Imaging subsalt is still a challenging task in oil and gas exploration. We have used magnetotellurics (MT) to improve the integration of seismic and gravity data to image the Wedehof salt dome, located in the Northern German Basin. High-density natural field source broadband MT data were acquired and enhanced the definition of the top and overhanging salt structures in addition to imaging the salt dome root. Salt boundaries show strong resistivity contrasts with the surrounding sediments and thus represent a good target for electromagnetic measurements, especially for top salt and salt flanks imaging. With integrated 3D gravity modeling focusing on the salt dome’s flanks at intermediate depths, an improved model was achieved. The new model provided sound input to a follow-up seismic depth migration that led to an improved imaging of the subsalt target proven by subsequent exploration drilling. The integrated interpretation of MT, gravity, and seismic combines the strengths of the different physics, thus increasing imaging reliability and reducing exploration drilling risks. Using a conservative workflow that included a feasibility study with field noise evaluation and careful acquisition parameter testing prior to survey start, a broadband array data acquisition, and advanced processing, the survey area's severe cultural noise issues could be overcome.

Reservoir characterization objectives are to understand the reservoir rocks and fluids through accurate measurements to help asset teams develop optimal production decisions. Within this framework, we develop a new workflow to perform petrophysical joint inversion (PJI) of seismic and controlled-source electromagnetic (CSEM) data to resolve for reservoirs properties. Our workflow uses the complementary information contained in seismic, CSEM, and well-log data to improve the reservoir’s description drastically. The advent of CSEM, measuring resistivity, brought the possibility of integrating multiphysics data within the characterization workflow, and it has the potential to significantly enhance the accuracy at which reservoir properties and saturation, in particular, can be determined. We determine the power of PJI in the retrieval of reservoir parameters through a case study, based on a deepwater oil field offshore Brazil in the Sergipe-Alagoas Basin, to augment the certainty with which reservoir lithology and fluid properties are constrained.

Marine-controlled source electromagnetics (CSEM) have been extensively applied to various exploration scenarios worldwide. However, its perceived value and cost relative to seismic and the scarcity of realistic case studies have limited the industry’s interest in time-lapse reservoir-monitoring (4D) applications. A feasible way to make demand for CSEM for 4D-monitoring programs would be to increase the value of information and reduce survey costs by performing joint operations where seismic and CSEM data are acquired during the same survey and at equivalent spatial densities. To this end, we propose a new multiphysics ocean-bottom nodes (OBN) concept and show the industry that CSEM can be a cost efficient and effective integrators to 4D seismic projects. To this end, we conducted a feasibility study demonstrating that horizontal magnetic field components have the required sensitivities and can be used instead of horizontal electric field components in mapping the 3D resistivity distribution and 4D fluid change responses in a given reservoir. This makes engineering a new OBN class simpler and cheaper, as various miniaturized magnetic field sensors are available off-the-shelf or readily working along with packaging and coupling solutions.

A Bayesian approach is developed to estimate lithofluid facies and other rock properties conditioned on seismic and electromagnetic data for reservoir characterization. Prior distributions are assumed to be facies-related Gaussian modes of geophysical rock properties directly acquired or converted from petrophysical properties by calibrated rock-physics modeling. An original generalization includes two distributions in the same marginalization integral, analytically solved under a linearized Gaussian assumption to provide a facies model likelihood conditioned on geophysical data. Because computing this probability for all possible facies configurations may be impractical, a Markov chain Monte Carlo algorithm efficiently samples models to provide a full posterior distribution. The linearized Gaussian approach allows the computation of the conditional distributions of geophysical and petrophysical rock properties by applying local deterministic inversions over the many sampled facies models. The inversion uses simulated geophysical data from a 1D synthetic model based on the geologic scenario and a well from a selected marine oil field. Two other wells from the same reservoir are used to gather prior distributions. Data from the well, calibration of the rock-physics modeling, and facies matching between the priors and the synthetic model are presented and discussed. Numerical tests validate nonlinear forward-modeling adaptations based on the assumed linearized Gaussian approach. The simulated stand-alone and joint geophysical data sets are then inverted for lithofluid facies models under different prior inputs. Two challenging geoelectric scenarios also are tested, one with lower resistivity contrasts and another with a misguided background model. All results demonstrate a gain in precision and accuracy when associating these geophysical signals to estimate the oil column. Facies-conditioned inversions for the rock properties also indicate potential for quantitative reservoir interpretations.