Reservoir properties prediction integrating controlled-source electromagnetic, prestack seismic, and well-log data using a rock-physics framework: Case study in the Hoop Area, Barents Sea, Norway

Abstract

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.

Reservoir Prediction and Prospectivity of Omos Field Onshore Niger Delta Basin, Nigeria

“…Rock physics analysis was conducted both on wells and seismic to evaluate the inversion results for the reservoir of interest. Rock physics is the bridge that relates inversion derived elastic rock properties to litho-types and reservoir properties such as porosity, shale volume and water saturation (Chi and Han 2009;Kumar et al 2016;Alvarez et al 2017). Rock properties such as the Lame parameters; incompressibility (  ), rigidity (μ), and density (𝞺), can enhance the ability to point out reservoir zones (Sohail and Hawkes 2020).…”

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

“…Our approach uses the deep resistivity log reading as a starting point for resistivity model construction and calibration. Water saturation is obtained from the well log, while brine resistivity is set equal to 0.18 ohm-m, based on previous research [ 28 ]. It is, however, worth mentioning that CSEM and well-log resistivities look at two different scales of the same physical properties, especially when resistivity anisotropy is present [ 28 ].…”

“…The well enters the top of the reservoir which is the Stø formation at a depth of about 650 m. The water depth at the site is 400 m and the reservoir top is currently buried 250 m beneath the seafloor. Wisting is known for its discernible electric properties where the background resistivity is high and can reach 20 Ωm [ 28 , 29 ]. Due to the shallow burial depth, typical marine CSEM signals (0.1–16 Hz) have the desired penetration depths to the reservoir.…”

Maturing broadband 3D CSEM for improved reservoir property prediction in the Realgrunnen Group at Wisting, Barents Sea

“…Various schemes exist e.g. petrophysical joint inversion (PJI) of seismic and CSEM (Miotti et al, 2013), joint interpretation of CSEM and multivariate seismic attributes analysis (Alvarez et al, 2017), and the prospectivity evaluation approach (Baltar and Barker, 2015) where ATR and background are interpreted from inverted data and a Monte Carlo simulation defines probability distributions for hydrocarbon volume controlling parameters to provide volume range quantiles. The approach taken here is different and more labor and computationally intensive.…”