Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Weiqing Chen and Abdulazeez Abdulraheem
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Ahmed Abdulhamid Mahmoud: College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Salaheldin Elkatatny: College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Weiqing Chen: College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Abdulazeez Abdulraheem: College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

Energies, 2019, vol. 12, issue 19, 1-13

Abstract: Hydrocarbon reserve evaluation is the major concern for all oil and gas operating companies. Nowadays, the estimation of oil recovery factor (RF) could be achieved through several techniques. The accuracy of these techniques depends on data availability, which is strongly dependent on the reservoir age. In this study, 10 parameters accessible in the early reservoir life are considered for RF estimation using four artificial intelligence (AI) techniques. These parameters are the net pay (effective reservoir thickness), stock-tank oil initially in place, original reservoir pressure, asset area (reservoir area), porosity, Lorenz coefficient, effective permeability, API gravity, oil viscosity, and initial water saturation. The AI techniques used are the artificial neural networks (ANNs), radial basis neuron networks, adaptive neuro-fuzzy inference system with subtractive clustering, and support vector machines. AI models were trained using data collected from 130 water drive sandstone reservoirs; then, an empirical correlation for RF estimation was developed based on the trained ANN model’s weights and biases. Data collected from another 38 reservoirs were used to test the predictability of the suggested AI models and the ANNs-based correlation; then, performance of the ANNs-based correlation was compared with three of the currently available empirical equations for RF estimation. The developed ANNs-based equation outperformed the available equations in terms of all the measures of error evaluation considered in this study, and also has the highest coefficient of determination of 0.94 compared to only 0.55 obtained from Gulstad correlation, which is one of the most accurate correlations currently available.

Keywords: hydrocarbon reserve estimation; oil recovery factor; water drive sandy reservoirs; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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