[J]. Journal of Xi'an Shiyou University (Natural Science Edition), 2023, 38(2): 113-121. doi: 10.3969/j.issn.1673-064X.2023.02.015 [25] 沈定金,崔宇涛,王瀚,等. 基于机器学习的FPSO油气处理系统能耗预测[J]. 石油与天然气化工, 2025, 54(5): 126-136. doi: 10.3969/j.issn.1007-3426.2025.05.015 SHEN Dingjin, CUI Yutao, WANG Han, et al. Energy consumption prediction of FPSO oil and gas treatment system based on machine learning[J]. Chemical Engineering of Oil & Gas, 2025, 54(5): 126-136. doi: 10.3969/j.issn.1007-3426.2025.05.015 [26] 王诗慧,蒋巍,黄坤,等. 基于深度学习的天然气脱硫过程多目标预测建模研究[J]. 石油与天然气化工, 2025, 54(4): 1-11. doi: 10.3969/j.issn.1007-3426.2025.04.001 WANG Shihui, JIANG Wei, HUANG Kun, et al. Multiobjective predictive modeling of natural gas desulfurization process based on deep learning[J]. Chemical Engineering of Oil & Gas, 2025, 54(4): 1-11. doi: 10.3969/j.issn.1007-3426.2025.04.001 [27] MANYIKA J, CHUI M, BROWN B, et al. Big data: The next frontier for innovation, competition and productivity[R]. San Francisco: McKinsey Global Institute, 2011. [28] LANEY D. 3D data management: Controlling data volume, velocity and variety[J]. META Group Research Note, 2001, 6(70): 1. [29] AL-SHAMMARI A D, KUMAR R. Kuwait oil company's integrated data management system for exploration & production[C]. Calgary: the 16th World Petroleum Congress, 2000. [30] 耿黎东. 大数据技术在石油工程中的应用现状与发展建议[J]. 石油钻探技术, 2021, 49(2): 72-78. doi: 10.11911/syztjs.2020134 GENG Lidong. Application status and development suggestions of big data technology in petroleum engineering[J]. Petroleum Drilling Techniques, 2021, 49(2): 72-78. doi: 10.11911/syztjs.2020134 [31] MAOUCHE Z, AL-RAWAHI F, AGAPIE I, et al. New PDC bit technology sets the standards in drilling hard and abrasive formations in Oman-case study[C]. SPE 170462-MS, 2014. doi: 10.2118/170462-MS [32] JOHN-MORTEN G, KJETIL A K. High performance and reliability for MPD control system ensured by extensive testing[C]. SPE 128222-MS, 2010. doi: 10.2118/128222-MS [33] 全球 TMT. 美国第二大石油公司雪佛龙已授权云端Peloton平台[EB/OL]. 2022[2022-06-28]. https://baijiahao.baidu.com/s?id=1730859817180436850. [34] 马涛,张仲宏,王铁成,等. 勘探开发梦想云平台架构设计与实现[J]. 中国石油勘探, 2020, 25(5): 71-81. doi: 10.3969/j.issn.1672-7703.2020.05.010 MA Tao, ZHANG Zhonghong, WANG Tiecheng, et al. Architecture design and implementation of E & P Dream Cloud platform[J]. China Petroleum Exploration, 2020, 25(5): 71-81. doi: 10.3969/j.issn.1672-7703.2020.05.010 [35] 王力. 数据挖掘在客户关系管理中的应用研究[D]. 淮南:安徽理工大学, 2010. WANG Li. Research on data mining application in CRM[D]. Huainan: Anhui University of Science and Technology, 2010. [36] 杨传书,李昌盛,孙旭东,等. 人工智能钻井技术研究方法及其实践[J]. 石油钻探技术, 2021, 49(5): 7-13. doi: 10.11911/syztjs.2020136 YANG Chuanshu, LI Changsheng, SUN Xudong, et al. Research method and practice of artificial intelligence drilling technology[J]. Petroleum Drilling Techniques, 2021, 49(5): 7-13. doi: 10.11911/syztjs.2020136 [37] 章明,衡星,王曦麟,等. 基于LabVIEW的油气井井下集成监测系统设计[J]. 石油机械, 2018, 46(10): 92-96. doi: 10.16082/j.cnki.issn.1001-4578.2018.10.018 ZHANG Ming, HENG Xing, WANG Xilin, et al. Design of downhole integrated monitoring system for oilgas wells[J]. China Petroleum Machinery, 2018, 46(10): 92-96. doi: 10.16082/j.cnki.issn.1001-4578.2018.10.018 [38] FAN J, LÜ J. A selective overview of variable selection in high dimensional feature space[J]. Statistica Sinica, 2010, 20(1): 101-148. [39] 曾颖,石峰,刘英. 数字孪生技术在油气田业务中的应用现状及未来需求[J]. 信息系统工程, 2021(9): 90-92. doi: 10.3969/j.issn.1001-2362.2021.09.030 ZENG Ying, SHI Feng, LIU Ying. The application and demand of digital twin technology in oil and gas field business[J]. China CIO News, 2021(9): 90-92. doi: 10.3969/j.issn.1001-2362.2021.09.030 [40] 胡贵,崔明月,陶冶,等. 