摘要
中国新疆玛湖致密砂砾岩油藏非均质性强,各井地质和施工条件参差不齐,压裂设计方案缺乏针对性,压裂效果的主控因素也有待进一步深化认识.为此,建立了一套基于机器学习的玛湖地区水平井产能预测及压裂设计优化的流程框架.以玛湖地区75口压裂水平井为例,针对储层和工程2类16个影响因素,采用随机森林算法确定了压后产能主控因素,建立了遗传算法优化的逆传播算法神经网络产能预测模型,并基于此对水平井的压裂进行优化设计.结果表明,现场2口井在优选压裂参数方案后,产能分别提高了9.3%和37.3%.建立的产能预测及压裂设计优化方法可为现场施工提供指导.
The Mahu tight glutenite reservoir is highly heterogeneous,and the geology and construction conditions of each well are uneven.The fracturing design scheme lacks pertinence.The main control factors of the fracturing effect need to be further understood.Therefore,we establish a set of machine learning-based process frameworks for horizontal well productivity prediction and fracturing design optimization in the Mahu area.Taking 75 fractured horizontal wells in the Mahu area as an example,based on 16 influencing factors of 2 types of geology and engineering,the random forest algorithm is used to determine the main control factors of post-fracturing productivity,and a error back Propagation(BP)neural network productivity prediction model optimized by genetic algorithm is established.Based on this,the optimal design of fracturing for horizontal wells is carried out.The results show that the productivity of two wells in the field is increased by 9.3%and 37.3%respectively after the fracturing parameter scheme is optimized.The established productivity prediction and fracturing design optimization methods can be applied to other oil fields and provide targeted guidance for on-site construction.
作者
马俊修
石胜男
陈进
张景臣
何小东
李雪晨
郭丁菲
MA Junxiu;SHI Shengnan;CHEN Jin;ZHANG Jingchen;HE Xiaodong;LI Xuechen;GUO Dingfei(Institute of Oil and Gas Technology,Research Institute of Engineering and Technology,Xinjiang Oilfield Company,Petro China,Karamay 834000,Xinjiang Uygur Autonomous Region,P.R.China;Unconventional Oil and Gas Science and Technology Research Institute,China University of Petroleum(Beijing),Beijing 102249,P.R.Chin)
出处
《深圳大学学报（理工版）》
CAS
CSCD
北大核心
2021年第6期621-627,共7页
Journal of Shenzhen University(Science and Engineering)
基金
新疆自治区科技厅天山青年计划资助项目(2018Q030)
中石油战略科技资助项目(ZLZX2020-02-07-03,ZLZX2020-02-07-05)
中国石油大学(北京)克校区基金资助项目(RCYJ2017B-01-003)
新疆克拉玛依市科技重大专项基金资助项目(2018ZD001B)
关键词
非常规油气藏
压裂优化
玛湖油田
机器学习
主控因素
随机森林
逆传播算法神经网络
unconventional reservoirs
fracturing optimization
Mahu oilfield
machine learning
main control factor
random forest
back propagation(BP)neural network
