%0 Journal Article
%@ 21900558
%A Belazreg, L.
%A Mahmood, S.M.
%A Aulia, A.
%D 2019
%F scholars:11076
%I Springer Verlag
%J Journal of Petroleum Exploration and Production Technology
%K Data handling; Data mining; Gases; Learning systems; Machine learning; Mean square error; Petroleum reservoir evaluation; Petroleum reservoirs; Recovery; Risk assessment; Water injection, Group method of data handling; Machine learning techniques; Recovery factors; Reservoir simulation; Reservoir simulation model; Root mean square errors; Water-alternating gas injections; Water-alternating-gas injection, Secondary recovery
%N 4
%P 2893-2910
%R 10.1007/s13202-019-0673-2
%T Novel approach for predicting water alternating gas injection recovery factor
%U https://khub.utp.edu.my/scholars/11076/
%V 9
%X Water alternating gas (WAG) injection process is a proven EOR technology that has been successfully deployed in many fields around the globe. The performance of WAG process is measured by its incremental recovery factor over secondary recovery. The application of this technology remains limited due to the complexity of the WAG injection process which requires time-consuming in-depth technical studies. This research was performed for a purpose of developing a predictive model for WAG incremental recovery factor based on integrated approach that involves reservoir simulation and data mining. A thousand reservoir simulation models were developed to evaluate WAG injection performance over waterflooding. Reservoir model parameters assessed in this research study were horizontal and vertical permeabilities, fluids properties, WAG injection scheme, fluids mobility, trapped gas saturation, reservoir pressure, residual oil saturation to gas, and injected gas volume. The outcome of the WAG simulation models was fed to the two selected data mining techniques, regression and group method of data handling (GMDH), to build WAG incremental recovery factor predictive model. Input data to the machine learning technique were split into two sets: 70 for training the model and 30 for model validation. Predictive models that calculate WAG incremental recovery factor as a function of the input parameters were developed. The predictive models correlation coefficient of 0.766 and 0.853 and root mean square error of 3.571 and 2.893 were achieved from regression and GMDH methods, respectively. GMDH technique demonstrated its strength and ability in selecting effective predictors, optimizing network structure, and achieving more accurate predictive model. The achieved WAG incremental recovery factor predictive models are expected to help reservoir engineers perform quick evaluation of WAG performance and assess a WAG project risk prior launching detailed time-consuming and costly technical studies. © 2019, The Author(s).
%Z cited By 14