Development of high speed computing leads to major advancements in every field of science and engineering. Artificial intelligence (AI) method is emerging as new modern technology applied to machine learning, pattern recognition, processing and understanding data, robotics etc. Its application in oil and gas industry is new despite of the fact that it has huge potential to explore the knowledge regarding reservoir characterization, PVT properties estimation, maximize productions, locating sweet spot using pattern recognition, optimum design of fracturing job, calculation of recoverable hydrocarbon, well placement etc. The main objective of this study is to put AI such as LSSVM in perspective from reservoir engineering and encourage engineers and researchers to consider it as a valuable alternative tool in the petroleum industry. Factors most affecting the production from fractured low permeability reservoirs such as reservoir permeability, gas relative permeability exponent, rock compressibility, initial gas oil ratio, slope of gas oil ratio in PVT, initial pressure, flowing bottom hole pressure and fracture spacing, are studied. A wide range of values of each parameter based on real field data from Eagle Ford, Bakken and Niobrara in the USA are assigned. Two different kinds of mathematical surrogate models, polynomial response surface method (RSM) and least square support vector machine (LSSVM) are compared to seek the better surrogate models in terms of predictability. Data are generated from a generic reservoir model using commercial simulator. Various models of recovery factors and gas oil ratio are developed for different times (after 90 days, 1 year, 5 years, 10 years, 15 years and 20 years) and for a minimum economic rate (5 STB/ day). Multivariate regression was used to obtain coefficients for the second-order polynomial response surface models using 80% of the simulated results (144). The LSSVM models coupled with radial basis kernel function (RBF) are trained with 60% data. 20% of data is used to tune the regularization parameter and kernel parameter using genetic algorithm (GA) optimization routine. Rest 20% data is utilized for testing the models' predictability for future performance. Goodness of fit is statistically measured by calculating coefficient of determination (R2), normalized root mean square error (NRMSE) and average absolute relative error (AARE). LSSVM exhibits good predictability to forecast the production such as oil recovery, gas recovery as surrogate models. The developed models can be used with high accuracy to forecast the production of oil from ultra-low permeability reservoirs. Quick sensitivity analysis of oil recovery to any parameter used in this study can be performed. The models are also useful for uncertainty analysis of productions.

You can access this article if you purchase or spend a download.