基于布谷鸟—BP神经网络的页岩脆性指数预测研究

doi:10.3969/j.issn.1672-7703.2024.02.013

摘要/Abstract

摘要： 页岩储层具有低孔隙度、低渗透率的物理性质，因此在页岩气开采中往往需要对其储层进行压裂处理，而页岩储层的可压裂性可用脆性指数来评价。目前应用最广泛的岩石脆性指数计算方法是基于矿物组分法。基于矿物组分法计算获得岩心页岩脆性指数（BI），利用BP神经网络的自我学习能力，探寻测井参数与页岩脆性指数(BI)之间的非线性关系，再结合布谷鸟（CS）算法的全局优化能力和稳定性来提升BP神经网络的预测精度和稳定性,从而建立基于CS—BP神经网络的页岩脆性指数预测模型。使用CS—BP预测模型对研究区Y1井和Y2井两口井进行了页岩BI值预测，其预测结果显示：CS—BP预测值与岩心BI值的变化趋势基本一致；CS-BP预测值总体预测效果较好。研究结果表明：基于布谷鸟（CS）—BP神经网络，利用测井资料快速计算页岩脆性指数的方法在研究区具有一定的实用价值。

关键词: 可压裂性, 页岩储层, BP神经网络, CS—BP算法, 页岩脆性指数

Abstract: Shale reservoir is characterized by physical properties of low porosity and low permeability, and fracturing treatment is often required in shale gas production. The fracability of shale reservoir can be evaluated by brittleness index (BI), and the mineral composition method is the most popular method for calculating rock BI. Based on the mineral composition method calculated core BI value, the non-linear relationship between logging parameters and shale BI has been analyzed by applying the self-learning ability of BP neural network, and then the cuckoo (CS) algorithm with global optimization ability and stability has been used to improve the prediction accuracy and stability of BP neural network, so as to establish a shale BI prediction model based on CS-BP neural network. The CS-BP prediction model has been used to predict shale BI values in wells Y1 and Y2 in the study area, which indicates that CS-BP prediction values have a basically consistent trend with core BI values, showing a good prediction result as a whole. The study results indicate that the cuckoo (CS)-BP neural network based method for rapidly calculating shale BI by using logging data has certain practical value in the study area.

中图分类号:

TE19

黄开兴, 刘卫华, 吴朝容, 胡华锋, 周枫, 李勇, 陈朝譞, 汪子祺, 孙正星. 基于布谷鸟—BP神经网络的页岩脆性指数预测研究[J]. 中国石油勘探, 2024, 29(2): 158-166.

Huang Kaixing, Liu Weihua, Wu Chaorong, Hu Huafeng, Zhou Feng, Li Yong, Chen Chaoxuan, Wang Ziqi, Sun Zhengxing. Prediction of shale brittleness index based on cuckoo-BP neural network[J]. China Petroleum Exploration, 2024, 29(2): 158-166.

