Development of a data‐driven fuzzy screening model for enhanced oil recovery methods using an adaptive weighting system

Abstract

Screening of enhanced oil recovery (EOR) methods is a main prerequisite for EOR planning and design. In this study, an integrated data‐driven screening model (DDSM) is developed to improve EOR screening using the combined capabilities of the fuzzy expert approach (FEA) and support vector regression (SVR) techniques. In this study, EOR field data from the past 40 years were reviewed to generate an updated and reliable EOR criteria table as a basis to construct a fuzzy screening model. The DDSM was evaluated to determine the quantitative screening and ranking of EOR methods using seven field datasets, including the fast forecasting of the nominated EOR methods. In order to improve screening performance, a fuzzy model was integrated using 4 SVR models to predict the adaptive weights of the screening parameter for decision making. The SVR models can predict the recovery factor (RF) of EOR methods including gas, chemical, steam, and combustion to calculate the adaptive effective weight of the screening parameters. The SVR models were trained with datasets generated from simulations of the EOR process. The absolute average error (AAE) of the SVR models from the simulation varied within the range of 0.078–0.095 for the prediction of the RF. The DDSM results were compatible to the data published in other literature. In addition, the developed model can provide comparable results to common screening software. The results showed improvements due to the adaptive weighting system on the EOR methods’ screening for the studied reservoirs relative to the fuzzy engine with constant weights. The presented integrated model can guide the screening process to select the efficient EOR method in practical applications.