Application of artificial neural network and least squares regression technique in developing novel models for predicting rock parameters

This study was carried out within the offshore Niger Delta Basin to generate novel predictive models for estimating rock parameters. MATLAB was employed in obtaining models for four different rock parameter relationships including unconfined compressive strength (UCS) against bulk density, UCS against sonic transit time (STT), shear wave velocity against STT, and permeability against bulk density using multiple ordinary least-squares regression (OLSR) methods. Also, the Adaptive-Neuro Fuzzy Inference System (ANFIS) artificial intelligence network was utilized for modeling and optimization of the data. Statistical tools including the Sum of Squares Total (SST), the Sum of Squares Error (SSE), the Sum of Squares Regression (SSR), and Correlation Coefficient (R-squared) were applied in investigating the prediction performances of the models. Results of OLSR analysis show that only the UCS against bulk density model gave high prediction performance in all the OLSR models with R-squared values of 0.8637, 0.8848, 0.8216, 0.9956, and 0.8108 for linear, quadratic, power, logarithmic, and exponential models respectively. ANN model results revealed that UCS against bulk density, UCS against STT, and shear wave velocity against STT models all gave high prediction performances with respective R-squared values of 0.89635, 0.99365, and 0.52703, while the permeability against bulk density model gave low performance (0.03378). These findings imply that all the OLSR models can be applied for the prediction of rock UCS from bulk density information only, while ANN-generated models can be used in predicting UCS from bulk density and STT, in addition to shear wave velocity from STT in the study area and similar geologic environments.