摘要
页岩气井产量评价与预测对页岩气高效开发具有重要意义,现有解析模型的假设条件与实际页岩气井差异较大,数值模型计算难度大、效率低、不确定性高,导致页岩气井产量预测难度大。基于机器学习方法综合考虑地质因素与工程因素,整合页岩气开采全周期地质、钻井、压裂、生产等数据,对A页岩气藏气井产量进行了评价与预测。首先,对原始数据体进行处理,包括缺失值插补、相关性分析、异常值处理、主成分分析等,以减小数据的噪声;其次,采用聚类分析方法对页岩气井产量进行评价,研究影响A页岩气藏气井产量的主要因素;最后,应用随机森林方法预测A页岩气藏气井产量。结果表明:A区块页岩气藏中产量优、中、劣等井分别占比36.4%、37.8%、25.8%,其中压裂因素对A页岩气井产量评价结果影响最大。调参后的页岩气井产量预测结果准确度达到90%以上,预测结果较好,表明本文模型能够用于页岩气井产量预测。
Well production evaluation and prediction are of great significance to the efficient development of shale gas.The assumptions of existing analytical models are more different from actual shale gas wells,and the numerical model has the problems of high calculation difficulty,low efficiency and high uncertainty,which makes the prediction of shale gas well production more difficult.In this paper,the production rates of the gas wells in A shale gas reservoir were evaluated and predicted by integrating the geological,drilling,fracturing and production data in the whole cycle of shale gas production and considering geological factors and engineering factors comprehensively based on machine learning methods.Firstly,the initial data was processed based on themissing value interpolation,correlation analysis,outlier processing and principle component analysis to reduce data noise.Then,the production rates of shale gas wells were evaluated using the cluster analysis method,and the dominated factors influencing the production rates of the gas wells in A shale gas reservoir were analyzed.Finally,the production rates of the gas wells in A shale gas reservoir were predicted by using the random forest method.It is indicated that high,moderate and low production wells in A shale gas reservoir account for 36.4%,37.8%and 25.8%,respectively,and fracturing factor has the greatest influence on the evaluation results of the production rates of the gas wells in A shale gas reservoir.The prediction accuracy of shale gas well production after parameter adjustment is up to 90%,indicating better prediction accuracy.In conclusion,this proposed model can be used to predict production rates of shale gas wells.
作者
何佑伟
贺质越
汤勇
秦佳正
宋俊杰
汪勇
HE Youwei;HE Zhiyue;TANG Yong;QIN Jiazheng;SONG Junjie;WANG Yong(State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation,Southwest Petroleum University,Chengdu 610500,Sichuan,China)
出处
《石油钻采工艺》
CAS
北大核心
2021年第4期518-524,共7页
Oil Drilling & Production Technology
基金
国家自然科学基金委员会青年科学基金“致密气藏多井动态缝网数值试井理论研究”(编号:52004238)
中国博士后科学基金面上项目“致密气藏复杂缝网表征与水平井产气位置诊断方法研究”(编号:2019M663561)。
关键词
页岩气
产量预测
机器学习
聚类分析
随机森林
智慧油气田
shale gas
production prediction
machine learning
cluster analysis
random forest
smart oil and gas field
