%X This paper presents the application of Surrogate Reservoir Model (SRM) for predicting the Bottom-Hole Flowing Pressure (BHFP) on an initially undersaturated reservoir. SRM is recently introduce technology that is used to replicates the results of numerical simulation model. High computational cost and long processing time limits our ability to perform comprehensive sensitivity analysis and quantify uncertainties associated with reservoir because reservoir model that contains large number of grids in its geological structure takes considerable amount of time for a single simulation run. And also making hundred and thousands simulation runs is considered as a cumbersome process and sometimes impractical. SRM is considered as as a solution tool to tackle this issue. SRM uses Artificial Neural Network (ANN) technique for the reservoir simulation and modeling. In this paper, the results of SRM for predicting BHFP is presented and a reservoir simulation model has been presented using Black Oil Applied Simulation Tool (BOAST). To build any SRM, it requires small number of runs to train the model. Once we train the SRM, it can generate hundred and thousands of simulation runs in a matter of seconds. As a part of this system, it is proposed to develop a SRM extraction based on ANN to enhance the realization run time. © 2014 IEEE.
%A P.Q. Memon
%A S.-P. Yong
%A W. Pao
%A P.J. Seanl
%L scholars4236
%I Institute of Electrical and Electronics Engineers Inc.
%J 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2014 - Proceedings
%K Bottom hole pressure; Data mining; Forecasting; Reservoir management; Sensitivity analysis; Uncertainty analysis, Computational costs; Flowing pressures; General regression neural network; Geological structures; Processing time; Reservoir modeling; Reservoir simulation; Reservoir simulation model, Neural networks
%D 2014
%O cited By 2; Conference of 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:112912
%T Prediction of Bottom-Hole Flowing Pressure using general regression neural network
%R 10.1109/ICCOINS.2014.6868849