We present a structural smoothing regularization scheme in the context of inversion of marine controlled‐source electromagnetic data. The regularizing hypothesis is that the electrical parameters have a structure similar to that of the elastic parameters observed from seismic data. The regularization is split into three steps. First, we ensure that our inversion grid conforms with the geometry derived from seismic. Second, we use a seismic stratigraphic attribute to define a spatially varying regularization strength. Third, we use an indexing strategy on the inversion grid to define smoothing along the seismic geometry. Enforcing such regularization in the inversion will encourage an inversion result that is more intuitive for the interpreter to deal with. However, the interpreter should also be aware of the bias introduced by using seismic data for regularization. We illustrate the method using one synthetic example and one field data example. The results show how the regularization works and that it clearly enforces the structure derived from seismic data. From the field data example we find that the inversion result improves when the structural smoothing regularization is employed. Including the broadside data improves the inversion results even more, due to a better balancing between the sensitivities for the horizontal and vertical resistivities.

Electromagnetic signals are exponentially attenuated in conductive media. Thus, marine controlled-source electromagnetic (CSEM) data where the source and the receivers are located in the water column has exponentially low sensitivity towards the deep stratigraphy, compared to the shallow stratigraphy. In addition, CSEM inversions are also highly non-linear and ill-posed. It is therefore often difficult to achieve good inversion results for the deeper part of the subsurface using gradient based inversion methods. In this abstract, we describe a large-scale 3-dimensional anisotropic Gauss-Newton (3DGN) CSEM inversion implementation and discuss its advantages over gradient based algorithms. We also show, by synthetic and real data case studies, the large improvements in the 3DGN inversion results compared to those from the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm.

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.

Understanding heat flow and its lateral variability is important for petroleum prospecting. We present a methodology and workflow for estimation of radiogenic heat production in the crust and heat flow from geophysical data. The surface heat flow is determined by three main components: heat from Earth's interior, heat produced in the crust, and heat produced in the sediments. To estimate the crustal heat contribution, we have developed a statistical inversion approach. We first estimate density and susceptibility by inversion of crustal gravity and magnetic anomalies. Then we compute the radiogenic heat production in the crystalline crust by Bayesian rock-physics inversion of density and susceptibility. We demonstrate the proposed methodology with two case examples. In the first example, we compute radiogenic heat production from density and susceptibility measured on rock samples from onshore Norway. The estimated average value of the radiogenic heat production in the rock samples is about 2 µW/m³, with uncertainty of ± 0.5 µW/m³. The second example is from the greater Barents Sea, where we applied a full workflow including a large-scale lithospheric modelling to estimate the mantle heat flow, 3D gravity and magnetic inversion, and rock-physics inversion. The average heat production in the Barents Sea crust is about 1.5 µW/m³. The predicted heat production and heat flow are in good agreement with results presented by other authors.

A method has been developed to link present-day well or seismic measurements through rock-physics temperature and net-erosion variations caused by past tectonic events within a sedimentary basin. An ambiguous and intricate link among physical rock properties, temperature history, and burial history is demonstrated by modeling examples. Net-erosion estimates presuppose an estimate about temperature history and vice versa. Net erosion and temperature history normally cannot be estimated independently when using elastic rock properties, although net erosion in principle can be estimated independently of temperature for shallow unconsolidated sediments. For rocks that have been subjected to thermal diagenesis, either net erosion or paleotemperature must be known by consensus while estimating the other, or both must be estimated simultaneously. Well data demonstrate how rock physics is used to predict the maximum historical temperature and net erosion based on well-log velocities. From these parameters, sediment cooling can be derived. These estimates can be performed away from well positions, based on seismic velocities in shale. Temperature and erosion estimates are given for a 2D line, and large lateral variations are estimated. Normally, if well data are sparse, there will be a high uncertainty in net-erosion estimates. The new method reduces uncertainty in net-erosion and temperature estimates in areas away from well control. The method is used in an area with good estimations of uplift and erosion from wells and gives results consistent with observations.