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 inherent nonuniqueness of geophysical analysis can mean that interpretations based only on a single geophysical measurement can be ambiguous or uncertain. We have developed a case study from the Hoop area of the Barents Sea, in which prestack seismic, well-log, and controlled-source electromagnetic (CSEM) data were integrated within a rock-physics framework to provide a more robust assessment of the prospectivity of the area than could be obtained by seismic analysis alone. In this example, although quantitative seismic interpretation identified potentially hydrocarbon-bearing sands, the saturation was uncertain. In this area and at shallow depths, the main focus is on (very) high oil saturations. Adding the CSEM data in this setting allows us to distinguish between high saturations ([Formula: see text]) and low and medium saturations ([Formula: see text]): It is clear that saturations similar to those observed at the nearby Wisting well ([Formula: see text]) are not present in this area. However, because of limitations on the sensitivity of the CSEM data in this high-resistivity environment, it is not possible to distinguish between low and medium saturations. This remains an uncertainty in the analysis. Based on the resulting downgrade of the main prospect Maya and the limited additional high-risk prospectivity at other stratigraphic levels, the partnership agreed to surrender the license.

We have developed an example from the Hoop Area of the Barents Sea showing a sequential quantitative integration approach to integrate seismic and controlled-source electromagnetic (CSEM) attributes using a rock-physics framework. The example illustrates a workflow to address the challenges of multiphysics and multiscale data integration for reservoir characterization purposes. A data set consisting of 2D GeoStreamer seismic and towed streamer electromagnetic data that were acquired concurrently in 2015 by PGS provide the surface geophysical measurements that we used. Two wells in the area — Wisting Central (7324/8-1) and Wisting Alternative (7324/7-1S) — provide calibration for the rock-physics modeling and the quantitative integrated analysis. In the first stage of the analysis, we invert prestack seismic and CSEM data separately for impedance and anisotropic resistivity, respectively. We then apply the multi-attribute rotation scheme (MARS) to estimate rock properties from seismic data. This analysis verified that the seismic data alone cannot distinguish between commercial and noncommercial hydrocarbon saturation. Therefore, in the final stage of the analysis, we invert the seismic and CSEM-derived properties within a rock-physics framework. The inclusion of the CSEM-derived resistivity information within the inversion approach allows for the separation of these two possible scenarios. Results reveal excellent correlation with known well outcomes. The integration of seismic, CSEM, and well data predicts very high hydrocarbon saturations at Wisting Central and no significant saturation at Wisting Alternative, consistent with the findings of each well. Two further wells were drilled in the area and used as blind tests in this case: The slightly lower saturation predicted at Hanssen (7324/7-2) is related to 3D effects in the CSEM data, but the positive outcome of the well is correctly predicted. At Bjaaland (7324/8-2), although the seismic indications are good, the integrated interpretation result predicts correctly that this well was unsuccessful.

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.