石油工程

基于长短期记忆神经网络的随钻地层倾角解释方法

  • 孙歧峰 ,
  • 李娜 ,
  • 段友祥 ,
  • 李洪强 ,
  • 唐海全
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  • 1.中国石油大学(华东)计算机科学与技术学院,山东青岛 266580;
    2.中国石化胜利石油工程有限公司钻井工艺研究院,山东东营 257000;
    3.中国石化胜利石油工程有限公司测控技术研究院,山东东营 257000
孙歧峰(1976-),男,山东东营人,博士,中国石油大学(华东)计算机科学与技术学院讲师,主要从事人工智能及其在石油行业中的应用研究。地址:山东省青岛市黄岛区长江西路66号,中国石油大学(华东),邮政编码:266580。E-mail:sunqf@upc.edu.cn

收稿日期: 2020-12-10

  修回日期: 2021-04-29

  网络出版日期: 2021-07-23

基金资助

中国石油重大科技项目(ZD2019-183-006); 中央高校基本科研业务费专项资金(20CX05017A); 国家科技重大专项“低渗透油气深层高温高压随钻测控技术”(2016ZX05021-001)

Logging-while-drilling formation dip interpretation based on long short-term memory

  • SUN Qifeng ,
  • LI Na ,
  • DUAN Youxiang ,
  • LI Hongqiang ,
  • TANG Haiquan
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  • 1. College of Computer Science and Technology in China University of Petroleum, Qingdao 266580, China;
    2. Drilling Technology Research Institute of Shengli Petroleum Engineering Corporation Limited, Sinopec, Dongying 257000, China;
    3. Measurement and Control Technology Research Institute of Shengli Petroleum Engineering Corporation Limited, Dongying 257000, China

Received date: 2020-12-10

  Revised date: 2021-04-29

  Online published: 2021-07-23

摘要

鉴于随钻方位伽马测井面临实时数据传输的信息有限且解释难度大的问题,将人工智能与随钻测井解释相结合以提高实时数据处理的准确性和效率,阐述了具体方法并对提出的方法进行了验证和应用。通过研究方位伽马测井曲线的地层响应特征,基于小波变换模极大值的方法初步判断地层变化位置并确定动态阈值,进而得到描述地层边界的轮廓点集合。基于长短期记忆神经网络设计地层识别分类器模型,判定轮廓点集合描述地层信息的真伪,提高地层识别的准确度。结合非线性最小二乘法实现地层相对倾角的计算。方位伽马数据解释与随钻实时数据处理两方面的应用结果表明:提出的方法在有效、准确地判断地层变化的同时,提高了倾角解释的准确率,且能够满足随钻实时地质导向的需要。 图8 表3 参27

本文引用格式

孙歧峰 , 李娜 , 段友祥 , 李洪强 , 唐海全 . 基于长短期记忆神经网络的随钻地层倾角解释方法[J]. 石油勘探与开发, 2021 , 48(4) : 843 -850 . DOI: 10.11698/PED.2021.04.17

Abstract

Azimuth gamma logging-while-drilling (LWD) is one of the important technologies of geosteering but the information of real-time data transmission is limited and the interpretation is difficult. This study proposes a method of applying artificial intelligence in the LWD data interpretation to enhance the accuracy and efficiency of real-time data processing. By examining formation response characteristics of azimuth gamma ray (GR) curve, the preliminary formation change position is detected based on wavelet transform modulus maxima (WTMM) method, then the dynamic threshold is determined, and a set of contour points describing the formation boundary is obtained. The classification recognition model based on the long short-term memory (LSTM) is designed to judge the true or false of stratum information described by the contour point set to enhance the accuracy of formation identification. Finally, relative dip angle is calculated by nonlinear least square method. Interpretation of azimuth gamma data and application of real-time data processing while drilling show that the method proposed can effectively and accurately determine the formation changes, improve the accuracy of formation dip interpretation, and meet the needs of real-time LWD geosteering.

