收稿：2020-08-12，
纸质出版：2021-10-28
移动端阅览
识别储层岩性是开展地层对比和沉积展布等地质基础研究工作的重要前提。致密砂岩储层多为砂、泥岩薄互层，形成的岩性不仅种类较多，而且大部分岩性的测井响应也较为相似，为相关的岩性识别工作带来了困难。基于机器学习技术在模式识别上具有强大的分析性能，选用了稳定性好且计算效率高的XGBoost模型来解决致密砂岩储层岩性识别问题。该模型在建模过程中需要较多的经验参数参与，且计算速度随着自变量的增加而逐渐降低，为此提出了采用CRBM模型和PSO模型对其进行改进。CRBM模型具备数据提取功能，可从源数据中挖掘出更少但更利于建模的新自变量，而PSO模型可通过迭代计算确定XGBoost所有经验参数的最优值。以姬塬油田西部长4+5段部分取心井资料为基础，通过设计两个实验来验证提出的混合模型的预测能力。为加强验证效果，在实验中加入了PNN和SVM的优化模型进行对比。实验后发现所提出模型的预测准确率最高，均在90%以上。实验结果表明所提出的模型不仅能有效解决致密砂岩储层岩性识别问题，且较以往经典预测模型更具推广应用性。
Lithology identification is universally regarded as a prerequisite for some fundamental geological work such as stratigraphic correlation and analysis of sedimentary system distribution. The tight sandstone reservoirs are generally composed of multiple thin sandstone-mud alternating beds
featuring a variety of lithological types
most of which are similar in log responses
posing great difficulty for lithology identification. Given the powerful analytical performance of machine learning techniques shown on pattern recognition
XGBoost
a stable and highly efficient model is adopted to predict lithology of the tight sandstone reservoirs. However
this model needs many empirical parameters to complete its prediction
and its computing efficiency will be reduced when it deals with more independent variables. The CRBM and PSO models are
therefore
introduced to improve the prediction performance. CRBM is functional to extract features from original data so that fewer but more significant independent variables can be created by the CRBM in modeling. While PSO is effective to determine the optimal values via iterative computation for those empirical parameters used by XGBoost. Based on the data derived from some cored wells of Chang 4+5 Members in western Jiyuan oilfield
two experiments are designed to validate the prediction performance of the hybrid model proposed
CRBM-PSO-XGBoost. Furthermore
two optimized models formed by PNN and SVM are selected to make a comparison
as a way to consolidate the validation effect. The experiment results manifest that the prediction accuracy comes to peak by the proposed model
up to over 90%. In all
the hybrid model proposed in the study
capable of identifying lithology of tight sandstone reservoirs
is of wider application to lithology prediction compared with previous models.
孔强夫 , 杨才 , 李浩 , 等 . 基于图论聚类和最小临近算法的岩性识别方法 [J ] . 石油与天然气地质 , 2020 , 41 ( 4 ): 884 - 890 . http://ogg.pepris.com/CN/10.11743/ogg20200420 http://ogg.pepris.com/CN/10.11743/ogg20200420
Kong Qiangfu , Yang Cai , Li Hao , et al . A lithology recognition method based on multi-resolution graph-based clustering and K-nearest neighbor: A case study from the Leikoupo formation carbonate reservoirs in western Sichuan Basin [J ] . Oil & Gas Geology , 2020 , 41 ( 4 ): 884 - 890 . http://ogg.pepris.com/CN/10.11743/ogg20200420 http://ogg.pepris.com/CN/10.11743/ogg20200420
