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Expert, independent advice on the most appropriate satellite imagery (optical, radar) and elevation data products.
The benchmark for petrophysics, rock physics, facies analysis and statistical mineralogy. Collaborative multi-well log analysis made easy for better drilling decisions.
The goal of seismic attribute analysis, and in particular multi-attribute analysis, is to improve the spatial prediction of petrophysical and geomechanical rock properties throughout the reservoir. Key to the analysis is discovering the linear and often non-linear relationships between the seismic trace and production indicators, such as lithology, fluids, geomechanical properties and in-situ stresses. The CGG GeoConsulting teams have extensive global experience discovering these complex relationships.
Reservoir focused seismic and well log data conditioning is a critical prerequisite to ensure that noise and other data irregularities are not propagated throughout the attribute volumes. This is especially important when utilizing pre-stack azimuthal seismic data for analysis of anisotropic fractured rocks.
Seismic attribute analysis starts with petrophysical rock property analysis from well logs and core or cuttings. Forward modeling is then employed to identify key seismic attributes that distinguish the primary controls on production.
Individual seismic attributes give us indications of many subtle properties, however the real enhancement in predictive accuracy and reliability of results comes from combining multiple seismic attributes and solving the non-linear relationship between the seismic attributes and reservoir properties. Multi-linear regression and neural network techniques are employed to determine which seismic attributes should be combined to predict a particular reservoir property. Care is taken to weight the contribution from each attribute separately, and ensure that only independent information not found in the other attributes is added with each subsequent attribute. Careful cross-validation blind well testing is performed to determine the optimum number of attributes to use, eliminating the risk of “over training” the prediction. Ultimately, 3D volumes of rock properties or other production indicators are generated.
A final calibration step compares the predicted data to measured rock properties from core or cutting mineralogical analysis.
Seismic traces are composite responses to geological factors such as lithology, fluid saturations and geomechanical properties. Unraveling the influence each rock property has on the seismic trace and identifying a seismic attribute or combination of attributes which is capable of predicting that property is a mainstay of the CGG GeoConsulting reservoir characterization workflow. The improved geologic interpretation and prediction of production indicators assist in the identification of preferential drilling locations and completion practices.
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