A statistical methodology for deriving reservoir properties from seismic data

Abstract

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.

Quantitative lithostratigraphic interpretation of seismic data for characterization of the Unayzah Formation in central Saudi Arabia

“…If such correlations exist, then a statistical relationship is built to convert the seismic information into the related properties at each trace location (Fournier and Derain, 1995). The correlation analysis is carried out in the vicinity of wells where both geologic and seismic information are available.…”

Artificial immune-based self-organizing maps for seismic-facies analysis

“…Seismic attribute volumes, together with appropriate classification schemes, help in the interpretation and identification of lateral changes in reservoir properties. These can further be calibrated with well-logs (Dumay and Fournier, 1988;Schultz et al, 1994;Fournier and Derain, 1995;Walls et al, 1999;Matos et al, 2007). Due to inconsistency and sparseness of seismic data, a priori knowledge of available clusters or classes is generally not available; therefore, unsupervised classification algorithms are attractive for these applications.…”

Unsupervised seismic facies analysis using wavelet transform and self-organizing maps

“…Given the appropriate combination of seismic attributes, one can identify lateral changes in the reservoir, which can then be calibrated with well information. The search for an appropriate representation of petroleum reservoirs, using seismic data and pattern recognition techniques, has been the subject of several scientific publications ͑Dumay and Fournier, 1988;Schultz et al, 1994;Fournier and Derain, 1995;Walls et al, 1999;Johann et al, 2001;Saggaf et al, 2003͒. When the geological information is incomplete or nonexistent, seismic facies analysis is called nonsupervised and is performed through unsupervised learning or clustering algorithms ͑Duda et al,…”