A comparison of machine-learning methods to predict porosity in carbonate reservoirs from seismic-derived elastic properties

Porosity prediction from seismic data in carbonate reservoirs is challenging, since the common presence of heterogeneities in carbonates makes it difficult to establish a clear physical relationship between reservoir properties and elastic responses. Regarding the strong nonlinearities underlying the relationship, machine learning is considered as a good alternative to traditional methods. We compare several representative supervised machine learning algorithms (Light Gradient Boosting Machine, eXtreme Gradient Boosting, Categorical Boosting, Random Forest, Multi-layer Perceptron, Convolutional Neural Network) in terms of predictive accuracy and runtime for cross-well validation and seismic prediction in a heterogeneous carbonate reservoir, offshore Brazil. The machine learning models are trained with the porosity and elastic parameters (P-impedance and Vp/Vs ratio) from the smoothed and standardized logging data. Then we apply the trained model on the inverted elastic properties to predict the porosity profile from seismic data. Based on the cross-well blind tests for the studied reservoirs, the three GBDT (Gradient Boosting Decision Tree) implementations obviously stand out among the compared methods, to get the highest prediction accuracy. The runtime of LightGBM (Light Gradient Boosting Machine) is much shorter than any other machine learning approaches, showing potential for fast porosity prediction from seismic data. Additionally, we analyze the geological factors (clay content, oil saturation, and relative depth) that possibly affect the predictive accuracy. It is found that, with the constraints from clay content and fluids saturation, the performance of porosity prediction from elastic parameters can be further improved to a certain degree.