A data-driven approach to predict compressional and shear wave velocities in reservoir rocks

Abstract

Compressional and shear wave velocities (V_p and V_s respectively) are essential reservoir parameters that can be used to delineate lithology, calculate porosity, identify reservoir fluids, evaluate fracture and calculate mechanical properties of rocks. In this study, the potential application of intelligent systems in predicting V_p and V_s of reservoir rocks is presented. To date, considerable efforts are being carried out to obtain the best set of parameters capable of predicting V_p and V_s with a high degree of accuracy. Three intelligent models namely artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and least square support vector machine (LSSVM) were used in this study. The different models were based on the available information sourced from wireline log data. Parametric studies showed that measured depth, neutron porosity, gamma-ray, and density log data are vital in predicting both V_p and V_s. In developing the models, a comprehensive dataset available from one of the oil fields in the Norwegian North Basin was used. In evaluating the different models, two different statistical parameters namely Pearson’s correlation coefficient (R²) and root mean square error (RMSE) were considered. It was found that the LSSVM model is the most accurate technique for predicting both V_p and V_s. LSSVM model predicted the V_p with R² and RSME of 0.9706 and 0.0893 respectively. In addition, the model showed an excellent accuracy level in the prediction of V_s with R² and RMSE of 0.9991 and 0.0457 respectively. The proposed approach, if implemented, is crucial for geoscientists, reservoir and drilling engineers working on reservoir characterization and drilling operations.

Keywords

Artificial intelligent / Shear wave / ANFIS / Reservoir rocks

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