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.

The Plato Depression in the Lower Magdalena Basin is a Miocene depocenter where a thick, shale-prone marine sequence known as the Porquero Formation was laid down in basin-floor conditions. Seismic inversion carried out on new and existing 2D seismic data helped to focus early exploration on a shallow stratigraphic gas-sand play associated with what seemed to be isolated shale diapirs with shallow roots. A subsequent land 3D survey helped to locate the first exploratory well, which resulted in the discovery of the Guama gas-condensate field. The main reservoir consists of laminar, low-permeability sands in a relatively thick shale-prone sequence of Early and Middle Miocene age. Sequential application of acoustic and elastic inversion and AVO analysis was used to build an evolving 3D predictive model of gas sands, extracted from an otherwise featureless seismic cube. Workflows were based on careful rock-physics analysis, simultaneous seismic inversion, and AVA analysis supported by custom well-log and seismic-gather conditioning. Work routines carried out in parallel became essential to applying quality control and fine-tuning the model, which supported three additional successful wells, early reservoir planning, and key volumetrics.

A complete well-seismic integration project was successfully performed in a gas field located in the Lower Magdalena Basin, Colombia. The reservoir level corresponds to a laterally limited, mixed clastic turbidte system (laminated, shaly sands) which makes prospect identification and delineation difficult. The study can be divided in three phases: well log analysis, seismic analysis and interpretation/integration. Well log analysis included petrophysical volumetric estimation and rock physics modeling. Seismic analyses included gather conditioning and pre-stack simultaneous inversion. The interpretation/integration phase pulled together the results from the phases described above. Based on the rock physics modelling and the rock physics template, there is a clear separation between gas shaly sands and other facies (wet shaly sands and shales) in the Poisson's ratio vs. P-impedance domain. During the interpretation phase, seismic inversion attributes guided by the rock physics information made it possible to separate the shaly gas sands in order to identify new prospective areas in the field. New successful wells were drilled, validating the workflow.

The multi-attribute rotation scheme (MARS) is a methodology that uses a numerical solution to estimate a transform to estimate 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 Euclidian space formed by n attributes, and subsequent scaling of this attribute to the target unit properties. This approach is performed using well logderived elastic attributes and petrophysical properties, and posteriorly applied over seismically-derived elastic attributes. In this study MARS was applied to predict a transform to estimate water saturation and total porosity from elastic attributes, using a two-and three-dimensional approach, respectively. The final goal of this workflow is to apply these transforms over seismically-derived attributes to generate volumes of these properties, which can be used in exploration and production settings for reservoir characterization and delineation, as well as soft variables in geostatistical workflows for static model generation and reserve estimation.

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