Effects of tuning decision trees in random forest regression on predicting porosity of a hydrocarbon reservoir. A case study: volve oil field, north sea
Kushan Sandunil, Ziad Bennour, Hisham Ben Mahmud, Ausama Giwelli
Abstract
This study investigates the effects of tuning n_estimators along with max_features and min_samples_leaf in random forest regression when predicting the porosity of the Volve oil field.
Topics & Concepts
PorosityOil fieldRandom forestField (mathematics)Decision treeGeologyRegressionPetroleum engineeringHydrocarbonOil spillRegression analysisLinear regressionNorth seaStatisticsGeotechnical engineeringMathematicsOceanographyMachine learningComputer sciencePure mathematicsChemistryOrganic chemistryHydrocarbon exploration and reservoir analysisHydraulic Fracturing and Reservoir AnalysisReservoir Engineering and Simulation Methods