张天付 , 黄理力 , 倪新锋 , 等 . 塔里木盆地柯坪地区下寒武统吾松格尔组岩性组合及其成因与勘探意义 [J ] . 石油与天然气地质 , 2020 , 41 ( 5 ): 928 - 940 . http://ogg.pepris.com/CN/10.11743/ogg20200504 http://ogg.pepris.com/CN/10.11743/ogg20200504
Zhang Tianfu , Huang Lili , Ni Xinfeng , et al . Lithological combination, genesis and exploration significance of the lower Cambrain Wusonggeer formation of Kalpin area in Tarim Basin: Insight through the deepest Asian onshore well-Well Luntan 1 [J ] . Oil & Gas Geology , 2020 , 41 ( 5 ): 928 - 940 . http://ogg.pepris.com/CN/10.11743/ogg20200504 http://ogg.pepris.com/CN/10.11743/ogg20200504
黄彦庆 , 刘忠群 , 林恬 , 等 . 川东北元坝地区徐家河组三段基于相控的相对优质储层预测 [J ] . 石油与天然气地质 , 2021 , 42 ( 4 ): 863 - 872 . http://ogg.pepris.com/CN/10.11743/ogg20210408 http://ogg.pepris.com/CN/10.11743/ogg20210408
Huang Yanqing , Liu Zhongqun , Lin Tian , et al . Lithofacies-based prediction of relatively high-quality reservoirs of the Xu 3 member in Yuanba area, northeastern Sichuan Basin [J ] . Oil & Gas Geology , 2021 , 42 ( 4 ): 863 - 872 . http://ogg.pepris.com/CN/10.11743/ogg20210408 http://ogg.pepris.com/CN/10.11743/ogg20210408
郑见中 . 辽河坳陷兴隆台潜山带中生界储层测井评价方法 [J ] . 石油地质与工程 , 2019 , 33 ( 6 ): 29 - 33 . DOI: 10.3969/j.issn.1673-8217.2019.06.007 http://doi.org/10.3969/j.issn.1673-8217.2019.06.007
Zheng Jianzhong . Logging evaluation method of Mesozoic reservoir in Xinlongtai buried hill belt of Liaohe depression [J ] . Petroleum Geology and Engineering , 2019 , 33 ( 6 ): 29 - 33 . DOI: 10.3969/j.issn.1673-8217.2019.06.007 http://doi.org/10.3969/j.issn.1673-8217.2019.06.007
闫林 , 冉启全 , 高阳 , 等 . 新疆芦苇沟组致密油赋存形式及可动用性评价 [J ] . 油气藏评价与开发 , 2017 , 7 ( 6 ): 20 - 26 . DOI: 10.3969/j.issn.2095-1426.2017.06.004 http://doi.org/10.3969/j.issn.2095-1426.2017.06.004
Ran Lin , Ran Qiquan , Gao Yang , et al . Tight oil occurence form and recoverability evaluation of tight oil reservoir in Lucaogou Formation of Xinjiang [J ] . Reservoir Evaluation and Development , 2017 , 7 ( 6 ): 20 - 26 . DOI: 10.3969/j.issn.2095-1426.2017.06.004 http://doi.org/10.3969/j.issn.2095-1426.2017.06.004
袁述武 , 李想 , 史乐 , 等 . 克拉玛依油田六、七、九区石炭系内幕有利火山岩储层岩性分布预测 [J ] . 大庆石油地质与开发 , 2019 , 38 ( 6 ): 40 - 45 . https://www.cnki.com.cn/Article/CJFDTOTAL-DQSK201906005.htm https://www.cnki.com.cn/Article/CJFDTOTAL-DQSK201906005.htm
Yuan Shuwu , Li Xiang , Shi Le , et al . Prediction of the lithology distribution of favorable volcanic reservoirs in the inside Carboniferous system in Block 6, 7 and 9 of Karamay Oilfield [J ] . Petroleum Geology & Oilfield Development in Daqing , 2019 , 38 ( 6 ): 40 - 45 . https://www.cnki.com.cn/Article/CJFDTOTAL-DQSK201906005.htm https://www.cnki.com.cn/Article/CJFDTOTAL-DQSK201906005.htm
赵彤彤 , 张春雷 , 张春雨 , 等 . 基于模糊熵的KNN分类模型在岩性识别中的应用 [J ] . 计算机工程与应用 , 2018 , 54 ( 24 ): 260 - 265 . DOI: 10.3778/j.issn.1002-8331.1709-0084 http://doi.org/10.3778/j.issn.1002-8331.1709-0084
