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基于灰色神经网络的油料储备预测模型研究.docx

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基于灰色神经网络的油料储备预测模型研究 Introduction Thepredictionofoilreserveisanimportantaspectofenergymanagement.Basedonaccurateprediction,governmentandorganizationscanplanandexecutetheirpoliciesandstrategiesrelatedtooilmanagement,production,andexploration.Thepredictionofoilreservecanbechallengingduetothehighlevelofuncertaintyassociatedwithoil-welldrillingandproduction.Inthispaper,weproposeanovelapproachtopredicttheoilreservebasedontheGrayNeuralNetwork(GNN). Background TheGNNisapowerfultoolforpredictionbasedonanalysisofdatasetsthatareincompleteorhaveincompleteinformation.TheGNNisacombinationoftheGrayModel(GM)andtheArtificialNeuralNetwork(ANN).TheGMapproachisusedtopreprocessdatasetsthatareincomplete,whereastheANNisusedtomodeltherelationshipsbetweentheinputandoutputvariables.Bycombiningthesetwoapproaches,theGNNisabletopredictaccurately,evenincaseswheretraditionalmethodsmayfail. OilReservePredictionwithGNN ThepredictionmodelforoilreserveusingGNNinvolvesthefollowingsteps: Step1:Datacollection.Thefirststepistocollectdatarelatedtooilproduction,oil-welldrilling,andexploration.Thedatacollectedincludesinformationonoilwelldepth,location,geologicalstructure,drillingtechnology,andwatercut. Step2:DataprocessingwithGM.ThesecondstepistopreprocessthedatausingGM.TheGMapproachisusedtoidentifyandremoveanyirrelevantorredundantdata.TheGMmodelisusedtoestimatethetrendofthecollecteddataandtheoveralltrendofoilreserveestimation. Step3:DatamodelingwithANN.Thethirdstepistotrainaneuralnetworkusingtheprocesseddata.Theneuralnetworkistrainedusingbackpropagationtomodeltherelationshipsbetweentheinputandoutputvariables.Theinputvariablesincludedepth,geologicalstructure,drillingtechnology,andwatercut,andtheoutputvariableistheoilreserve. Step4:Testingandvalidation.Finally,themodelistestedandvalidatedusingdatasetsthatwerenotusedfortraining.ThemodelisevaluatedusingcommonstatisticalmeasuressuchasMeanSquareError(MSE),RootMeanSquareError(RMSE),andMeanAbsoluteError(MAE). Conclusion Inthispaper,weproposedanovelapproachforpredi