油气井筒工程数据平台技术进展及数据深度应用思考[J]. 石油科技论坛, 2021, 40(5): 65-72. doi: 10.3969/j.issn.1002-302x.2021.05.009 HU Gui, CUI Mingyue, TAO Ye, et al. Progress in oil and gas wellbore engineering data platform technology and suggestions on in-depth application of data[J]. Petroleum Science and Technology Forum, 2021, 40(5): 65-72. doi: 10.3969/j.issn.1002-302x.2021.05.009 [41] LI H, KUMAR N, CHEN R, et al. Deep reinforcement learning[C]. Calgary: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018. doi: 10.1109/ICASSP.2018.8462686 [42] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. doi: 10.1126/science.1127647 [43] 谢晓峰. 机器学习、深度学习、强化学习、迁移学习和人工智能的联系和区别? [EB/OL]. 2019[2022-08-28]. https://blog.csdn.net/princexiexiaofeng/article/details/89-063568. [44] BELLO O, TEODORIU C, YAQOOB T, et al. Application of artificial intelligence techniques in drilling system design and operations: A state of the art review and future research pathways[C]. SPE 184320-MS, 2016. doi: 10.2118/184320-MS [45] 刘合,李艳春,杜庆龙,等. 基于多变量时间序列模型的高含水期产量预测方法[J]. 中国石油大学学报(自然科学版), 2023, 47(5): 103-114. doi: 10.3969/j.issn.1673-5005.2023.05.010 LIU He, LI Yanchun, DU Qinglong, et al. Prediction of production during high water-cut period based on multivariate time series model[J]. Journal of China University of Petroleum (Edition of Natural Science), 2023, 47(5): 103-114. doi: 10.3969/j.issn.1673-5005.2023.05.010 [46] NOSHI C I, SCHUBERT J J. The role of machine learning in drilling operations: A review[C]. SPE 191823-18ERMMS, 2018. doi: 10.2118/191823-18ERM-MS [47] HEGDE C, WALLACE S, GRAY K. Using trees, bagging, and random forests to predict rate of penetration during drilling[C]. SPE 176792-MS, 2015. doi: 10.2118/176792-MS [48] 张智,赵苑瑾,吴优,等. 页岩气井井下安全阀完整性研究[J]. 石油管材与仪器, 2020, 6(4): 56-62. doi: 10.19459/j.cnki.61-1500/te.2020.04.010 ZHANG Zhi, ZHAO Yuanjin, WU You, et al. Integrity of subsurface safety valve in shale gas well[J]. Petroleum Tubular Goods & Instruments, 2020, 6(4): 56-62. doi: 10.19459/j.cnki.61-1500/te.2020.04.010 [49] 张智,何雨. 超深复杂结构井钻井参数优化软件: 2017SR-369848[CP/CD]. 2017-07-13. ZHANG Zhi, HE Yu. Well drilling parameter optimization software of ultra-deep complex structure: 2017SR-369848[CP/CD]. 2017-07-13. [50] LI Zejun, CHEN Mian, JIN Yan, et al. Study on intelligent prediction for risk level of lost circulation while drilling based on machine learning[C]. Washington: ARMA US Rock Mechanics/Geomechanics Symposium, 2018. [51] SHI Junfeng, CHEN Shiwen, ZHANG Xishun, et al. Artificial lift methods optimising and selecting based on big data analysis technology[C]. Beijing: International Petroleum Technology Conference, 2019. doi: 10.2523/IPTC-19470-MS [52] SHADRAVAN A, TARRAHI M, AMANI M. Intelligent tool to design drilling, spacer, cement slurry, and fracturing fluids by use of machine-learning algorithms[J]. SPE Drilling & Completion, 2017, 32(2): 131-140. doi: 10.2118/175238-PA [53] CASTIEIRA D, TORONYI R, SALERI N. Machine learning and natural language processing for automated analysis of drilling and completion data[C]. SPE 192280-MS, 2018. doi: 10.2118/192280-MS [54] AL-FATTAH S M, STARTZMAN R A. Predicting natural gas production using artificial neural network[C]. SPE 68593-MS, 2001. doi: 10.2118/68593-MS [55] 冯学章,孙玉铎,王晓磊,等. 基于贝叶斯网络的气井环空带压风险评价模型及应用分析[J]. 