Porosity Prediction With Uncertainty Quantification From Multiple Seismic Attributes Using Random Forest

Inferring porosity of subsurface from seismic data is of profound significance to many fields of Earth science and engineering applications, including but not limited to: hydrocarbon reservoir characterization, underground water flow modeling, geological CO2 storage, and geothermal energy exploitation. Traditional model-driven approaches confront the problems of strong nonlinearity and geological heterogeneity, while machine learning is good at nonlinear mapping, providing higher efficiency and accuracy as well. We propose a Random Forest (RF) based method using multiple seismic attributes to predict the underground porosity distribution with uncertainty quantification. The standard deviation of base models' predictions is used to quantify the regression uncertainty of RF. The uncertainty can robustly indicate the prediction quality in numerous experiments, where low uncertainty corresponds to relatively precise prediction and high uncertainty gives a possibility of larger errors. Furthermore, we utilize the quantified uncertainty to improve the RF regression accuracy by correcting the originally predicted porosity according to the statistical relationship between the absolute error and the standard deviation. The application of the proposed method on seismic data shows its potential to characterize spatially varying reservoir parameters, and the quantified uncertainty profile offers insights into risk evaluation for hydrocarbon exploration and development.

Automated summary

The study presents a Random Forest-based method for predicting underground porosity distribution using multiple seismic attributes, with improved accuracy through quantifying uncertainty. Experimental results indicate that low uncertainty corresponds to relatively accurate predictions, while high uncertainty may result in larger errors. This method shows potential for risk assessment in hydrocarbon exploration and development.