Enhancing economic sustainability in mature oil fields:Insights from the clustering approach to select candidate wells for extended shut-in  

作　　者：B.Lobut E.Artun 

机构地区：[1]Istanbul Technical University,Department of Petroleum and Natural Gas Eng.,Istanbul,Türkiye [2]Sultan Qaboos University,Department of Petroleum and Chemical Eng.,Muscat,Oman

出　　处：《Artificial Intelligence in Geosciences》2024年第1期173-188,共16页地学人工智能（英文）

基　　金：support from research grants MGA-2021-42991 and MYL-2022-43726,funded by Istanbul Technical University-Scientific Research Projects,Turkey.Thissupportis gratefully acknowledged.

摘　　要：Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts.In periods of significant price drops,companies may consider extended duration of well shut-ins(i.e.temporarily stopping oil production)for economic reasons.For example,prices during the early days of the Covid-19 pandemic forced operators to consider shutting in all or some of their active wells.In the case of partial shut-in,selection of candidate wells may evolve as a challenging decision problem considering the uncertainties involved.In this study,a mature oil field with a long(50+years)production history with 170+wells is considered.Reservoirs with similar conditions face many challenges related to economic sustainability such as frequent maintenance requirements and low production rates.We aimed to solve this decision-making problem through unsupervised machine learning.Average reservoir characteristics at well locations,well production performance statistics and well locations are used as potential features that could characterize similarities and differences among wells.While reservoir characteristics are measured at well locations for the purpose of describing the subsurface reservoir,well performance consists of volumetric rates and pressures,which are frequently measured during oil production.After a multivariate data analysis that explored correlations among parameters,clustering algorithms were used to identify groups of wells that are similar with respect to aforementioned features.Using the field’s reservoir simulation model,scenarios of shutting in different groups of wells were simulated.Forecasted reservoir performance for three years was used for economic evaluation that assumed an oil price drop to$30/bbl for 6,12 or 18 months.Results of economic analysis were analyzed to identify which group(s)of wells should have been shut-in by also considering the sensitivity to different price levels.It was observed that wells can be characterized in the

关 键 词：Unsupervised learning CLUSTERING Mature oil fields Extended shut-in Well classification 

分 类 号：F42[经济管理—产业经济]