Fast and Cost-Effective Mathematical Models for Hydrocarbon-Immiscible Water Alternating Gas Incremental Recovery Factor Prediction

Abstract

Predicting the incremental recovery factor with an enhanced oil recovery (EOR) technique is a very crucial task. It requires a significant investment and expert knowledge to evaluate the EOR incremental recovery factor, design a pilot, and upscale pilot result. Water-alternating-gas (WAG) injection is one of the proven EOR technologies, with an incremental recovery factor typically ranging from 5 to 10%. The current approach of evaluating the WAG process, using reservoir modeling, is a very time-consuming and costly task. The objective of this research is to develop a fast and cost-effective mathematical model for evaluating hydrocarbon-immiscible WAG (HC-IWAG) incremental recovery factor for medium-to-light oil in undersaturated reservoirs, designing WAG pilots, and upscaling pilot results. This integrated research involved WAG literature review, WAG modeling, and selected machine learning techniques. The selected machine learning techniques are stepwise regression and group method of data handling. First, the important parameters for the prediction of the WAG incremental recovery factor were selected. This includes reservoir properties, rock and fluid properties, and WAG injection scheme. Second, an extensive WAG and waterflood modeling was carried out involving more than a thousand reservoir models. Third, WAG incremental recovery factor mathematical predictive models were developed and tested, using the group method of data handling and stepwise regression techniques. HC-IWAG incremental recovery factor mathematical models were developed with a coefficient of determination of about 0.75, using 13 predictors. The developed WAG predictive models are interpretable and user-friendly mathematical formulas. These developed models will help the subsurface teams in a variety of ways. They can be used to identify the best candidates for WAG injection, evaluate and optimize the WAG process, help design successful WAG pilots, and facilitate the upscaling of WAG pilot results to full-field scale. All this can be accomplished in a short time at a low cost and with reasonable accuracy.

Diagenetic Control of Reservoir Performance and Its Implications for Reservoir Prediction in Jinci Sandstone of Upper Carboniferous in the Middle East Ordos Basin

“…The tight sandstone reservoir, with extremely low porosity and permeability (porosity <10%, permeability less than 0.1mD), , shows strong heterogeneity in the anisotropy of lithology, pore structure, and physical properties due to the diagenetic modification, , among which, the micropore structure is of great significance for hydrocarbon filling, migration, and its exploitation; , however, the pore system of the tight sandstone reservoir is tortuous with a small pore radius and poor connectivity, which makes the micropore structure difficult to accurately characterize. , A large amount of research has been done on the pore type, pore-throat radius, pore structure of tight sandstone reservoirs, and interaction that the pore-throat structure posed on reservoir seepage capacity. ,,− However, the research on the pore-throat relationship and the diagenetic-controlled pore-throat evolution pattern of different pore-throat configuration types needs more attention.…”

“… 2 , 9 A large amount of research has been done on the pore type, pore-throat radius, pore structure of tight sandstone reservoirs, and interaction that the pore-throat structure posed on reservoir seepage capacity. 2 , 12 , 14 − 17 However, the research on the pore-throat relationship and the diagenetic-controlled pore-throat evolution pattern of different pore-throat configuration types needs more attention.…”