Zhao Tongtong , Zhang Chunlei , Zhang Chunyu , et al . Application of KNN classification model based on fuzzy entropy in lithology recognition [J ] . Computer Engineering and Applications , 2018 , 54 ( 24 ): 260 - 265 . DOI: 10.3778/j.issn.1002-8331.1709-0084 http://doi.org/10.3778/j.issn.1002-8331.1709-0084
张梓童 , 张春雷 , 张艳 , 等 . 数据空间结构性及在KNN算法中的应用 [J ] . 数学的实践与认知 , 2019 , 49 ( 1 ): 195 - 202 . https://www.cnki.com.cn/Article/CJFDTOTAL-SSJS201901025.htm https://www.cnki.com.cn/Article/CJFDTOTAL-SSJS201901025.htm
Zhang Zitong , Zhang Chunlei , Zhang Yan , et al . Data space structure and application in KNN algorithm [J ] . Mathematics in Practice and Theory , 2019 , 49 ( 1 ): 195 - 202 . https://www.cnki.com.cn/Article/CJFDTOTAL-SSJS201901025.htm https://www.cnki.com.cn/Article/CJFDTOTAL-SSJS201901025.htm
陈玉林 , 李戈理 , 杨智新 , 等 . 基于KNN算法识别合水地区长7储层岩性岩相 [J ] . 测井技术 , 2020 , 44 ( 2 ): 182 - 185 . https://www.cnki.com.cn/Article/CJFDTOTAL-CJJS202002015.htm https://www.cnki.com.cn/Article/CJFDTOTAL-CJJS202002015.htm
Chen Yulin , Li Geli , Yang Zhixin , et al . Identification of lithology and lithofacies of Chang 7 reservoir in Heshui area by KNN algorithm [J ] . Well Logging Technology , 2020 , 44 ( 2 ): 182 - 185 . https://www.cnki.com.cn/Article/CJFDTOTAL-CJJS202002015.htm https://www.cnki.com.cn/Article/CJFDTOTAL-CJJS202002015.htm
赵杰 , 李春华 . 基于神经网络的两种岩性识别方法的研究 [J ] . 现代电子技术 , 2009 , 32 ( 23 ): 136 - 138 . DOI: 10.3969/j.issn.1004-373X.2009.23.043 http://doi.org/10.3969/j.issn.1004-373X.2009.23.043
Zhao Jie , Li Chunhua . Research on two lithology identification methods based on neural network [J ] . Modern Electronics Technique , 2009 , 32 ( 23 ): 136 - 139 . DOI: 10.3969/j.issn.1004-373X.2009.23.043 http://doi.org/10.3969/j.issn.1004-373X.2009.23.043
陈刚 . PNN在煤田随钻测井岩性判识应用研究 [J ] . 地质与资源 , 2018 , 27 ( 1 ): 103 - 106 . DOI: 10.3969/j.issn.1671-1947.2018.01.015 http://doi.org/10.3969/j.issn.1671-1947.2018.01.015
Chen Gang . Application of PNN in the lithology identification of logging while drilling in coal field [J ] . Geology and Resources , 2018 , 27 ( 1 ): 103 - 106 . DOI: 10.3969/j.issn.1671-1947.2018.01.015 http://doi.org/10.3969/j.issn.1671-1947.2018.01.015
陈刚 , 汪凯斌 , 薛必辞 , 等 . 随钻测井中岩性识别方法的对比及应用 [J ] . 煤田地质与勘探 , 2018 , 46 ( 1 ): 165 - 169 . DOI: 10.3969/j.issn.1001-1986.2018.01.028 http://doi.org/10.3969/j.issn.1001-1986.2018.01.028
Chen Gang , Wang Kaibin , Xue Bici , et al . Comparison and application of LWD lithology identification method [J ] . Coal Geology & Exploration , 2018 , 46 ( 1 ): 165 - 169 . DOI: 10.3969/j.issn.1001-1986.2018.01.028 http://doi.org/10.3969/j.issn.1001-1986.2018.01.028
李政宏 , 刘永福 , 张立强 , 等 . 数据挖掘方法在测井岩性识别中的应用 [J ] . 断块油气藏 , 2019 , 26 ( 6 ): 713 - 718 . https://www.cnki.com.cn/Article/CJFDTOTAL-DKYT201906008.htm https://www.cnki.com.cn/Article/CJFDTOTAL-DKYT201906008.htm