参考文献

[1] 宋岩，姜林，马行陟.非常规油气藏的形成及其分布特征[J].古地理学报，2013,15(5):605-614.
Song Yan, Jiang Lin, Ma Xingdou. Formation and distribution characteristics of unconventional reservoirs[J]. Journal of Paleogeography, 2013,15(5):605-614.
[2] 邹才能，杨智，张国生，等.常规—非常规油气“有序聚集”理论认识及实践意义[J].石油勘探与开发，2014,41(1):14-26.
Zou Caineng, Yang Zhi, Zhang Guosheng, et al. Theoretical understanding and practical significance of“orderly accumulation”of conventional unconventional oil and gas[J].Petroleum Exploration and Development, 2014,41(1):14-26.
[3] 李澈，李雨澈，郭佳.地球物理技术在页岩储层脆性评价中的应用[J].当代化工，2014,43(5):733-734.
Li Che, Li Yuche, Guo Jia. Application of geophysical technology in shale reservoir brittleness evaluation[J]. Contemporary Chemical Industry, 2014,43(5):733-734.
[4] 陈吉，肖贤明.南方古生界3套富有机质页岩矿物组成与脆性分析[J].煤炭学报，2013,38(5):822-826.
Chen Ji, Xiao Xianming. Mineral composition and brittleness of three sets of Paleozoic organic-rich shale in China South area[J]. Journal of China Coal Society, 2013,38(5):822-826.
[5] 李钜源.东营凹陷泥页岩矿物组成及脆度分析[J].沉积学报，2013, 31(4):616-620.
Li Juyuan. Analysison mineral components and frangibility of shales in Dongying Depression[J]. Acta Sedimentologica Sinica, 2013,31(4): 616-620.
[6] Jarvie D M, Hill R J, Ruble T E, et al. Unconventional shale-gas systems: the Mississippian Barnett Shale of north-central Texas as one model for thermogenic shale-gas assessment[J]. AAPG Bulletin, 2007,91(4):475-499.
[7] Hajiabdolmajid V, Kaiser P. Brittleness of rock and stability assessment in hard rock tunneling[J]. Tunnelling and Underground Space Technology, 2003,18(1):35-48.
[8] 李庆辉，陈勉，金衍,等.页岩脆性的室内评价方法及改进[J].岩石力学与工程学报，2012,31(8):1681-1685.
Li Qinghui, Chen Mian, Jin Yan, et al. Indoor evaluation method for shale brittleness and improvement[J]. Chinese Journal of Rock Mechanics and Engineering, 2012,31(8):1681-1685.
[9] Rickman R, Mullen M J, Petre J. E, et al. A practical use of shale petrophysics for stimulation design optimization: all shale plays are not clones of the Barnett Shale[C]//SPE Annual Technical Conference and Exhibition.Society of Petroleum Engineers, 2008:1-11.
[10] 刘恩龙，沈珠江.岩土材料的脆性研究[J].岩石力学与工程学报，2005,24(19):3449-3453.
Liu Enlong, Shen Zhujiang. Study on brittleness of geomaterials[J]. Chinese Journal of Rock Mechanics and Engineering, 2005,24(19): 3449-3453.
[11] Tarasov B, Potvin Y. Universal criteria for rock brittleness estimation under triaxial compression[J]. International Journal of Rock Mechanics and Mining Sciences, 2013,59(4):57-69.
[12] Hucka V, Das B. Brittleness determination of rocks by different methods[J]. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1974,11(10):389-392.
[13] Altindag R. Correlation of specific energy with rock brittleness concepts on rock cutting[J]. The Journal of The South African Institute of Mining and Metallurgy, 2003,103(3):163-171.
[14] 于庭，巴晶，钱卫，等.非常规油气储层脆性评价方法研究进展[J].地球物理学进展，2019,34(1):236-243.
Yu Ting, Ba Jing, Qian Wei, et al. Research progress in evaluation methods of unconventional reservoir brittleness[J].Progress in Geophysics, 2019,34(1):236-243.
[15] Wood D A. Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities[J]. Journal Pre-Proof, 2020,1(9):1-41.
[16] Lai J, Wang G, Huang L, et al. Brittleness index estimation in a tight shaly sandstone reservoir using well logs[J]. Journal of Natural Gas Science and Engineering, 2015,1(27):1536-1545.
[17] Jin X C, Shah S, Truax J A. practical petrophysical approach for brittleness prediction from porosity and sonic logging in shale reservoirs[C]. SPE Annual Technical Conference and Exhibition, 2014,10:10-27.