石油钻采工艺, 2021, 43(4): 532-537. doi: 10.13639/j.odpt.2021.04.018 FENG Xuezhang, SUN Yuduo, WANG Xiaolei, et al. Risk assessment model of sustained casing pressure of gas well based on Bayesian network and its application analysis[J]. Oil Drilling & Production Technology, 2021, 43(4): 532-537. doi: 10.13639/j.odpt.2021.04.018 [56] IGNOVA M, SCHLUMBERGER, AMAYA D, et al. Recognizing abnormal shock signatures during drilling with help of machine learning[C]. SPE 194952-MS, 2019. doi: 10.2118/194952-MS [57] SINGH H, SEOL Y, MYSHAKIN E M. Automated welllog processing and lithology classification by identifying optimal features through unsupervised and supervised machine-learning algorithms[J]. SPE Journal, 2020, 25(5): 2778-2800. doi: 10.2118/202477-PA [58] MAIDLA E, MAIDLA W, RIGG J, et al. Drilling analysis using big data has been misused and abused[C]. SPE 189583-MS, 2018. doi: 10.2118/189583-MS [59] 杨勇. 胜利油田勘探开发大数据及人工智能技术应用进展[J]. 油气地质与采收率, 2022, 29(1): 1-10. doi: 10.13673/j.cnki.cn37-1359/te.2022.01.001 YANG Yong. Application progress of big data & AI technologies in exploration and development of Shengli Oilfield[J]. Petroleum Geology and Recovery Efficiency, 2022, 29(1): 1-10. doi: 10.13673/j.cnki.cn37-1359/te.2022.01.001 [60] 张智,张乃艳,刘志伟,等. 一种井筒完整性综合风险定量计算方法: CN107403266A[P]. 2017-07-21. ZHANG Zhi, ZHANG Naiyan, LIU Zhiwei, et al. A comprehensive quantitative calculation method for wellbore integrity risks: CN107403266A[P]. 2017-07-21. |
| [1] | GE Liang, TENG Yi, XIAO Guoqing, XIAO Xiaoting, DENG Hongxia. Research on Overflow Intelligent Warning Technology Based on Downhole Annulus Parameters [J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2023, 45(2): 126-134. |
| [2] | MIN Chao, DAI Boren, ZHANG Xinhui, DU Jianping. A Review of the Application Progress of Machine Learning in Oil and Gas Industry [J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 1-15. |
| [3] | HUANG Xuri, DAI Yue, XU Yungui, TANG Jing. Seismic Inversion Experiments Based on Deep Learning Algorithm Using Different Datasets [J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 16-25. |
| [4] | FANG Yu, CAO Xuemei, LI Binqian, MIN Fan, QIAO Ying. Active Learning Method for Abnormal Operating Conditions of Natural Gas Gathering System [J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 124-132. |
| [5] | HAN Jie, ZHANG Shaowei, WU Jiangyong, CHEN Si, MA Xiaoping. Application of Big Data to Carbonate Oil and Gas Field Exploitation [J]. 西南石油大学学报(自然科学版), 2018, 40(6): 1-11. |
| [6] | ZENG De-ren PENG Tuo HUANG Cheng WANG Yong-qing.. RISK EVALUATION OF WELL TEST DESIGN [J]. 西南石油大学学报(自然科学版), 2001, 23(4): 23-25. |
| [7] | Cao Xie-dong Zhao Jin-zhou Li Yun et al . A METHOD OF GETTING KNOWLEDGE BASED ON TH FUZZY NERVE NETWORK [J]. 西南石油大学学报(自然科学版), 2000, 22(3): 98-100. |
| [8] | Chen Wei Duan Yong-gang Liu Hui Xie Jun. ARTIFICIAL INTELLIGENCE TO ASSIST WELL TESTING INTERPRETATION [J]. 西南石油大学学报(自然科学版), 1999, 21(2): 5-8. |
| [9] | Chen Wei Duan Yonggang Jang Hong. Neural Network Structure Design Based on Genetic Algorithms [J]. 西南石油大学学报(自然科学版), 1998, 20(1): 96-98. |
| [10] | Mei Wenrong. RECOGNITION RESEARCH OF COMPLICATED CASES AND ACCIDENTS IN DRILLING OPERATION BASED ON BAM NEURAL NETWORK [J]. 西南石油大学学报(自然科学版), 1993, 15(1): 49-55. |
| [11] | Cheng Suimin Li Ruyong Wang Weixing. AN EXPERT SYSTEM IN WELL TEST ANALYSIS [J]. 西南石油大学学报(自然科学版), 1992, 14(1): 1-7. |
| [12] | Wang Wei-xing Cheng Sui-min li Ru-yong Wang Hao. USE OF ARTIFICIAL INTELLIGENCE IN WELL TEST INTERPRETATION [J]. 西南石油大学学报(自然科学版), 1991, 13(2): 31-37. |
| [13] | Cheng Sui-min . GENERAL EXPRESSION OF EVALUTION AND ANALYSIS-MODEL OF MODERN TESTWELL FOR FORMATION DAMAGE OF UNIFORM AND NONUNIFORM RESERVOIRS [J]. 西南石油大学学报(自然科学版), 1988, 10(4): 49-58. |
| Viewed | ||||||
|
Full text |
|
|||||
|
Abstract |
|
|||||