Li Zhenghong , Liu Yongfu , Zhang Liqiang , et al . Application of data mining method in lithology identification using well log [J ] . Fault-block Oil & Gas Field , 2019 , 26 ( 6 ): 713 - 718 . https://www.cnki.com.cn/Article/CJFDTOTAL-DKYT201906008.htm https://www.cnki.com.cn/Article/CJFDTOTAL-DKYT201906008.htm
林香亮 , 朱建伟 , 刘光涛 , 等 . 基于PCA-SVM的砂砾岩岩性识别 [J ] . 长江大学学报(自然科学版) , 2020 , 17 ( 1 ): 21 - 26 . DOI: 10.3969/j.issn.1673-1409.2020.01.005 http://doi.org/10.3969/j.issn.1673-1409.2020.01.005
Lin Xiangliang , Zhu Jianwei , Liu Guangtao , et al . Lithologic identification of glutenite based on PCA-SVM [J ] . Journal of Yangtze University (Natural Science Edition) , 2020 , 17 ( 1 ): 21 - 26 . DOI: 10.3969/j.issn.1673-1409.2020.01.005 http://doi.org/10.3969/j.issn.1673-1409.2020.01.005
石锁 , 余继峰 , 曹慧涛 , 等 . 基于高斯核SVM的储层岩性识别——以东濮凹陷上古生界碎屑岩为例 [J ] . 中国科技论文 , 2020 , 15 ( 1 ): 112 - 118 . DOI: 10.3969/j.issn.2095-2783.2020.01.017 http://doi.org/10.3969/j.issn.2095-2783.2020.01.017
Shi Suo , Yu Jifeng , Cao Huitao , et al . Reservoir lithology identification based on SVM using radial basis function: an example of Upper Paleozoic clastic rocks in Dongpu sag [J ] . China Science Paper , 2020 , 15 ( 1 ): 112 - 118 . DOI: 10.3969/j.issn.2095-2783.2020.01.017 http://doi.org/10.3969/j.issn.2095-2783.2020.01.017
Chen T, Guestrin C. XGboost: A scalable tree boosting system[C]//ACM SIGKDD International Conference on Konwledge Discovery and Data Mining, 2016: 785-794.
Zhou K , Zhang J , Ren Y , et al . A gradient boosting decision tree algorithm combining synthetic minority over-sampling technique for lithology identification [J ] . Geophysics , 2020 , 85 , 1 - 52 . DOI: 10.1190/geo2020-0711-fe.1 http://doi.org/10.1190/geo2020-0711-fe.1
Dev V A , Eden M R . Formation lithology classification using scalable gradient boosted decision trees [J ] . Computers & Chemical Enginee-ring , 2019 , 128 , 392 - 404 . http://www.sciencedirect.com/science/article/pii/S0098135419302200 http://www.sciencedirect.com/science/article/pii/S0098135419302200
闫星宇 , 顾汉明 , 肖逸飞 , 等 . XGBoost算法在致密砂岩气层测井解释中的应用 [J ] . 石油地球物理勘探 , 2019 , 54 ( 2 ): 447 - 455 . https://www.cnki.com.cn/Article/CJFDTOTAL-SYDQ201902024.htm https://www.cnki.com.cn/Article/CJFDTOTAL-SYDQ201902024.htm
Yan Xingyu , Gu Hanming , Xiao Yifei , et al . XGBoost algorithm applied in the interpretation of tight-sand gas reservoir on well logging data [J ] . Oil Geophysical Prospecting , 2019 , 54 ( 2 ): 447 - 455 . https://www.cnki.com.cn/Article/CJFDTOTAL-SYDQ201902024.htm https://www.cnki.com.cn/Article/CJFDTOTAL-SYDQ201902024.htm
杨维 , 李歧强 . 粒子群优化算法综述 [J ] . 中国工程科学 , 2004 , 6 ( 5 ): 87 - 94 . DOI: 10.3969/j.issn.1009-1742.2004.05.018 http://doi.org/10.3969/j.issn.1009-1742.2004.05.018
Yang Wei , Li Qiqiang . Survey on particle swarm optimization algorithm [J ] . Engineering Science , 2004 , 6 ( 5 ): 87 - 94 . DOI: 10.3969/j.issn.1009-1742.2004.05.018 http://doi.org/10.3969/j.issn.1009-1742.2004.05.018
刘建华. 粒子群算法的基本理论及其改进研究[D]. 长沙: 中南大学, 2009.
