A First Attempt to Predict Reservoir Porosity from Advanced Mud Gas Data

Abstract

Abstract Porosity, a critical property of petroleum reservoirs, is a key controlling factor of the reservoir storage capacity. It has been conventionally measured from core plugs. Empirical correlations, statistical, and machine learning methods have been employed for indirect estimation of porosity. The results obtained from these approaches are available only after acquiring drilling and wireline logs. Obtaining porosity estimates in real time, ahead of wireline logging, can help in making critical decisions and enabling early assessment of reservoir quality. We present the results of a machine learning approach to predicting porosity from advanced mud gas data. Datasets integrating advanced mud gas data with porosity were gathered from seven wells to prove this concept. The mud gas data includes light and heavy flare gas components. Optimized artificial neural network (ANN) models were applied to the datasets and multivariate linear regression (MLR) models were used as benchmarks. Each well dataset was split into training and validation subsets using a randomized sampling approach with the ratio of 70:30. A 100 ppm cut-off was applied to the total normalized gas. To evaluate the performance of the models, we use correlation coefficients (R) and mean squared error (MSE). The ANN models consistently outperformed the MLR models in all the datasets. The ANN models have training and validation correlation coefficients of up to 0.89 and 0.88, respectively, compared to an average of 0.79 and 0.77 for the MLR models. The training and validation MSEs for the ANN models are as low as 0.0135 and 0.021, respectively, compared to those of the MLR models in the range of 0.0007 and 0.03, respectively. These results indicate the nonlinearity of the relationship between porosity and the gas components. Furthermore, it can be deduced that the approach is feasible and better results are achievable. The randomized sampling ensures that each data point has an equal chance to be used for either training or validation without bias. The cut-off applied to the normalized total gas is a standard practice to eliminate the background gas effect in the mud gas data. This study provides an opportunity to utilize mud gas data beyond the traditional fluid typing and petrophysical correlation purposes. The presented approach has the capability to complement existing reservoir characterization approaches in providing reservoir quality assessments at the early stage of exploration. We plan to apply state-of-the-art machine learning models and perform sensitivity analysis on the gas components in the future to increase the accuracy.