A comprehensive techno-economic assessment of alkali–surfactant–polymer flooding processes using data-driven approaches

Abstract

The objective of this work is to propose a machine-learning assisted multi-objective optimization protocol to design and optimize alkali–surfactant–polymer flooding processes in the presence of multiple technical and economic objective functions. Several universal multi-layer neural networks are trained, and they act as universal surrogate models of high-fidelity numerical simulator to evaluate the objective functions involved in the optimization workflow. The HLD-NAC equation is employed to model the microemulsion phase behavior of the crude oil/brine/surfactant system with the presence of alkali agents. The validity of the expert systems is confirmed via extensive blind testing applications with error margins of 9%, which includes the predictions of fluid production and pressure responses. A multi-objective optimization workflow is structured by coupling the expert systems with particle swarm optimizer, which employs Pareto optimum theory to carry comprehensive ASP injection assessments considering various technical and economic objective functions. Moreover, the proposed workflow enables the decision makers to comprehend the project risks by taking the inherent uncertainties from crude oil market into accounts. The robustness of the optimization protocol is verified by introducing a synthetic field case study, which validates the competence of optimizing ASP injection design considering project net present value as an objective function. A series of Pareto front solutions are generated when more objective functions such as chemical efficacies and water cut reductions are included. Subsequently, the workflow evaluates the project economic uncertainties from the fluctuation of oil price, which helps the decision makers investigate the project risks from technical and economic perspectives.