A Data Driven Machine Learning Approach to Predict the Nuclear Magnetic Resonance Porosity of the Carbonate Reservoir

Abstract

Abstract Carbonate rocks have a very complex pore system due to the presence of interparticle and intra-particle porosities. This makes the acquisition and analysis of the petrophysical data, and the characterization of carbonate rocks a big challenge. Neutron porosity log and sonic porosity logs are usually considered as less accurate compared to the NMR porosity. Neutron-density porosity depends on parameters related to rock matrix which cause the inaccurate estimation of the porosity in special cases suchlike dolomitized and fractured zone. Whereas NMR porosity is based on the amount of hydrogen nuclei in the pore spaces and is independent of the rock minerals and is related to the pore spaces only. In this study, different machine learning algorithms are used to predict the Nuclear Magnetic Resonance (NMR) porosity. Conventional well logs such as Gamma ray, neutron porosity, deep and shallow resistivity logs, sonic traveltime, and photoelectric logs were used as an input parameter while NMR porosity log was set as an output parameter. More than 3500 data points were collected from several wells drilled in a giant carbonate reservoir of the middle eastern oil reservoir. Extensive data exploratory techniques were used to perform the data quality checks and remove the outliers and extreme values. Machine learning techniques such as random forest, deep neural networks, functional networks, and adaptive decision trees were explored and trained. The tuning of hyper parameters was performed using grid search and evolutionary algorithms approach. To optimize further the results of machine learning models, k-fold cross validation criterion was used. The evaluation of machine learning models was assessed by average absolute percentage error (AAPE), root mean square error (RMSE), and coefficient of correlation (R). The results showed that deep neural network performed better than the other investigated machine learning techniques based on lowest errors and highest R. The results showed that the proposed model predicted the NMR porosity with an accuracy of 94% when related to the actual values. In this study in addition to the development of optimized DNN model, an explicit empirical correlation is also extracted from the optimized model. The validation of the proposed model was performed by testing the model on other wells, the data of other wells were not used in the training. This work clearly shows that computer-based machine learning techniques can determine NMR porosity with a high precision and the developed correlation works extremely well in prediction mode.