Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field

Expanding exploration activities into new fields has significantly boosted oil production. Well logging is a key method in petroleum exploration, used to evaluate hydrocarbon zones by analyzing parameters such as gamma ray, porosity, density, resistivity, and wave propagation velocity. These parameters are displayed as vertical log curves against well depth. However, logging tools sometimes fail to capture formation parameters accurately, creating gaps in well log data. Sonic log data are particularly prone to such gaps, as they are newer and less common in older wells. To address missing data, machine learning algorithms, like gradient boosting, provide an effective solution. Gradient boosting employs an ensemble of decision trees, iteratively correcting errors to model complex data patterns. This method is especially suitable for handling the intricate nature of well log data. In this study, Python was used to develop predictions for missing data, demonstrating the capability of machine learning to enhance data reliability and improve petroleum exploration processes. By bridging data gaps, machine learning ensures more accurate assessments of hydrocarbon zones, supporting better exploration outcomes.