Should We Care About the Background Gas Effect on Reservoir Properties Prediction Using Machine Learning and Advanced Mud Gas Data?

Abstract

Background gas is the baseline gas measurement due to the recycled gas dissolved in or expelled from the drilling mud additives. It occurs more in oil-based mud systems than in water-based. A cut-off is usually applied on the mud gas data to remove the background gas effect in traditional mud gas analyses. This imposes an overhead on modeling procedures. This study investigates the effect of applying the cut-off on the performance of machine learning algorithms. A case of porosity prediction using advanced mud gas data is considered in this study. Using data from six wells, we implemented two experiments to compare the performance of artificial neural networks (ANN) with and without the cut-off. The first experiment applies a cut-off of 100 ppm on the total normalized gas while the second uses the entire data without the cut-off. The comparative results are benchmarked with those of a multivariate linear regression (MLR). Each well dataset was split into training and validation subsets using a randomized sampling approach in the ratio of 70:30. The results compare each of the MLR and ANN models individually and over all the datasets without and with the cut-off applied. The ANN models show better or same performance on the datasets without the cut-off in four out of six cases (67%). This shows that the ANN models may be less affected by the presence of the background gases in the mud gas datasets. It could be preliminarily concluded, based on the data used in this study, that it might be unnecessary to apply cut-offs on the mud gas data for ML algorithms due to their capability to handle noisy data. This conclusion is, however, subject to more extensive studies while ensuring consistency. Avoiding the application of the cut-off will remove the unnecessary overhead and provide more data for effective ML model training. While the results of this preliminary study somewhat agree with the traditional practice of applying a cut-off on advanced mud gas data, more extensive experiments will be conducted in our future work to further validate the conclusion. The background gas is traditionally considered noisy. In ML modeling, it could provide more information to further explain the nonlinear relationship between the input features and the target variable, hence improving the predictive capability.