RF
R. Feng
Authored
20 records found
Reservoir lithology classification based on seismic inversion results by Hidden Markov Models
Applying prior geological information
Hidden Markov Models (HMMs) have been applied to predict reservoir lithologies using seismic inversion results as inputs. This approach takes into account the conditional probabilities between different lithologies, i.e. the vertical transitions in sedimentary sequences. These pr
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In this paper, geological prior information is incorporated in the classification of reservoir lithologies after the adoption of Markov random fields (MRFs). The prediction of hidden lithologies is based on measured observations, such as seismic inversion results, which are assoc
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For reservoir characterization, the subsurface heterogeneity needs to be qualified in which the distribution of lithologies is an essential part since it determines the location and migration paths of hydrocarbons. Preliminary analysis of well-log data could help to identify v
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Location limitation of logged wells restricts the porosity estimation across the whole reservoir target, whereas seismic data are always collected to cover larger areas. In this paper, inversion results of seismic data are proposed as inputs for the prediction of reservoir porosi
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The coupled Markov chain model can be used to simulate reservoir lithologies between wells, by conditioning them on the observed data in the cored wells. However, with this method, only the state at the same depth as the current cell is going to be used for conditioning, which ma
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In this study, geological prior information is incorporated in the classification of reservoir lithologies using the Markov Random Field (MRF) technique. The prediction of hidden lithologies in seismic data is based on measured
observations such as seismic inversion results, whic
...
A previous geological and petrophysical model of the fluvio-deltaic Book Cliffs outcrops contained eight lithotypes, within each of which a number of lithologies were grouped. While this model was an adequate representation of the overall depositional architecture, for reservoir-
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Hidden Markov Model has been applied to predict the reservoir lithologies by using seismic inversion results as inputs. This method can take the conditional probability between different states or lithologies into account which is the vertical correlation in geology. In order to
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Inversion results from seismic data of a synthetic example based on the Cretaceous fluvio-deltaic Book Cliffs outcrops in Utah (USA) have been used to extract the reservoir parameters. The input data sets are compressibility and shear compliance which are from the full elastic wa
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