Automation of conceptual facilities design of oil and gas asset in the early stages

Background. The authors highlight two aspects of automation of conceptual facilities design. The technological aspect. The specificity of probabilistic geological and economic evaluations is the high degree of uncertainty of geological and development parameters, which leads to a large number of combinations and is the reason for the generation of more than 10 thousand variants of fluid production profiles. This set is the initial information for performing technological and cost calculations. The probabilistic formulation of the problem eliminates the possibility of manual labor by an engineer for each individual option. Software that implements the technological aspect in the probabilistic formulation of the problem must methodically correspond to the approaches to the early stages of conceptual design, cover all fundamental technological processes and calculations — gathering, treatment and external transport of oil, gas, condensate, calculation of the gas/energy balance. It is also important to ensure high speed of calculations; it should not exceed several seconds per option. There are no ready-made solutions on the global software market, except for the “Arrangment” module developed by the authors of this study [1, 2].

Expert aspect. For the first time, the authors set the task of creating an expert support system for an engineer as a variant of the implementation of artificial intelligence. The target result is that the expert system, based on limited initial data, automatically generates variants of the arrangement concept and generates a calculation task in the technological and cost module “Arrangement”.

Aim. Development of an expert system for automating the conceptual facilities design in express evaluations (screening) and in probabilistic geological and economic evaluations.

Materials and methods. The process of conceptual facilities design is supplemented by the participation of an expert system. The authors have formed an ontological knowledge base sufficient for automatic decisionmaking about the technical rationality/irrationality of options for development concepts — scenarios. In this case, an expert system is a way to implement artificial intelligence, based on modeling the logical decision-making process, with the ability to demonstrate the progress of “thinking” — cause-and-effect relationships leading to making a specific decision

Results. The functionality of the author’s prototype is demonstrated on a model oil case.

Conclusion. Further development of the expert system will allow the engineer to move from the role of a concept developer to the role of an expert reviewing an automatically generated concept.

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