Prediction of Compressional Slowness from Conventional Well Log Data using the Gradient Boosting Algorithm

Abstract

Compressional slowness (DTCO) is the most basic parameter in geophysics, petrophysics, and geomechanics. These parameters can be obtained through the sonic log tool. However, equipment constraints, relatively new technology, and high cost of measurement make the parameters generated by sonic logs unavailable in old wells or wells being developed. Therefore, it is essential to predict sonic logs, especially in the case of compressional slowness prediction. Using machine learning, predictions can be generated by studying data on existing log wells. One of the algorithms that can produce predictions on continuous data, such as log values, is gradient boosting. MAPE and RMSE were used as evaluation metrics. The inputs used are gamma ray log data (GR), density (RHOB), porosity (NPHI), and shear slowness (DTSM). MAPE results show an error value of 12.28% with an RMSE of 10.74, indicating that the predictive model obtained has good results and performance. Using hyperparameter tuning in machine learning can reduce the error rate by 2.29% with faster processing times. In addition, it was found that the quantity of training wells can affect the resulting error value. The existence of this research can help a petrophysicist, geologist, and geophysicist characterize a reservoir with limited data. The use of this method also has the potential to be an alternative solution when sonic log measurements are expensive.