A Field-Scale Real-Time Prediction of Reservoir Porosity from Advanced Mud Gas Data

Abstract

In our previous study, we presented the preliminary results of the first attempt to predict reservoir rock porosity from advanced mud gas (AMG) data within the wellbore. The objective was to investigate the feasibility of generating a porosity log while drilling prior to wireline logging and core description processes. Knowing that porosity remains a critical property of petroleum reservoirs, this work improves on the previous research to predict porosity within a field. The methodology leveraged the machine learning (ML) paradigm in the absence of established physical relationship between AMG data, comprising light and heavy flare gas components, and reservoir rock porosity. More than 15,000 data points collected from representative wells in a field were used to prove the possibility of predicting the missing porosity in a well within the field. Optimized models of artificial neural network (ANN), decision trees (DT) and random forest (RF) were applied to the combined dataset. The dataset was randomly split into training and validation subsets in 70:30 ratio simulating the complete and missing sections respectively. Comparing the results of the ANN, DT, and RF models using statistical model performance evaluation metrics, the RF model consistently outperformed the others. In one of the test cases, the RF model gave a correlation coefficient (R-Squared) value of 0.84 compared to 0.46, and 0.78 for ANN and DT models respectively. The RF model also has a mean squared error (MSE) of 0.001 compared to 0.02 and 0.01 respectively for ANN and DT models. Having showed in a previous publication 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. It can be deduced 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 at the field scale and the huge benefit in utilizing AMG data beyond the traditional fluid typing and petrophysical correlation processes. The presented approach has the capability to complement 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.