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Geophysical imaging in the foothills environment is typically hampered by complex structure, and the high cost of data acquisition in poorly accessible, rugged topography. Seismic imaging is particularly difficult due to poor signal penetration, steeply dipping structures and irregular data coverage. The use of magnetotellurics (MT) has become a successful complementary tool, due to good sensitivity to the deep resistive targets typically encountered below folded sequences of more conductive units. Due to non-uniqueness and resolution limitations, MT 3D inversion requires additional constraints in order to recover a reliable image. These usually come from geological interpretation of available seismic and well data, however it is often the case that several competing structural models can be derived. In this abstract we propose a workflow that employs magnetotelluric inversion to assess the validity of such structural models, by examining whether they are compatible with the electromagnetic observations. We further apply 3D non-linear uncertainty estimation to address the reliability of the inversion results, obtaining a bounding envelope of the resistive anomaly.

Direct wave arrivals are the most robust signals to determine velocity and consequently they have been used for almost a century in hydrocarbon exploration. The reason is simple as the arrival time is explicitly available. In order to acquire these direct arrivals in a seismic experimental setting it is necessary that these waves turns back to the surface after having been sent into the Earth. As is well known it is possible to turn waves back up if they encounter faster propagation velocities than have been previously experienced. Using these simple concepts we show how it is possible to design a seismic acquisition to measure subsalt velocities when the salt cover is very thick and potentially not homogeneous. Until now (in marine seismic surveying) the physical limitations of the Earth have meant that use of direct wave arrivals have been restricted to relatively shallow depths of investigation, linked to streamer length. In this paper we describe how a new and novel application of node technology has been combined with a well established physical phenomena to support the acquisition of a world first exploration-scale Ocean Bottom Node (OBN) survey.

A major challenge in the US Gulf of Mexico is transforming disparate public datasets into a quality-controlled database. CGG has successfully created and used the database for its Storage Play Quality Index (SPQI) carbon storage screening methodology and applied its latest imaging technologies to rejuvenate the legacy seismic data.

Exploring the Middle East’s energy landscape in an exclusive chat with CGG’s Ramez Refaat

We propose a more accurate alternative to Gassmann's equation for the modeling of the undrained bulk compressibility of a reservoir rock with moderate to high crack density. The new model combines the Vernik-Kachanov model and Brown-Korringa's equation. A practical quantification of the applicability of Gassmann's equation dependent on the rock stress dependency is also suggested.

In this paper, we show, first, how a high-resolution 65 Hz Vp model, obtained using Time-Lag FWI, can improve the imaging from the shallow to the deep. Similar improvements are then shown for PS data using a 30 Hz Vs model obtained from PS reflection-FWI. The most remarkable achievement is the flattening of the undulating chalk and top reservoir surfaces on both the Vp and Vs FWI Images, obtained from PP and PS data, respectively, which was confirmed by drilling observations.

This paper illustrates a robust methodology for achieving detailed mapping of sand injectites in the Greater Fram area using seismic attribute analysis and integrating machine learning techniques that are specifically targeted at injectites and fault prediction. Key to our methodology is the use of newly processed dual-azimuth seismic data, characterized by advances in pre-processing, noise mitigation, and velocity model building (VMB) technologies. Applying ML predictions to enable the identification of injectite geobodies provides deeper insights into these subsurface reservoirs, allowing a better understanding of the geometries of the injectites and helping to increase the efficiency of hydrocarbon exploration and production.

Subsalt imaging at Stampede field in the Gulf of Mexico has remained challenging for decades due to the existence of a large and complex sediment inclusion inside thick tabular salt. Recently, appropriate full-waveform inversion (FWI) algorithms have been developed for automatic salt model updating (Shen et al., 2017; Zhang et al., 2018). By applying this technology to newly acquired ocean bottom node (OBN) data with good low-frequency content and ultra-long offsets, we are able to invert both the shape and velocity of this complex sediment inclusion at Stampede and provide significant improvement to the subsalt image. A good starting model for FWI is still needed in this workflow, but detailed interpretation efforts are not necessary. Moreover, we expect further improved subsalt imaging if data with even longer offsets becomes available.

This study explores the predictive capability of an integrated geologic, petrologic, petrophysical and geophysical model based on detailed facies analysis of thirteen sediment cores expanded over 400 wireline log suites and 440 square miles of 3D seismic in the western Powder River Basin, Wyoming.