%0 Journal Article
%@ 21900558
%A Belazreg, L.
%A Mahmood, S.M.
%D 2020
%F scholars:13538
%I Springer
%J Journal of Petroleum Exploration and Production Technology
%K Data handling; Data mining; Economic analysis; Forecasting; Gas permeability; Gases; Hydrocarbons; Petroleum reservoir engineering; Recovery; Regression analysis, Coefficient of determination; Group method of data handling; Lessons learned; Numerical reservoir simulations; Original oil in places; Recovery factors; Water-alternating gas injections; Water-alternating-gas injection, Enhanced recovery
%N 2
%P 249-269
%R 10.1007/s13202-019-0694-x
%T Water alternating gas incremental recovery factor prediction and WAG pilot lessons learned
%U https://khub.utp.edu.my/scholars/13538/
%V 10
%X Water alternating gas (WAG) injection process is a proven enhanced oil recovery (EOR) technology with many successful field applications around the world. WAG pilot projects demonstrate that WAG incremental recovery factor typically ranges from 5 to 10 of original oil in place, though up to 20 has been observed in some fields. Despite its proven success, WAG application growth has been very slow. One of the reasons is the unavailability of robust analytical predictive tools that could estimate WAG incremental recovery factor, which is required for preliminary economic analysis before committing to expensive and time-consuming detailed technical studies and field pilot test that often requires a lot of input data. A semi-numerical model for WAG incremental recovery factor prediction was developed based on data mining of published WAG pilots to fill this gap. An extensive review of published WAG pilot projects was carried out, and consequently, 33 projects from 28 fields around the world were selected for this research study. Field WAG incremental recovery factor and parameters with total of one hundred and seventy-seven (177) observations were inputted to the predictive model. A predictive model was developed using both regression and group method of data handling (GMDH) techniques; 70 of the 33 WAG pilot projects data were used as validation, whereas remaining 30 of data set were used for validation. The predictive model results achieved with coefficient of determination (R2) from the regression method were ranging from 0.892 to 0.946 and 0.854 to 0.917 for training and validation sets, respectively. However, the prediction model coefficient of determination (R2) using GMDH method was ranging from 0.964 to 0.981 and 0.934 to 0.974 for training and validation, respectively. The developed predictive model can predict WAG incremental recovery factor versus multiple input parameters that include rock type, WAG process type, hydrocarbon pore volume of injected gas, reservoir permeability, oil gravity, oil viscosity, reservoir pressure, and reservoir temperature. The results of the study demonstrated that few input parameters have a significant impact on WAG incremental recovery factor as reservoir permeability and hydrocarbon pore volume of injected gas. This research study uses a novel approach in pre-defining the expected incremental WAG recovery factor before committing resources for building complex numerical reservoir simulation models and running WAG pilot tests, which are very time-consuming and costly and require extensive data input. © 2019, The Author(s).
%Z cited By 11