Prediction Model Based on an Artificial Neural Network for Rock Porosity

The rock porosity is considered a key petrophysical property for the rock due to its great impact on the hydrocarbon reserve estimation and petroleum economics. The conventional methods for determining the rock porosity either from the logging tools, lab measurements for the cored samples, or using empirical correlations from other parameters are costly, time-consuming, or did not provide the required level of accuracy. The new horizon for implementing machine learning techniques as a new approach for predicting the rock porosity overcomes all of the above drawbacks. Therefore, the objective of this research is to develop a new model based on an artificial neural network (ANN) for predicting rock porosity from only drilling parameters that include weight on bit, torque, standpipe pressure, drill string rotation speed, rate of penetration, and pump rate. The study used two data sets for building the model (3767 data points) and the second one for validating the developed ANN model (1676 data points). ANN model was built and optimized with deep sensitivity analysis for the ANN model parameters to achieve strong prediction results. ANN model showed a correlation coefficient (R) between the predicted and actual porosity values of 0.97 and 0.92 with average absolute percentage errors (AAPE) of 6.2 and 9.3% for training and testing, respectively. The model validation enhanced the high prediction performance as ANN achieved R of 0.95 and AAPE of 8.5%. The study provides new contributions as predicting the rock porosity for complex lithology formations (sandstone, shale, and carbonate), developing an ANN porosity model with a high level of accuracy, and a newly developed ANN-based equation for estimating the porosity from only the surface drilling data.