Application of artificial intelligence to forecast hydrocarbon production from shales
ارسال کننده : جناب آقای مهدی بوربورمرادی
سطح فعالیت : مدیر ارشد
ایمیل : mahdibourbourmoradi[@]gmail.com
تاریخ ارسال : ۹ دی ۱۳۹۶
دفعات بازدید : 33
زبان نوشتاری : انگلیسی
تعداد صفحه : 15
فرمت فایل : pdf
حجم فایل : 3.08 MB
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.
a b s t r a c t
Optimization routine: Particle Swarm Optimization
Surrogate models are particularly useful for quick predictions given a range of input parameters. These models are used to forecast oil production and perform sensitivity and uncertainty analyses. Polynomial equations and other non-linear equations known as response surface models (RSM) have been popularized for their simple mathematical structure and for easier implementation. Recently, artificial intelligence applications have gained the interest of engineers and scientists due to their unconventional ways of connecting input data to output. RSM coupled with a proper design of experiments  was proven to be an efficient and fast proxy model for forecasting production performance and analyzing uncertainties . Oil rate and water cut results were also predicted using RSM . Response surface models are widely applied to various aspects of reservoir engineering including estimating initial hydrocarbon uncertainty , production uncertainty [5e10], finding an optimal scheme for well placement [7,11e14], history matching [13,15,16], and determining the dew point of water in natural gas processing unit . Field cases have been studied using pattern recognition techniques  to determine pressure and production variation according to well locations.