Liu Jianhua. The research of basic theory and improvement on particle swarm optimization[D]. Changsha: Center South University, 2009.
温阳东 , 李龙剑 . 基于LDIW-PSO算法的BP神经网络在压力传感器中的应用 [J ] . 化工自动化及仪表 , 2014 , 41 ( 9 ): 1031 - 1034 . DOI: 10.3969/j.issn.1000-3932.2014.09.011 http://doi.org/10.3969/j.issn.1000-3932.2014.09.011
Wen Yangdong , Li Longjian . Application of LDIW-PSO algorithm-based BP neural network in pressure sensor [J ] . Control and Instruments in Chemical Industry , 2014 , 41 ( 9 ): 1031 - 1034 . DOI: 10.3969/j.issn.1000-3932.2014.09.011 http://doi.org/10.3969/j.issn.1000-3932.2014.09.011
Tang T B , Murray A F . Adaptive sensor modeling and classification using a continuous restricted Boltzmann machine (CRBM) [J ] . Neurocomputing , 2006 , 70 ( 7-9 ): 1198 - 1206 . http://www.researchgate.net/profile/Tong_Boon_Tang2/publication/222821880_Adaptive_sensor_modelling_and_classification_using_a_continuous_restricted_Boltzmann_machine_CRBM/links/572bf40d08aef7c7e2c6ba9b.pdf http://www.researchgate.net/profile/Tong_Boon_Tang2/publication/222821880_Adaptive_sensor_modelling_and_classification_using_a_continuous_restricted_Boltzmann_machine_CRBM/links/572bf40d08aef7c7e2c6ba9b.pdf
Huang H B , Li R X , Yang M L , et al . Evaluation of vehicle interior sound quality using a continuous restricted Boltzmann machine-based DBN [J ] . Mechanical Systems & Signal Processing , 2017 , 84 , 245 - 267 . http://www.onacademic.com/detail/journal_1000039503729010_619b.html http://www.onacademic.com/detail/journal_1000039503729010_619b.html
Chen Y , Lu L , Li X . Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly [J ] . Journal of Geochemical Exploration , 2014 , 140 ( 4 ): 56 - 63 . http://or.nsfc.gov.cn/bitstream/00001903-5/457953/1/1000008293468.pdf http://or.nsfc.gov.cn/bitstream/00001903-5/457953/1/1000008293468.pdf
黎盼. 低渗透砂岩储层微观孔隙结构表征及生产特征分析[D]. 西安: 西北大学, 2019.
Li Pan. Microscopic pore structure characterization and production characteristic analysis of low-permeability sandstone reservoir: a case study on Chang 4+5 and Chang 6 reservoir of T area in Jiyuan Oilfield[D]. Xi'an: Northwest University, 2019.
赵冰瑶. 姬塬油田王盘山地区长4+5储层成岩相微观孔隙结构及渗流特征研究[D]. 西安: 西北大学, 2018.
Zhao Bingyao. The study on microscopic pore structure and percolation characteristics of diagenetic facies in Chang 4+5 reservoir of Wangpanshan area, Jiyuan Oilfield[D]. Xi'an: Northwest University, 2018.
翟利华. 姬塬油田西部长4+5油层组沉积微相与单砂体研究[D]. 北京: 中国石油大学(北京), 2018.
Zhai Lihua. Study on sedimentary microfacies and single sandbodies on Chang 4+5 member in the western Jiyuan Oilfield[D]. Beijing: China University of Petroleum(Beijing), 2018.
沈佳男. 鄂尔多斯盆地姬塬油田西部长4+5油层组储层评价[D]. 北京: 中国石油大学(北京), 2018.
Shen Jianan. Reservoir evaluation of Chang 4+5 member in west Jiyuan Oilfield in Ordos Basin[D]. Beijing: China University of Petroleum(Beijing), 2018.
相关作者
相关机构
京公网安备11010802024621
