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

Abstract

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 3-cluster case as low, medium and high performance wells. Analyzing the forecasting scenarios showed that shutting in all or high- and medium-performance wells altogether results in better economic outcomes. The results were most sensitive to the number of active wells and the oil price during the high-price period. This study demonstrated the effectiveness of unsupervised machine learning in well classification for operational decision making purposes. Operating companies may use this approach for improved decision making to select wells for extended shut-in during low oil-price periods. This approach would lead to cost savings especially in mature fields with low-profit margins.