2017_EJRNL_PP_PALASH_PANJA_1.pdf
Terbatas  Suharsiyah
» Gedung UPT Perpustakaan
Terbatas  Suharsiyah
» Gedung UPT Perpustakaan
Artificial intelligence (AI) methods and applications have recently gained a great deal of attention in
many areas, including fields of mathematics, neuroscience, economics, engineering, linguistics, gaming,
and many others. This is due to the surge of innovative and sophisticated AI techniques applications to
highly complex problems as well as the powerful new developments in high speed computing. Various
applications of AI in everyday life include machine learning, pattern recognition, robotics, data processing and analysis, etc. The oil and gas industry is not behind either, in fact, AI techniques have recently
been applied to estimate PVT properties, optimize production, predict recoverable hydrocarbons, optimize well placement using pattern recognition, optimize hydraulic fracture design, and to aid in reservoir
characterization efforts. In this study, three different AI models are trained and used to forecast hydrocarbon production from hydraulically fractured wells. Two vastly used artificial intelligence methods,
namely the Least Square Support Vector Machine (LSSVM) and the Artificial Neural Networks (ANN), are
compared to a traditional curve fitting method known as Response Surface Model (RSM) using second
order polynomial equations to determine production from shales. The objective of this work is to further
explore the potential of AI in the oil and gas industry. Eight parameters are considered as input factors to
build the model: reservoir permeability, initial dissolved gas-oil ratio, rock compressibility, gas relative
permeability, slope of gas oil ratio, initial reservoir pressure, flowing bottom hole pressure, and hydraulic
fracture spacing. The range of values used for these parameters resemble real field scenarios from prolific
shale plays such as the Eagle Ford, Bakken, and the Niobrara in the United States. Production data
consists of oil recovery factor and produced gas-oil ratio (GOR) generated from a generic hydraulically
fractured reservoir model using a commercial simulator. The Box-Behnken experiment design was used
to minimize the number of simulations for this study. Five time-based models (for production periods of
90 days, 1 year, 5 years, 10 years, and 15 years) and one rate-based model (when oil rate drops to 5 bbl/
day/fracture) were considered. Particle Swarm Optimization (PSO) routine is used in all three surrogate
models to obtain the associated model parameters. Models were trained using 80% of all data generated
through simulation while 20% was used for testing of the models. All models were evaluated by
measuring the goodness of fit through the coefficient of determination (R2
) and the Normalized Root
Mean Square Error (NRMSE). Results show that RSM and LSSVM have very accurate oil recovery forecasting capabilities while LSSVM shows the best performance for complex GOR behavior. Furthermore,
all surrogate models are shown to serve as reliable proxy reservoir models useful for fast fluid recovery
forecasts and sensitivity analyses.