[18] Ye Y, Tang S, Xi Z, et al. A new method to predict brittleness index for shale gas reservoirs: insights from well logging data[J]. Journal of Petroleum Science and Engineering, 2022,2(8):1-14.
[19] 贾凌霄，贾家磊，张毅，等.改进BP神经网络在裂缝预测中的应用[J].工程地球物理学报，2012,9(3):301-304.
Jia Lingxiao, Jia Jialei, Zhang Yi, et al. Application of improved BP neural network to fracture prediction[J]. Chinese Journal of Engineering Geophysics, 2012,9(3):301-304.
[20] 张赛民，周竹生，陈灵君.应用一种改进BP神经网络算法预测密度曲线[J].物探化探计算技术，2007,29(6):497-500.
Zhang Saimin, Zhou Zhusheng, Chen Lingjun. Density log prediction by using an improved BP neural network[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2007,29(6):497-500.
[21] 周传友，李少华，何维领，等.BP神经网络在岩相预测中的应用[J].物探化探计算技术,2020,42(6):743-750.
Zhou Chuanyou, Li Shaohua, He Weiling, et al. Application of BP neural network in seismic-lithofacies prediction[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2020, 42(6):743-750.
[22] 周政．BP神经网络的发展现状综述[J].山西电子技术，2008,1(2): 90-92.
Zhou Zheng. Overview of the development status of BP neural network[J]. Shanxi Electronic Technology, 2008,1(2):90-92.
[23] 孙晨，李阳，李晓戈，等.基于布谷鸟算法优化BP神经网络模型的股价预测[J].计算机应用与软件，2016,33(2):276-279.
Sun Chen, Li Yang, Li Xiaoge, et al. Stock price prediction based on BP neural network model optimized by cuckoo algorithm[J]. Computer Application and Software, 2016,33(2): 276-279.
[24] 张高记，李杨莉，姚引娣.基于改进CS优化BP神经网络的降水量预测模型[J].数学的实践与认识，2022,52(5):103-110.
Zhang Gaoji, Li Yangli, Yao Yindi. Precipitation prediction model based on improved CS optimized BP neural network[J].Mathematical Practice and Understanding, 2022,52(5):103-110.
[25] 李锋，攀玉和，王安敏，等.基于改进布谷鸟优化BP神经网络控制环境参量研究[J].计算机与数字工程，2021,49(8):1505-1524.
Li Feng, Pan Yuhe, Wang Anmin, et al. Research on environmental parameters control based on improved cuckoo bird optimization BP neural network[J]. Computer and Digital Engineering, 2021,49(8):1505-1524.
[26] 陈尔奎，韩清香，周峦.基于布谷鸟算法和BP神经网络的矿用变压器故障诊断[J].煤炭技术，2018,37(6):223-224.
Chen Erkui, Han Qingxiang, Zhou Luan. Fault Diagnosis of Mining Transformer Based on Cuckoo Algorithm and BP Neural Network[J]. Coal Technology, 2018,37(6):223-224.
[27] Noori R, Karbassi A, Salman S M. Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction[J]. Journal of Environmental Management, 2010,91(3):767-771.
[28] 杨进，高德利，郑权方，等.岩石声波时差与岩石可钻性的关系及其应用[J].钻采工艺，1998,21(2):1-3.
Yang Jin, Gao Deli, Zheng Quanfang, et al. The relationship between rock acoustic transit time and rock drillability and its application[J]. Drilling & Production Technology, 1998,21(2): 1-3.
[29] 贾建亮.基于地球化学—地球物理的松辽盆地上白垩统油页岩识别与资源评价[D].长春：吉林大学，2012.
Jia Jianliang. Identification and resource evaluation of Upper Cretaceous oil shale in the Songliao Basin based on geochemistry geophysics[D]. Changchun: Jilin University, 2012.
[30] 翟文宝，李军，周英操，等.基于测井资料的页岩储层可压裂性评价新方法[J].岩性油气藏，2018,30(3):112-123.
Yao Wenbao, Li Jun, Zhou Yingcao, et al. A new method for fracturing evaluation of shale reservoirs based on logging data[J]. Lithologic Reservoirs, 2018,30(3):112-123.
[31] 高辉，张晓，何梦卿，等.基于测井数据体的页岩油储层可压裂性评价研究[J].地球物理学进展，2018,33(2):603-612.
Gao Hui, Zhang Xiao, He Mengqing, et al. Study on evaluation of shale oil reservoir fracability based on well logging data volume[J]. Progress in Geophysics, 2018,33(2):603-612.
[32] 袁俊亮，邓金根，张定宇，等.页岩气储层可压裂性评价技术[J].石油学报，2013,34(3):523-527.
Yuan Junliang, Deng Jingen, Zhang Dingyu, et al. Fracability evaluation of shale-gas reservoirs[J]. Acta Petrolei Sinica, 2013,34(03):523-527.
[33] Potter C C, Foltinek D S. Formation elastic parameters by deriving S-wave velocity logs[J]. Crewes,1997,9(1):10-23.