煤层气压裂效果与影响因素之间存在的非线性关系难以从机理层面进行系统分析,针对该问题,提出了一种基于聚类匹配的压裂效果主控因素识别方法。该方法从数据中挖掘影响因素的内在联系而非通过主观判断来分析压裂效果与影响因素之间的联系。首先,以压裂后的产气指标数据为研究对象,利用凝聚聚类方法对样本井进行分类和效果评价;其次,利用K-means聚类算法结合信息增益排序与相关性分析,对影响因素进行分类与筛选,从中选取前置液用量、携砂液用量、含气饱和度、含气量、垂直应力、支撑剂用量、破裂压力、加砂强度8个因素;最后,对筛选出的因素进行样本聚类,将聚类结果与压裂效果的评价分类结果进行聚类匹配,实现了压裂效果主控因素的识别。与其他主控因素识别方法对比,验证了该方法的有效性和可操作性。该研究可为优化二次压裂施工方案提供技术支持。
It is difficult to systematically analyze the nonlinear relationship between CBM fracturing effect and influencing factors from the perspective of mechanism, and a method based on cluster matching was proposed to identify the main controlling factors of fracturing effect. The method analyzed the connection between fracturing effect and influencing factors by tapping into the intrinsic connection of influencing factors based on data rather than by subjective judgment. Firstly, the data of gas production indicator after fracturing was taken as the object of study and the agglomerative clustering method was employed to classify and evaluate the effect of sample wells. Secondly, the influencing factors were classified and screened by K-means clustering algorithm in conjunction with information gain sequencing and correction analysis, from which 8 factors were selected: prepad fluid dosage, proppant-carrying fluid dosage, gas saturation, gas content, vertical stress, proppant dosage, fracturing pressure and proppant filling strength. Finally, the selected factors were clustered, and the clustering results were clustered and matched with the evaluation and classification results of fracturing effect, so as to identify the main controlling factors of fracturing effect. The effectiveness and operability of this method were verified by comparing it with other methods for identifying the main controlling factors. The study can provide technical support for optimizing the secondary fracturing plan.
[1] CHEN Y,LIU D N,CAI Y D,et al. Fracturing curve and its corresponding gas productivity of coalbed methane wells in the Zhengzhuang block,southern Qinshui Basin,north China[J].Energy Exploration & Exploitation,2020,38(5):2048-4054.
[2] FENG F P,HUANG R,GAO B Y,et al.A new method for evaluating the effectiveness of hydraulic fracturing in tight reservoirs[J].Arabian journal of geosciences,2019,12(12):1866-7511.
[3] SONG H Q,DU S Y,YANG J S,et al.Evaluation of hydraulic fracturing effect on coalbed methane reservoir based on deep learning method considering physical constraints[J].Journal of Petroleum Science and Engineering,2022,212:110360.
[4] ZHANG Q,WANG K F,ZHOU S L,et al.Influence of geological factors on hydraulic fracturing effect of coalbed methane wells in Shizhuangnan Block,Qinshui Basin[J].Journal of the China Coal Society,2020,45(7):2636-2645.
[5] ZUO Y Q,XIAO Q H,WANG Z Y,et al.Analysis of the main controlling factors of the development effect of a single well for coalbed methane[J].Earth and Environmental Science,2020,495:012047.
[6] 冯立杰,贾依帛,岳俊举,等.煤层气开采关键地质影响因素[J].石油与天然气地质,2017,38(6):1105-1112.
FENG Lijie,JIA Yibo,YUE Junju,et al.Key geological factors influencing coal-bed methane exploitation[J].Oil & Gas Geology,2017,38(6):1105-1112.
[7] 王玉海,王庆红,闫桂芳,等.煤层气井压裂效果评价方法[J].油气井测试,2010,19(5):44-47,77.
WANG Yuhai,WANG Qinghong,YAN Guifang,et al.Evaluation method of fracturing effect for coal-bed gas wells[J].Well Testing,2010,19(5):44-47,77.
[8] 计勇,郭大立,赵金洲,等.影响煤层气井压后产量的因素分析——以韩城区块为例[J].煤田地质与勘探,2012,40(1):10-13.
JI Yong,GUO Dali,ZHAO Jinzhou,et al.Factors affecting the production of coalbed methane well after fracturing:a case study of Hancheng Block[J].Coal Geology & Exploration,2012,40(1):10-13.
[9] 李玉伟,艾池,胡超阳,等.应用模糊综合评判和灰色关联度分析评价水平井多级压裂效果[J].数学的实践与认识,2014,44(2):51-56.
LI Yuwei,AI Chi,HU Chaoyang,et al.Evaluating multistage fracturing effect of horizontal wells using fuzzy comprehensive evaluation and gray relational grade analysis[J].Journal of Mathematics in Practice and Theory,2014,44(2):51-56.
[10] 谢诗章,许浩,汤达祯,等.煤层气储层产水量的分类和成因分析[J].煤田地质与勘探,2016,44(1):47-50,55.
XIE Shizhang,XU Hao,TANG Dazhen,et al.Analysis of classification and causes of water production in CBM reservoir[J].Coal Geology & Exploration,2016,44(1):47-50,55.
[11] WU N,CHEN D,SUN B,et al.Evaluation on fracturing effect based on classification method[J].Journal of the China Coal Society,2018,43(6):1694-1700.
[12] 檀朝东,贺甲元,周彤,等.基于PCA-BNN的页岩气压裂施工参数优化[J]. 西南石油大学学报(自然科学版),2020,42(6):56-62.
TAN Chaodong,HE Jiayuan,ZHOU Tong,et al.A study on the optimization of fracturing operation parameters based on PCA–BNN[J].Journal of Southwest Petroleum University(Science & Technology Edition),2020,42(6):56-62.
[13] GIUSEPPE B.无监督学习[M].瞿源,刘江峰,译.北京:人民邮电出版社,2020:36-42.
GIUSEPPE B.Unsupervised Learning[M].Translators:QU Yuan,LIU Jiangfeng.Beijing:Post & Telecom Press Co.,Ltd.,2020:36-42.
[14] 章永来,周耀鉴.聚类算法综述[J].计算机应用,2019,39(7):1869-1882.
ZHANG Yonglai,ZHOU Yaojian. General overview on clustering algorithms[J].Journal of Computer Applications,2019,39(7):1869-1882.
[15] 杨兆中,熊俊雅,刘俊,等.基于Apriori关联分析的煤层气压裂效果主控因素识别[J].油气藏评价与开发,2020,10(4):63-69.
YANG Zhaozhong,XIONG Junya,LIU Jun,et al.Identification of main controlling factors on performance of CBM well fracturing based on Apriori association analysis[J].Reservoir Evaluation and Development,2020,10(4):63-69.
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