Application of machine learning and optimization methods for long-term planning of production enhancement operations using Epsilon software package

UDK: 622.276.1/.4.001.57
DOI: 10.24887/0028-2448-2022-7-48-51
Key words: production enhancement operations, field development scenario, optimal portfolio of production enhancement operations, machine learning methods, objective function, taxation system
Authors: A.V. Nasybullin (Almetyevsk State Oil Institute, RF, Almetyevsk), A.A. Dyakonov (Almetyevsk State Oil Institute, RF, Almetyevsk), M.I. Mannapov (Tatneft PJSC, RF, Almetyevsk), Ram.Z. Sattarov (ТаtNIPIneft, RF, Bugulma), R.R. Khafizov (ТаtNIPIneft, RF, Bugulma), V.S. Timofeev (Novosibirsk State Technical University, RF, Novosibirsk), A.V. Faddeenkov (Novosibirsk State Technical University, RF, Novosibirsk)

Petroleum reservoir management involves generation of multiple field development scenarios and portfolio of the best production enhancement technologies of an oil company given production and capital cost restrictions. Decision support systems based on fuzzy set methods and expert systems have gained wide acceptance for selection of production enhancement operations. The paper describes one of the approaches to long-term strategic planning based on approximate estimates of the performance of various simulation cases for all fields operated by a company using statistical proxy models. This enables assessment of company expenses and long-term economic performance. In Tatneft Company, this approach is implemented in Epsilon software package. Epsilon integrates hierarchical proxy models of Lazurit workstation. The models fully reproduce the production history and generate remaining oil reserve maps showing unswept areas and bypassed oil pockets, being potential candidates for infill drilling. The workflow allows for automatic, step-wise introduction of planned operations with gradual increase of well grid spacing. Each stage provides for evaluation of geological risks and profitability of each well. Those wells that do not meet specified conditions are excluded. At every simulation time step, remaining oil reserves are automatically redistributed according to extent of their depletion. Automatic execution of the algorithm yields a nonuniform, dense grid of planned production enhancement operations that meet geological, operational, and economic constraints. The paper describes optimization problem solution for different objective functions and constraints. To estimate the production and economic performance of Tatneft’s production assets and to optimize the investments in the selected development scenarios for each target, we applied two taxation systems. Analysis of the key performance indicators and selection of the best development scenario for the Company’s fields made it possible to decide on the most appropriate taxation system for a particular field.cie


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