参考文献

[1] NYE R, DI TOMMASO D.Well optimization using a LWD spectral azimuthal gamma ray tool in unconventional reservoirs[R]. OMC 2013-137, 2013.
[2] CARRILERO S G, HOLMES A, HINZ D, et al. A method for calculating more accurate stratigraphic positioning of horizontal wells using continuous inclination and azimuthal gamma ray images even while sliding[R]. SPE 191740-MS, 2018.
[3] LI K, GAO J, ZHAO X. Tool design of look-ahead electromagnetic resistivity LWD for boundary identification in anisotropic formation[J]. Journal of Petroleum Science and Engineering, 2020, 184: 106537.
[4] CHEN G, CHEN L, LI Q. Comparison and application of neural networks in LWD lithology identification[J]. IOP Conference Series: Earth and Environmental Science, 2020, 526: 012146.
[5] VIENS C, CLARK T, LIGHTFOOT J, et al. Real-time downhole data resolves lithology related drilling behavior[R]. SPE 189697-MS, 2018.
[6] BOOTLE R, WAUGH M, BITTAR M, et al. Laminated sand shale formation evaluation using azimuthal LWD resistivity[R]. SPE 123890-MS, 2009.
[7] 孙东征, 杨进, 杨翔骞, 等. 地层压力随钻预测技术在高温高压井的应用[J]. 石油钻采工艺, 2016, 38(6): 746-751.
SUN Dongzheng, YANG Jin, YANG Xiangqian, et al. Application of formation pressure prediction while drilling technology in HTHP wells[J]. Oil Drilling & Production Technology, 2016, 38(6): 746-751.
[8] POZO M, TORRES F, BEDOYA J, et al. Enhancing performance and operational safety of Argentinean Vaca Muerta shale wells by drilling high pressure formations using managed pressure drilling[R]. SPE 187142-MS, 2017.
[9] LUTFALLAH M, HIBLER A P, MISHRA A, et al. Optimizing a unique new generation LWD technology for petrophysical evaluation, well-placement, geological mapping and completion design: Carbonate reservoir case study[R]. SPE 181289-MS, 2016.
[10] MARTIN S J, THOMAS M G, HAMMONS S D, et al. Real-time azimuthal gamma and resistivity LWD data used to navigate complex unconventional reservoir: Western US Land[R]. SPE 165923-MS, 2013.
[11] GUPTA K D, VALLEGA V, MANIAR H, et al. A deep-learning approach for borehole image interpretation[R]. The Woodlands, Texas, USA: 2019 SPWLA 60th Annual Symposium, 2019.
[12] POPA A, CONNEL S. Optimizing horizontal well placement through stratigraphic performance prediction using artificial intelligence[R]. Calgary: SPE Annual Technical Conference and Exhibition, 2019.
[13] MOHAMED I M, MOHAMED S, MAZHER I, et al. Formation lithology classification: Insights into machine learning methods[R]. Calgary: SPE Annual Technical Conference and Exhibition, 2019.
[14] LI Xinran, WANG Lide, LI Peiqiang. The study on composite load model structure of artificial neural network[R]. Nanjing: 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, 2008.
[15] 薛波, 杨青, 张超虹. 基于形态学滤波与小波变换的测井曲线自动分层方法[J]. 地球物理学进展, 2020, 35(1): 203-210.
XUE Bo, YANG Qing, ZHANG Chaohong. Automatic slicing method of logging curves based on morphological filtering and wavelet transform[J]. Progress in Geophysics, 2020, 35(1): 203-210.
[16] YUAN Mei, WU Yuting, LI Lin. Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network[R]. Beijing: 2016 IEEE International Conference on Aircraft Utility Systems, 2016.
[17] 李洪强, 丁景丽, 林楠, 等. 随钻伽马测量数据处理方法的研究及应用[J]. 石油钻探技术, 2008, 36(4): 12-14.
LI Hongqiang, DING Jingli, LIN Nan, et al. Research and application of data processing method of gamma ray measurement while drilling[J]. Petroleum Drilling Technology, 2008, 36(4): 12-14.
[18] 周旋, 周树道, 黄峰, 等. 基于小波变换的图像增强新算法[J]. 计算机应用, 2005, 25(3): 606-608.
ZHOU Xuan, ZHOU Shudao, HUANG Feng, et al. A new image enhancement algorithm based on wavelet transform[J]. Computer Applications, 2005, 25(3): 606-608.
[19] 董泽, 谢华, 韩璞, 等. 小波变换模极大值消噪算法的研究[J]. 电力科学与工程, 2005(3): 12-16.
DONG Ze, XIE Hua, HAN Pu, et al.Research on wavelet transform modulus maximum denoising algorithm[J]. Power Science and Engineering, 2005(3): 12-16.
[20] 刁彦华, 王玉田, 陈国通. 基于小波变换模极大值的信号奇异性检测[J]. 河北工业科技, 2004, 21(1): 1-3.
DIAO Yanhua, WANG Yutian, CHEN Guotong. Signal singularity detection based on modulus maxima of wavelet transform[J]. Hebei Industrial Science and Technology, 2004, 21(1): 1-3.
[21] COLE T J, GREEN P J. Smoothing reference centile curves: The LMS method and penalized likelihood[J]. Statistics in Medicine, 1992, 11(10): 1305-1319.
[22] WANG J, HUISZOON C, XU L, et al. Quantitative study of natural gamma ray depth of image and dip angle calculations[R]. New Orleans, Louisiana: the SPWLA 54th Annual Logging Symposium, 2013.
[23] GRAVES A. Long short-term memory[M]. Berlin: Springer, 2012: 1735-1780.
[24] LI D, ZHANG Y, GONG D, et al. Gas data prediction based on LSTM neural network[J]. IOP Conference Series: Earth and Environmental Science, 2020, 750(1): 012175.
[25] LIU J, WANG G, DUAN L Y, et al. Skeleton-based human action recognition with global context-aware attention LSTM networks[J]. IEEE Transactions on Image Processing, 2018, 27(99): 1586-1599.
[26] 权波, 杨博辰, 胡可奇, 等. 基于LSTM的船舶航迹预测模型[J]. 计算机科学, 2018, 45(S2): 126-131.
QUAN Bo, YANG Bochen, HU Keqi, et al. Ship track prediction model based on LSTM[J]. Computer Science, 2018, 45(S2): 126-131.
[27] 王鑫, 吴际, 刘超, 等. 基于LSTM循环神经网络的故障时间序列预测[J]. 北京航空航天大学学报, 2018, 44(4): 772-784.
WANG Xin, WU Ji, LIU Chao, et al. Fault time series prediction based on LSTM recurrent neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 772-784.
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