Multiattribute rotation scheme: A tool for reservoir property prediction from seismic inversion attributes

Abstract

The multiattribute rotation scheme (MARS) is a methodology that uses a numerical solution to estimate a transform to predict petrophysical properties from elastic attributes. This is achieved by estimating a new attribute in the direction of maximum change of a target property in an n-dimensional Euclidean space formed by an n number of attributes and subsequent scaling of this attribute to the target unit properties. We have computed the transform from well-log-derived elastic attributes and petrophysical properties, and we have posteriorly applied it to seismically derived elastic attributes. Such transforms can be used to estimate reservoir property volumes for reservoir characterization and delineation in exploration and production settings and to estimate secondary variables in geostatistical workflows for static model generation and reserve estimation. To illustrate the methodology, we applied MARS to estimate a transform to predict the water saturation and total porosity from elastic attributes in a well located in the Barents Sea as well as to estimate a water-saturation volume in a mud-rich turbidite gas reservoir located onshore Colombia.

Machine learning on Crays to optimize petrophysical workflows in oil and gas exploration

“…Using such relationships, geophysical attributes can be used to determine properties such as the porosity, total Clay volume or fluid saturation. Examples of manual use of well log data in this context are provided by [11] and [12].…”

Comparing approaches for the quantitative estimation of lithology and fluid properties from seismic inversion attributes: A case study from the North West Shelf of Western Australia

“…The multi-attribute rotation scheme (MARS) (Alvarez et al, 2015) was used to estimate a cube of the hydrocarbon pore volume of the rock (HPV) from seismic inversion attributes. This is a key petrophysical property that combines information on the hydrocarbon saturation (1-Sw), the porosity (, and the clay content of the rock (Vclay) (equation 2), which also has the advantage that when spatially integrated in a region D, provides information about the total volume of hydrocarbon (TVH) at reservoir conditions in that region (equation 3).…”

“…This workflow, described by Mukerji et al, (2001) and Avseth et al (2005), combines petrophysics and rock physics information with classification and estimation techniques to interpret seismic attributes. The second methodology that was applied was the multiattribute rotation scheme (MARS) (Alvarez et al, 2015). This method is a hybrid rock-physics/statistical approach that uses a global search algorithm to estimate a customized transform for each geologic setting to predict target petrophysical properties from elastic attributes.…”

Brittleness, porosity, and total organic carbon estimation from elastic attributes in Eagle Ford Shale through the application of the multiattribute rotation scheme

“…From a 3D pre-stack seismic inversion, it is possible to estimate volumes of P-wave impedances (Ip) and S-wave impedances (Is), which are linked to these key petrophysical properties, and will constitute the means to extrapolate the well information into a tridimensional context by transforming these volumes of elastic attributes into volumes of these key petrophysical properties. This paper presents a methodology to transform Ip and Is attributes, and their derivative attributes, into petrophysical properties through the application of the multi-attribute rotation scheme (MARS) (Alvarez et al, 2015). The final goal of this workflow is to apply the resultant transform over seismically derived elastic attributes to predict the spatial distribution of petrophysical reservoir properties and locate the sweet spots for optimal hydrocarbon production.…”