One of the most important goals of seismic stratigraphy is to recognize and analyze seismic facies with regard to the geologic environment. The first problem is to determine which seismic parameters are discriminant for characterizing the facies, then to take into account all those parameters simultaneously. The second problem is to be sure that there is a link between the seismic parameters and the geologic facies we are investigating. This paper presents a methodology for automatic facies recognition based upon two steps. The first, or learning step, begins with the definition of learning seismic traces for each facies we wish to recognize. The choice of learning traces is based upon either well data or a seismic stratigraphic interpretation. A large number of seismic parameters are then computed from the learning traces; multidimensional analyses are carried out in order to validate the choice of learning traces and to select, among all the available parameters, those that discriminate best. At this stage, a modeling step may be carried out to relate the seismic parameters to the geologic features. The second step is a predictive one which allows automatic facies recognition. We compute the previously chosen discriminant parameters on unknown seismic traces and classify the unknown traces with regard to the learning traces. We develop the methodology and successfully apply it to two examples of reservoir facies recognition. Our main conclusion is that seismic traces contain geologic information that can be extracted by multivariate data analyses of a large number of seismic parameters. Automatic facies recognition is reliable and fast; the derived facies map has the great advantage of combining simultaneously several discriminant parameters.
The use of seismic data to better constrain the reservoir model between wells has become an important goal for seismic interpretation. We propose a methodology for deriving soft geologic information from seismic data and discuss its application through a case study in offshore Congo. The methodology combines seismic facies analysis and statistical calibration techniques applied to seismic attributes characterizing the traces at the reservoir level. We built statistical relationships between seismic attributes and reservoir properties from a calibration population consisting of wells and their adjacent traces. The correlation studies are based on the canonical correlation analysis technique, while the statistical model comes from a multivariate regression between the canonical seismic variables and the reservoir properties, whenever they are predictable. In the case study, we predicted estimates and associated uncertainties on the lithofacies thicknesses cumulated over the reservoir interval from the seismic information. We carried out a seismic facies identification and compared the geological prediction results in the cases of a calibration on the whole data set and a calibration done independently on the traces (and wells) related to each seismic facies. The later approach produces a significant improvement in the geological estimation from the seismic information, mainly because the large scale geological variations (and associated seismic ones) over the field can be accounted for.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractIn the petroleum exploration/production context, characterizing the reservoir quality, identifying the main rock types, or predicting their spatial variations is a challenge for the industry. To achieve this purpose, supervised pattern recognition methods, as discriminant analysis are widely used. These methods aim at calibrating, when possible, a relationship between field features -for example a set of borehole measurements, or a set of seismic attributes-and a predefined set of classes -for example, different rock types-. It has yet the major drawback not to take into account the uncertainties on the measurement arrays, which may cause drastic misinterpretations of reservoir characteristics. The methodology developed is an extension of the standard parametric approach to discriminant analysis. The calibration phase follows the same main steps as the standard algorithm, except that all the processed quantities are intervals. The different interval computations are based on interval arithmetic. Eventually, any imprecise object is assigned to a subset of classes, consistent with the measurements and their uncertainties. As a result, the computed reservoir quality model is less precise, but more realistic, taking into account data and its uncertainties.
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