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A Stochastic Approach to the Characterization of Lithofacies From Surface Seismic and Well Data
Author(s) -
Copty Nadim,
Rubin Yoram
Publication year - 1995
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/95wr00947
Subject(s) - seismic inversion , geology , covariance , hydrogeology , seismic to simulation , probability density function , covariance function , weighting , probability distribution , statistics , mathematics , geotechnical engineering , geometry , medicine , azimuth , radiology
Heterogeneity of natural geologic formations strongly influences groundwater flow and contaminant migration. This study presents a formal stochastic approach for the integration of surface seismic data and well data into the identification of the spatial arrangement (location, geometry, and interconnectedness) of lithofacies and incorporating this information in the estimation of the log permeability probability density function (pdf). To achieve this goal, normal move‐out (NMO) corrections are applied to the seismic reflection data to estimate interval seismic velocities. Calibration curves constructed from the well logs transform these velocity estimates into a lithology indicator prior probability field. From the well data and the indicator prior probability field, the indicator covariance function and its associated confidence limits are computed. Neighboring lithology logs and the indicator covariance function are then combined to update the indicator probability field. The conditional log permeability pdf is finally computed by weighting the log permeability distribution of each lithology with respect to its indicator probability field. The effectiveness of the proposed procedure is shown to depend on the quality of the seismic data and the degree of correlation between seismic and hydrogeologic soil properties. A synthetic case study is performed which shows that the surface seismic and well data complement each other with optimal results achieved when both types of data are incorporated into a joint identification procedure.