Detecting Skrugard by CSEM — Prewell prediction and postwell evaluation

Abstract

The discovery of Skrugard in 2011 was a significant milestone for hydrocarbon exploration in the Barents Sea. The result was a positive confirmation of the play model, prospect evaluation, and the seismic hydrocarbon indicators in the area. In addition, the well result was encouraging for the CSEM interpretation and analysis that had been performed. Prior to drilling the 7220/8-1 well, EM resistivity images of the subsurface across the prospect had been obtained along with estimates of hydrocarbon saturation at the well position. The resistivity distribution was derived from extensive analysis of the multiclient CSEM data from 2008. The analysis was based on joint interpretation of seismic structures and optimal resistivity models from the CSEM data. The seismic structure was furthermore used to constrain the resistivity anomaly to the Skrugard reservoir. Scenario testing was then done to assess potential alternative models that could explain the CSEM data in addition to extract the most likely reservoir resistivity. Estimates of hydrocarbon saturation followed from using petrophysical parameters from nearby wells and knowledge of the area, combined with the most likely resistivity model from CSEM. Our results from the prewell study were compared to the postwell resistivity logs, for horizontal and vertical resistivity. We found a very good match between the estimated CSEM resistivities at the well location and the corresponding well resistivities. Thus, our results confirmed the ability of CSEM to predict hydrocarbon saturation. In addition, the work demonstrated limitations in the CSEM data analysis tools as well as sensitivity to acquisition parameters and measurement accuracy. The work has led to more CSEM data acquisition in the area and continued effort in development of our tools for data acquisition and analysis.

Ensemble scenario‐based inversion: A new approach for estimating the uncertainty of resistivity models derived from 3D controlled source electromagnetic data

“…In principle, the set of models can be much more diverse and complex than in this paper. On the other side, a more basic set of models can also be used to test simple hypotheses with smaller computer resources, providing an automatic method for scenario testing comprising continuous adjustment of both scenarios and background resistivities, in contrast to the fixed scenarios of Løseth et al (2014). Single SBIs, while not allowing us to analyse uncertainties, can also be used to quickly generate start models for 3D GN inversion.…”

“…The Hessian is needed to calculate these bounds, so that 3D applications are not feasible without simplification. In the absence of uncertainty evaluations, scenario testing (Berre et al., 2020; Fanavoll et al., 2014; Løseth et al., 2014) can be used as a pragmatic attempt to test and rank alternative interpretations of a single final model.…”

Inversion of inline and broadside marine controlled‐source electromagnetic data with constraints derived from seismic data

“…The survey layout is shown in Løseth et al (2014). This is to show that the resistive features of the subsurface will follow the geometry as given by the seismic compared with regular smoothing regularization, and how the result will be affected by inclusion of broadside data.…”

“…The chosen line is a line from a multi-client CSEM survey acquired by EMGS in 2008 in the Norwegian Barents sea, and it runs across two discoveries. The survey layout is shown in Løseth et al (2014).…”

Time-lapse CSEM: how important is survey repeatability?

“…CSEM surveys have often been applied for hydrocarbon exploration-to help making drilling decisions and finding new targets (see e.g. Alcocer et al 2013;Løseth et al 2014), as well as for imaging CO 2 plume at carbon storage sites (Park et al 2017). Recent years have seen a significant progress in various aspects of marine CSEM technology: launch of a new source with up to 10 000 A electrical current (Hanssen et al 2017), pushing acquisition frequencies up to 45 Hz to reach a record high spatial resolution (Granli et al 2018) and a common use of advanced Gauss-Newton optimization schemes for large-scale 3-D inversions (Nguyen et al 2016;Perez et al 2020).…”