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.
Highlights
The petroleum industry is characterized by the need to make critical investment decisions under several uncertainties
Oil recovery factor (RF) is the most significant parameter for all exploration and development (E&P) companies mainly during the early reservoir life, because several investment decisions are based on the amount of hydrocarbon, which could be obtained from the target asset with the available techniques and operational practices [1]
The trained model was used to develop the empirical correlation in Equation (4), which predicted the oil RF for the testing data with R2 and average percentage error (AAPE) values of
Summary
The petroleum industry is characterized by the need to make critical investment decisions under several uncertainties. Oil recovery factor (RF) is the most significant parameter for all exploration and development (E&P) companies mainly during the early reservoir life, because several investment decisions are based on the amount of hydrocarbon, which could be obtained from the target asset with the available techniques and operational practices [1]. In 1945, The American Petroleum Institute (API) initiated a data collection process aiming to correlate the recovery factor with reservoir rock parameters and the properties of the produced fluid. An investigation was conducted by a special study committee on well spacing They examined data from 103 oil reservoirs, 25% of which are depletion-drive reservoirs, and the remaining are water-drive reservoirs from sandstone, limestone, and dolomite formations.
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