From Well to Field: Reservoir Rock Porosity Prediction from Advanced Mud Gas Data Using Machine Learning Methodology

Abstract

The utility of advanced mud gas (AMG) data has been limited to fluid typing and petrophysical correlations. There is the need to extend the utility to real-time reservoir characterization prior to wireline logging and geological core description. Our first attempt to predict reservoir rock porosity within a well yielded good result. This study improves on the previous effort by utilizing big data obtained from combining various wells in the study area. We used machine learning (ML) methodology in the absence of established physical relationship between AMG data, comprising light and heavy flare gas components, and reservoir rock porosity. More than 20,000 data points collected from representative wells were used to prove the concept of predicting the porosity in an interval or section of any well within the study area. Optimized models of artificial neural network (ANN), decision trees (DT) and random forest (RF) were applied to the combined dataset. The combined dataset was randomly split into training and validation subsets in 70:30 ratio. The 30% validation subset simulates a missing well interval or section. Comparing the results of the ANN, DT, and RF models using statistical model performance evaluation metrics, the RF model outperformed the others. The RF model gave a training and validation correlation coefficient (R-Squared) values of 0.94 and 0.83 respectively compared to 0.36 and 0.35 for the ANN and 0.84 and 0.73 for the DT models respectively. However, the p-value and mean errors show that the models are statistically acceptable. Having showed in a previous research that a multivariate linear regression model could not handle the complexity in the relationship between porosity and the flare gas components, these results have further confirmed the robustness of nonlinear solutions based on the ML methodology. We conclude that the ML approach to predicting reservoir rock porosity from advanced mud gas data is feasible and better results are achievable with more research. This study has confirmed the feasibility of predicting porosity based on a dataset of combined wells and the huge benefit in extending the utility of AMG data beyond the traditional workflows. This approach is capable of complementing existing reservoir characterization processes in assessing reservoir quality at the early stage of exploration. Future work will investigate the impact of integrating the AMG with surface drilling parameters to possibly increase the prediction accuracy.