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Hampson-Russell Software Services Ltd, 510, 715 – 5th Avenue SW, Calgary, Alberta, T2P 2X6ĬREWES Research Report - Volume 13 (2001)Īn alternate approach for the integration of well log and seismic data is to use geostatistical methods such as cokriging and kriging with external drift (Doyen, 1988, Todorov et al, 1997). Again, this requires a physical model that relates the seismic attributes to the well log data. One way to improve the result is to add information from other seismic attributes, such as frequency, coherency, AVO intercept and gradient, and so on. Thus, the final result is an approximation to the real goal, which is a spatially extended set of seismically derived well log curves that match the measured curves at the wells themselves. Key problems that need to be overcome in inversion are the band-limited nature of seismic data, the presence of noise, and amplitude scaling issues. There are numerous approaches to inversion, but most assume the convolutional model, extract an estimate of the earth’s reflectivity, and transform this reflectivity to impedance. Traditionally, this has been done using a technique called inversion, which has been well described in the literature (e.g.
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INTRODUCTION The classical problem in seismic exploration and production is how to integrate seismic data, which is closely sampled spatially but of relatively low resolution, with well log data, which is of high resolution but is poorly sampled spatially. Our results are very encouraging, and delineate the channel sand much more clearly than on the original seismic volume, as well as giving a better fit to the observed well logs. Finally, we improve the fit between the well log values and the porosity map using cokriging. We then apply the results of the training and cross-validation to data slices derived from both the seismic data cube and the inverted cube to produce an initial porosity map. The technique of cross-validation is then used to show which attributes are significant. We first extract average porosity values at the zone of interest, and then compare these values to average seismic attributes over the same zone. Our approach uses the well logs in the area to “train” the neural network. However, we found that the newly proposed method created an improved final result. We found an excellent correlation between porosity and the inverted acoustic impedance volume. The input consisted of twelve porosity logs as well as a 3-D seismic volume, and the inversion of this volume. The objectives of the survey were to delineate the channel and distinguish between sand-fill and shale-fill.
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Our case study involves the prediction of porosity in the Blackfoot field of central Alberta. Todorov1 ABSTRACT In this paper, we combine the methods of geostatistics and multiattribute prediction for the integration of seismic and well-log data, and illustrate this new procedure with a case study.
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Combining geostatistics, multiattribute transformsĬombining geostatistics and multiattribute transforms – A channel sand case study Brian H.