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Estimation of rock mechanic parameters is an important issue in reservoir management. Uniaxial compressive strength (UCS) and elastic modulus are the most important factors in determining the rock mechanic parameters in petroleum engineering studies. Accessibility to the parameters in fields such as designing fracture, analyzing of wellbore stability and drilling programming are very useful. The most accurate method to assign the aforementioned parameters is measuring these parameters in a laboratory. Laboratory determination of these parameters is problematic work due to technology issues, lack of laboratory equipment and coring problems in oil and gas wells, so indirect estimation of these parameters is required. Using well log data is the cheapest and most available approach in order to indirectly estimate these parameters. In this investigation, different models including multiple linear regression (MLR) and artificial neural network (ANN) (i.e., multi linear perceptron (MLP) and radial basis function (RBF)) were utilized for prediction of UCS via the three parameters of porosity, density and water saturation. These data were obtained from analysis of sonic, neutron, gamma ray and electric logs. The best results were obtained from a 3-15-1 MLP network which included one hidden layer and 15 neurons from the hidden layer using the trial and error method, and a 3-17-1 RBF which included 17 hidden neurons and a spread ó of 1.6. The core data from one of the carbonate Iranian oil fields (Asmari reservoir) were utilized for training, validation and testing of the networks, and correlation coefficients of 0.68, 0.90 and 0.83 were obtained for MLR, MLP and RBF, respectively. Keywords: Uniaxial compressive strength, Artificial neural network, Porosity, Linear regression, Water saturation |
