Multiphysics characterization of reservoir prospects in the Hoop area of the Barents Sea

Abstract

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.

A diverted submarine channel of Early Cretaceous age revealed by high-resolution seismic data, SW Barents Sea

“…Integration of CSEM and seismic data for prospect de-risking has proven a successful strategy in the Hoop area. Recent hydrocarbon discoveries Wisting (7324/8-1), Hanssen (7324/7-2), Mercury (7324/9-1) and Gemini North (7325/4-1) in good quality reservoir sands of the Jurassic-Late Triassic Realgrunnen Subgroup were systematically associated with coincident highly resistive anomalies and seismic DHIs (Alvarez et al, 2018;Baltar and Barker, 2017;Granli et al, 2017). The size of Gemini North discovery (7325/4-1) is inferior to 6 million barrels of oil equivalent and illustrates the high sensitivity of the CSEM method to subcommercial, shallow-buried hydrocarbon accumulations in the Hoop area.…”

“…2. The resistivity of the subsurface is retrieved from inversion of CSEM data, an exploration technique developed in the early 2000s (Constable and Weiss, 2006;Eidesmo et al, 2002) and intensively used for prospect de-risking in the Hoop area (Alvarez et al, 2018;Granli et al, 2017;Fanavoll et al, 2014). Resistivity is an anisotropic property of the subsurface and is mainly controlled, in sedimentary basins, by (1) porosity (2) brine conductivity (inverse of resistivity) (3) pore space connectivity and, (4) brine saturation (Senger et al, 2017).…”

“…Mercury (7325/4-1). In this area, integrated interpretation of seismic and Controlled-Source Electromagnetic (CSEM) data have proven to be an efficient de-risking tool (Alvarez et al, 2018;Granli et al, 2017;Fanavoll et al, 2014) because of the extremely high resistivity in the oil-and gas-saturated reservoir sands (Senger et al, 2017). Indeed, all the aforementioned discoveries were associated with coincident Direct Hydrocarbon Indicators (DHI), a high amplitude event in seismic data and a resistive anomaly from 3D inversion of CSEM data.…”

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].…”

Time-Lapse 3D CSEM for Reservoir Monitoring Based on Rock Physics Simulation of the Wisting Oil Field Offshore Norway

“…This indicates that the reservoir rock at Wisting has a negligible influence from clays. Moreover, Meunier [ 38 ] and Alvarez et al [ 39 ] characterize the two main formations of the reservoir—Stø and Nordmela—as consisting of well-sorted quartz-rich sandstone with negligible clay content, providing further evidence for the clay-free assumption we applied in Equation (2).…”