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Research On Seismic Methods Of Reservoir Prediction & Evaluation In Wu-Xia Fault Zone

Posted on:2008-04-20Degree:MasterType:Thesis
Country:ChinaCandidate:K ZhangFull Text:PDF
GTID:2120360218963424Subject:Earth Exploration and Information Technology
Abstract/Summary:PDF Full Text Request
The seismic reservoir prediction is an important research area inseismic exploration at present, the method research of the oil gas reservoirprediction has the extremely vital practical purpose. The paper'sbackground is some scientific research items of oil field, it's object is toreappear the distribution of oil gas reservoir and parameter correctly and toimprove the rating of exploration & development, which combine theadvanced computer technology and the modern theory of the seismicreservoirpredictionwiththeoilproduction.Basedonthefinedescriptionofreservoir geological feature, we finish some work, such as seismicmulti-attributeanalysisandpredictionofseismicreservoirparametersetc.Inviewofcrucial questions intherecognitionandthepredictionofthereservoir, we propose some new ideas and methods in an application angleand obtain a good geological application effect. At last, we form a suit ofeffective way for complex reservoir's recognition and prediction bycombininggeologywithexploration. Thepaperdoessomeresearchworkasthefollowingfouraspects.The first, after analyzing the seismic data quality of the assembly3-D,we use 3-D seismic volume visualization method and coherent bodytechnology,finishthefineinterpretationofthestructure.The second, through the geologic analysis and the forward simulationstudy, the paper discusses the reliability of the seismic attribute analysistechnique.The third, after analyzing and studying systematically artificial neuralnetwork theory, the structure and compute formula of BP and RBF arestudied. Considering the complex relation between reservoir parameter andseismic attribute parameters, which is difficult to be expressed in anaccurate formula,the neural network is an advantageous tool tosolveaboveproblem, using the data-driven methodology, BP neural network and RBFneuralnetworkpredictedthereservoirporosity.The fourth, using the comprehensive evaluation technology, basing ongeneralized analysis for the geology rule and the structural features of theresearch area, combining the geophysics characteristic with the predictionresults, we design a proposal well,which builds the foundation of thethoroughresearchatthisareainthenextstage.
Keywords/Search Tags:Reservoir Prediction, Seismic Multi-attribute Analysis, Data-Drivenmethodology, RadialBasisFunctionNeuralNetwork
PDF Full Text Request
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