Pore pressure prediction of hydrocarbon reservoirs with empirical models and artificial neural network: case study in the Doba basin, Chad

Abstract

Abstract Accurate Pore pressure prediction is essential in drilling as it enables drilling hazards to be avoided. However, traditional methods of pore pressure prediction, especially empirical methods are field-specific. It is therefore important to evaluate the best empirical method which is to be applied in any basin or field according to its main characteristics. Besides, recently, artificial neural networks (ANNs) are the most used for pore pressure prediction because they can help to learn complex relationships between input and output parameters. They lead to accurate pore pressure prediction results in many cases. In light of this, the aim of this study carried out in the Doba basin is to analyse and compare the applicability of various sonic log-based empirical models of pore pressure prediction, including Eaton’s model, Raymer-Hunt’s model and Kozeny-Carman’s model and a ANN model of pore pressure prediction in order to find out the best predictive one to use in the studied area in order to achieve better prediction results. To achieve this objective, firstly, the mathematical models of pore pressure prediction from Eaton, Terzaghi and Raymer-Hunt equations and Kozeny-Carman’s model were used. The artificial neural network method uses 33 datasets of well log data including sonic transient time, vertical stress and porosity as input parameters to train the ANN model. The output parameters are the measured pore pressures produced by the Repeat Formation Tester. Using 70% of data set for training, 15% for test and 15% for validation, the trained model with the Bayesian Regularization Backpropagation function predicts pore pressure with an average correlation coefficient of 98%. The results show that pore pressure obtained from the Kozeny-Carman’s model, also has a 98% correlation coefficient, thus proving to be the most accurate of empirical methods. Indeed, pore pressures obtained from Raymer-Hunt’s model and Eaton’s model have 97% and 90% respectively as correlation coefficients. This study provides the best and most reliable empirical method of sonic log-based pore pressure prediction to use in the Doba basin, which can be used to achieve the best pore pressure prediction results and hence ensures drilling safety and wellbore stability in the studied area. The study also highlights a good application of the Bayesian function of the ANN for pore pressure prediction in the investigated area.

Keywords

• Pore pressure prediction
• Empirical methods
• Artificial neural network
• Sonic log
• RFT
• Doba basin