Calibration of Seismic Attributes for Reservoir Characterization ...

Conventional attributesTo better image the shallow channel and deeper producing horizons, conventional attributes werederived. Conventional attributes can be divided into point based and interval based attributes.Point based attributes include, but are not limited to, amplitude, instantaneous amplitude,instantaneous phase, and instantaneous frequency. These attributes are derived directly on ahorizon. Interval attributes involve using a time window based upon an interval of a horizon oran interval between a horizon. Calculations that involve averages over a given time window,such as RMS amplitude, are examples of interval based attributes.Facies classification techniqueThe term “seismic facies classification” can have many different meanings. For the purposes ofthis study, seismic facies classification is used to describe the process of grouping the traces of asurvey into a limited number of categories based on the shape of each trace over a fixed timewindow (Poupon et al, 1999). To perform a seismic facies classification, a horizon and a fixedtime window surrounding the horizon of interest should be chosen and the amplitudes extractedwith the associated times. For example, if a 12 ms interval is chosen, with a 2 ms sample rate,there will be 7 amplitude values at 0,2,4,6,8,10 and 12 ms in time within the window. Thesesamples are used to compute gradients by calculating the change of the value between samples(Figure 4). For this example, a list of 6 gradient values would be created. The series is thennormalized by dividing each value by the largest gradient value. The series can be thought of asa 6 dimensional vector with each dimension having a value ranging between minus one and one.These vectors are then obtained for every trace in the survey or area of interest. Next, a neuralnetwork process searches for clusters of these vectors, a process roughly equivalent to findingclusters of waveshapes. Each trace is then placed into a category or class, based on theseclusters. Each trace has been classified based on its normalized gradient vector, and it isprimarily the trace shape (changes in amplitude) that drives the classification rather than directamplitude values. The number of classifications will determine the resolution of the trace shapeanalysis. As more classes are used, smaller and smaller details will become visible in theanalysis, which are shown as color changes in a facies classification map, but the noise in theimage will also tend to increase. If more detail is needed, the survey can be subdivided intomultiple regions to improve the classification process. A smaller region will have fewer tracesand will need fewer classifications to provide the same amount of information.Figure 4: Calculating normalizedtrace gradients for the faciesclassification.The previous work performed byHardage et al (1994) limited theanalysis to point based amplitudeobservations. If a point basedamplitude analysis can giveinformation about a feature, thenperforming an analysis using theentire trace over a window of time can be expected to provide more details about that feature.Furthermore, if a single instance of time near a horizon can give useful information, using aAnnual Technical Progress Report Michigan Technological University DE-AC26-98BC15135October, 2001 Page 16