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 calculat...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(R^(2))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 R^(2)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 R^(2)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.展开更多
基金The authors would like to acknowledge the academic support received from College of Petroleum Engineering and Geosciences at King Fahd University of Petroleum and Mineral Resources(KFUPM),Saudi Arabia.
文摘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(R^(2))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 R^(2)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 R^(2)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.
