To address the problem that the main controlling factors of single shale gas well production in Weiyuan Block, Sichuan Basin are unknown, based on the geological and engineering data and production data of 132 gas wells which have been put in production for more than a year in the area, an analytical study was conducted by the gray correlation method. The study shows that, the main controlling factors affecting the first-year cumulative production of a single shale gas well are proppant dose, number of fracturing stages, median vertical depth of horizontal wells, fracturing section length, fracturing fluid volume, porosity, pressure coefficient and sanding intensity. It was clear that the machine learning method was higher in accuracy after comparison of the machine learning method and the traditional empirical formula method to predict the first year's cumulative output and initial output. Meanwhile, based on the analysis of the main controlling factors, the machine learning method applicable to the study area was preferably selected as the support vector machine method, and its prediction accuracy was higher than 90%. The study has an important implication to the productivity evaluation of similar shale gas blocks.
[1] 赵文智,贾爱林,位云生,等.中国页岩气勘探开发进展及发展展望[J].中国石油勘探,2020,25(1):31-34.
ZHAO Wenzhi,JIA Ailin,WEI Yunsheng,et al.Progress in shale gas exploration in China and prospects for future development[J].China Petroleum Exploration,2020,25(1):31-34.
[2] 庞进,李尚,刘洪,等.基于流态划分的页岩气井产量预测可靠性分析[J].特种油气藏,2018,25(2):60-64.
PANG Jin,LI Shang,LIU Hong,et al.Reliability analysis of production forecast for shale gas well based on flow regime classification[J].Special Oil & Gas Reservoirs,2018,25(2):60-64.
[3] 刘传斌,姜汉桥,李俊键,等.预测页岩气产量递减组合模型的研究[J].断块油气田,2015,22(4):481-483,487.
LIU Chuanbin,JIANG Hanqiao,LI Junjian,et al.Combination model for shale gas production decline prediction[J].Fault-Block Oil & Gas Field,2015,22(4):481-483,487.
[4] 李海涛,王科,补成中,等.预测页岩气单井产量及最终储量的经验法分析[J].特种油气藏,2019,26(3):74-78.
LI Haitao,WANG Ke,BU Chengzhong,et al.Empirical method analysis of individual well production and EUR forecast in shale gas reservoir[J].Special Oil & Gas Reservoirs,2019,26(3):74-78.
[5] 位云生,胡永全,赵金洲,等.人工神经网络方法在水力压裂选井评层中的应用[J].断块油气田,2005,12(4):42-44,91.
WEI Yunsheng,HU Yongquan,ZHAO Jinzhou,et al.Application of artificial nerve network method to evaluating & choosing target layer/well in hydraulic fracturing treatment[J].Fault-Block Oil & Gas Field,2005,12(4):42-44,91.
[6] 彭成勇,吕欣润,马新仿,等.海上低渗气田综合多因素压裂选井选层方法[J].断块油气田,2015,22(4):508-513.
PENG Chengyong,LYU Xinrun,MA Xinfang,et al.Fractured well and layer selection by multi-factor optimization for low permeability offshore gas field[J].Fault-Block Oil & Gas Field,2015,22(4):508-513.
[7] 孙东生,王红才,侯默,等.影响水力压裂效果的因素及人工神经网络评价[J].地质力学学报,2006,12(4):485-491.
SUN Dongsheng,WANG Hongcai,HOU Mo,et al.Factors influencing the effects of hydrofracturing in a low-permeability oilfield and potential evaluation using artificial neural network[J].Journal of Geomechanics,2006,12(4):485-491.
[8] 马志国,刘翠玲.人工神经网络在石油水压裂过程仿真中的应用[J].北京工商大学学报(自然科学版),2007,25(4):30-33.
MA Zhiguo,LIU Cuiling.Using artificial neural network for simulation of hydraulic fracfuring treatment[J].Journal of Beijing Technology and Business University(Natural Science Edition),2007,25(4):30-33.
[9] 严禛,伍星蓉.基于BFO-BP神经网络的储层预测研究[J].能源与环保,2017,39(7):210-213.
YAN Zhen,WU Xingrong.Study on BFO-BP neural network to reservoir thickness prediction[J].China Energy and Environmental Protection,2017,39(7):210-213.
[10] 王继强,韩大匡,金志勇,等.支持向量机在单井措施增油量预测中的应用[J].新疆石油地质,2008,29(1):109-112.
WANG Jiqiang,HAN Dagang,JIN Zhiyong,et al.Application of SVM to prediction of incremental well production by stimulation treatments[J].Xinjiang Petroleum Geology,2008,29(1):109-112.
[11] 王俊奇,郑欣.天然气产量预测的优化组合模型及其应用[J].西安石油大学学报(社会科学版),2015,24(5):6-9.
WANG Junqi,ZHENG Xin.Optimizing combination model of natural gas production prediction and its application[J].Journal of Xi'an Shiyou University(Social Science Edition),2015,24(5):6-9.
[12] 刘苏苏,孙立民.支持向量机与RBF神经网络回归性能比较研究[J].计算机工程与设计,2011,32(12):4202-4205.
LIU Susu,SUN Limin.Performance comparison of regression prediction on support vector machine and RBF neural network[J].Computer Engineering and Design,2011,32(12):4202-4205.
[13] 李李.沙二气藏水平井压裂参数优化与产量预测研究[D].成都:成都理工大学,2016.
LI Li.Study on optimization of fracturing parameters and production prediction of horizontal wells in Sha II gas reservoir[D].Chengdu:Chengdu University of Technology,2016.
[14] 胡海涛.基于BP神经网络和遗传算法的并行迭代优化研究[J].机械与电子,2019,37(1):26-32.
HU Haitao.Research on parallel iterative optimization based on BP neural network and genetic algorithm[J].Machinery & Electronics,2019,37(1):26-32.
[15] 陈智军,李洋莹.神经网络BP算法改进及其性能分析[J].软件导刊,2017,16(10):39-41.
CHEN Zhijun,LI Yangying.Analysis of improved BP algorithm of neural network and the performance[J].Software Guide,2017,16(10):39-41.
[16] 李小刚,郑阳,瞿建华,等.基于灰色关联方法的砂砾岩油藏压后产量影响因素分析[J].油气藏评价与开发,2016,6(4):28-33.
LI Xiaogang,ZHENG Yang,QU Jianhua,et al.Influential factors of postfracture production of glutenite reservoir based on gray correlation method[J].Reservoir Evaluation and Development,2016,6(4):28-33.
