Global Net-to-Gross Uncertainty Assessment at Early Appraisal Stage: Application To Stanford VI Reservoir

@inproceedings{MaharajaRM2005,
 abstract = { Scarce and preferentially located well data at early appraisal stage of a reservoir tend to give an upwardly biased estimate of Net-to-Gross (NTG). Seismic data provide the most extensive coverage of the reservoir which helps to correct the biased estimate, but they need to be calibrated with well data. A Bayesian approach is used to calibrate acoustic impedance with core facies data in the synthetic Stanford VI reservoir. Such a calibration calls for a prior probability of NTG, which itself is highly uncertain and therefore treated as a random variable. Multiple-point sequential simulation honoring all available data is used to obtain multiple equi-probable realizations of the reservoir. Spatial bootstrap done on the realizations is used to obtain uncertainty about the NTG estimate. },
 author = { Maharaja, Amisha },
 booktitle = { 25th gOcad Meeting },
 month = { "june" },
 publisher = { ASGA },
 title = { Global Net-to-Gross Uncertainty Assessment at Early Appraisal Stage: Application To Stanford VI Reservoir },
 year = { 2005 }
}