Failure to prioritize objectives and improper selection of candidate wells can have significant implications for both derived value and potential risk. This paper addresses the business problem of reducing the uncertainty of well-work-program outcomes so that more-informed choices can be made, enhancing the benefits and value of a well-work program. It illustrates the use of data-driven models to estimate key performance indicators for well-work jobs and to predict the likely outcome using predetermined success criteria.
Well work consists of the complete end-to-end business process covering any operation on an oil or gas well during or at the end of its productive life. Well-by-well reviews, with good support information, remain the best way of spotting large amounts of potential production. Data-driven incremental-learning models provide a set of intelligent tools that synthesize large volumes of data and make timely recommendations on the basis of learned historical behaviors and discovered hidden patterns across scattered heterogeneous sources.
This project examined a wide range of data-mining and machine-learning algorithms capable of dealing with large volumes of data, data-quality issues, and restrictive parameter constraints. The resulting model uses existing variables available at the planning for workover jobs as input to predict the likely outcome of individual jobs. Enhancing the decision-making process with reduced uncertainty for the well-work portfolio maximizes the overall program value and its yield on investment....
Enhancing Well-Work Efficiency With Data Mining and Predictive Analytics
01 October 2015