Innovative Multidisciplinary Journal of Applied Technology Volume 3 Issue 12 (2025) · pp. 56-63
Analysis of Lightgbm Model Accuracy and Validation Strategies for Predicting Oil and Gas Probability Maps
Shukurova, Markhabo
Abstract
The accuracy of the LightGBM model in forecasting oil and gas probability maps had been evaluated based on MapOil analytic system. Integration of geological, geophysical, satellite and topographic map datasets in single spatial processing pipeline through MapOil streamline feature extraction, and probability assessment for each grid cell over exploration areas. The leaf-wise growth and advanced gradient boosting optimization criteria of LightGBM are capable of capturing the complex nonlinear relationships associated with subsurface properties. Differences among the various validation strategies are also examined to compare the robustness of predictions. Standard random cross-validation along with spatially informed cross-validation approaches, such as block and region-based validation, were utilized. Validation in space results in reduced information leakage and more realistic performance statistics, leading to a more informative generalisation of the model on untested areas. In summary, the application of LightGBM in combination with MapOil platform forms a powerful methodology for creating high-precision oil and gas probability maps which are able to improve the data driven decision making for hydrocarbon exploration.
LightGBMoil and gas probability mappingspatial validationhydrocarbon explorationmapoil systemgradient boostinggeological data analysismachine learning modelsspatial predictiongeoscience informatics
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