Kenneth E. Green

Massively parallel electrical-conductivity imaging of hydrocarbons using the IBM Blue Gene/L supercomputer

Large-scale controlled source electromagnetic (CSEM) three-dimensional (3D) geophysical imaging is now receiving considerable attention for electrical conductivity mapping of potential offshore oil and gas reservoirs. To cope with the typically large computational requirements of the 3D CSEM imaging problem, our strategies exploit computational parallelism and optimized finite-difference meshing. We report on an imaging experiment, utilizing 32,768 tasks/processors on the IBM Watson Research Blue Gene/L (BG/L) supercomputer. Over a 24-hour period, we were able to image a large scale marine CSEM field data set that previously required over four months of computing time on distributed clusters utilizing 1024 tasks on an Infiniband fabric. The total initial data misfit could be decreased by 67 percent within 72 completed inversion iterations, indicating an electrically resistive region in the southern survey area below a depth of 1500 m below the seafloor. The major part of the residual misfit stems from transmitter parallel receiver components that have an offset from the transmitter sail line (broadside configuration). Modeling confirms that improved broadside data fits can be achieved by considering anisotropic electrical conductivities. While delivering a satisfactory gross scale image for the depths of interest, the experiment provides important evidence for the necessity of discriminating between horizontal and vertical conductivities for maximally consistent 3D CSEM inversions.

CSEM inversion: Impact of anisotropy, data coverage, and initial models

While 3-D inversion is a particularly effective tool for interpreting marine CSEM data, 3-D data acquisition is needed to image the resistivity structure of an anisotropic subsurface. Tests on synthetic models show resistivity artifacts when anisotropic data are forced through isotropic inversion and when offline data are excluded from anisotropic inversions. Similar artifacts appear also when the inversion starting model differs too much from the actual subsurface. As a result, inadequate attention to anisotropy or inadequate data coverage can lead directly to misinterpretation of the subsurface resistivity structure. Anisotropic imaging with good data coverage, accurate receiver orientation, and good initial resistivity models are necessary to quantitatively image resistive anomalies in an anisotropic earth.

Inversion study of a large marine CSEM survey

The Brazil RC Marine CSEM survey was collected in April of 2004 for the ExxonMobil Remote Reservoir Resistivity Mapping (RM) Project. The portion of the survey reported here consisted of a total of 735 km of transmitter towlines arranged approximately on a 5 km x 5 km rectangular grid (see Figure 1). Vertical and horizontal electric field measurements were recovered at a total of 23 seafloor locations from a deployment of 36 seafloor instruments. Imaging of these CSEM data into full three-dimensional conductivity volumes represents a formidable challenge due to the subtle effects of reservoir targets, the volume of data and its large dynamic range. In this presentation, we report on an initial round of inversion results obtained using both isotropic and anisotropic (VTI) imaging methods. Our results support the need for an anisotropic model to accurately represent subsurface resistivity.

Rapid Interpretation of CSEM Reconnaissance Data

Summary A new method called Quasi-3D Resistivity Mapping (Q3DRM) has been developed for rapid interpretation of controlled-source electromagnetic (CSEM) reconnaissance data. Q3DRM is based on scaled resistivity profiles generated from single or multi-frequency 1D inversion of amplitude and/or phase measurements from online, inline electric fields (Ex). Application of Q3DRM to model and actual grid-style reconnaissance surveys is shown. Results show that the method recovers the shallowest and largest reservoirs. Reservoir non-detections are due to combinations of reservoir size, depth, resistivity contrast, and survey density. 3D modeling also shows that incorporating phase measurements increases reservoir detectability and stability of the results. Although Q3DRM has limited sensitivity and accuracy due to assumptions and data limitations, it can be applied rapidly to CSEM surveys, even when very little is previously known about subsurface resistivities. This technique allows real-time detection of resistive anomalies during coarse-grid reconnaissance surveys for subsequent finer-grid surveying during the same vessel deployment. Q3DRM results also provide useful starting models for more detailed 3D interpretation and for full 3D imaging.