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Over the recent years, numerous case studies have highlighted the strong link between the geophysical value of seismic acquisition and the maximization of two metrics: trace density (expressed as the number of source-receiver pairs per square kilometers) and frequency bandwidth (expressed in octaves). Here, on two surveys, acquired in the Egyptian western desert, in 2009 and 2015, we confirm the value gained from building up trace density and bandwidth and how it has been efficiently achieved by moving away from historical practices like replacing areal field arrays with in-line only arrays, by reducing the amount of equipment per array, by using novel broadband sweeps with much reduced distortion levels combined with high productivity schemes. Although the imaging improvement already achieved is impressive, we believe we can further improve the trace density, and consequently the imaging, with even less equipment per shot and receiver point and the use of even more efficient acquisition scenario. This case study illustrates the high reward one can benefit by moving away from historical practices to adopt novel one’s using at best lesson’s learn worldwide combined with the adoption of latest’s acquisition technologies.

This paper presents joint 3D MT, gravity, and MEQ tomography inversions at the Darajat geothermal filed, in Java, Indonesia, using cross-gradients to link the different geophysical properties. An additional structural constraint is provided via cross-gradients to the current geological porosity model for the field. The intention is to include this manuscript in the First Break special edition "Energy, Technology, Sustainability” in June 2018.

The Atlantis Field has gone through more than two decades of continuous seismic imaging efforts, during which time many innovative technologies were incubated, the most recent one being the successful application of full-waveform inversion (FWI) in salt environments. This technique led to a significant improvement in the subsalt image. However, imaging challenges remain for the Atlantis reservoirs, primarily due to the complex overburden salt geometries and the highly compartmentalized reservoir. Even with an improved velocity model from FWI, the conventional reverse time migration (RTM) images still suffer from illumination issues and contain strong migration swings that hinder the subsalt imaging and subsequent interpretations. Furthermore, early versions of FWI employed an acoustic assumption, leading to visible salt halos at the salt boundaries in the velocity model, which adversely impacted the reservoir imaging. In the last 12 months, elastic time-lag FWI (TLFWI) and FWI-derived reflectivity (FDR) imaging using long-offset ocean-bottom node data have minimized these imaging issues at Atlantis, providing another step change in subsalt understanding. Although the 3D RTM images using the elastic FWI velocity model are similar overall to their acoustic counterparts, the 4D time-lapse RTM images at Atlantis show noticeable improvements. Furthermore, FDR images derived from elastic FWI velocities show obvious benefits over the acoustic ones. With a more accurate modeling engine that allows for better match between synthetic and real data, FDR imaging shows improved illumination, higher signal-to-noise ratio, and better reservoir details over acoustic FDR imaging. This recent advancement in using elastic TLFWI has had immediate positive effects in facilitating the Atlantis Field’s current and future development.

The Gulf of Mexico (GoM) is one of the most prolific oil and gas producing provinces in the world. The Mad Dog Field, like many large deepwater fields in the GoM, is subsalt. The geometric complexity of the overlying salt causes extremely variable image quality of the strata beneath the salt. Improving the seismic image has been critical for field development, and a tremendous amount of effort has been expended over the years to solve this problem. Over the past two decades, data acquisition has evolved from narrow-azimuth towed streamer to wide-azimuth streamer, and finally to ocean-bottom nodes. Processing methods such as using different anisotropic velocity models of increasing complexity, exhaustive iterations of salt modeling, acoustic full-waveform inversion, and most recently elastic full-waveform inversion have been applied. Dozens of wells have been drilled at Mad Dog guided by the resulting seismic images, and many acquisition and processing learnings have been acquired and implemented over this period to optimize the imaging. This paper explores the techniques that have caused major uplift to subsalt imaging and some techniques that were of only minor impact, while giving a glimpse into the imaging history of one of the GoM’s giant fields.

Barreirinhas basin is located in the north-east of Brazil and is part of the Brazilian equatorial margin, a new exploration frontier with complex geology. This basin is characterized by a rugose water bottom, a fast carbonate platform, shallow gas pockets and a complex channel network. All these elements represent a big challenge for the velocity model building and the imaging of the depositional system. From the pre-processing to the final imaging, high-end technologies were required to meet the processing objectives: 3D de-signature and 3D de-ghost, dip-constrained tomography and FWI, Q tomography and Q-PSDM.

All seismic data, whether 2D, 3D, post-stack or pre-stack contains noise. Typically, this noise is comprised of both coherent and random components. Coherent noise presents itself as regular patterns in the seismic data. It may appear to be random or coherent depending on the orientation of the slice on which it is being observed. For example, coherent noise associated with acquisition may appear random on vertical slices through the volume, with its coherent nature becoming apparent on horizontal slices through the volume.

This case study of an advanced quantitative interpretation workflow is tailored for a seismic multiclient program in the Wolfberry Play of the Midland Basin. Seismic imaging begins by providing a structural interpretation basis, then quantitative interpretation provides 3D elastic and geomechanical attributes through prestack inversion and azimuthal inversion, respectively. This 3D canvas of elastic attributes is combined with petrophysical, mineralogical, geomechanical, and geochemical properties measured at the wells. The challenge of reconciling such data sets with different scales and spatial sampling is overcome using physical, empirical, or statistical relationships within the data. The adjunction of rock data to the 3D elastic attributes provides calibration and validation of the inversion results and quantitative prediction of lithology, facies, porosity, and geochemical properties away from the wells.

We present the application to a 3D real dataset of full waveform inversion (FWI) with optimal transport (OT) using the Kantorovich-Rubinstein (KR) distance as proposed by Métivier et al. (2016). This approach involves an efficient numerical implementation for OT in time and space directions, allowing taking into account lateral coherency of the traces; this has an important impact on the quality of the results. The approach also exhibits a slightly reduced sensitivity to local minima compared to least square (LSQ) misfit. Moreover the iterative method used for the computation of the KR distance allows producing a set of intermediary solutions that span progressively from LSQ to OT. We recall the main components of the approach and present its numerical implementation in 3D. We show the improvement of the results compared to conventional FWI on 2D synthetic and 3D real datasets for the same number of velocity update iterations.

Guided wave inversion (GWI) estimates accurate P-wave velocity in the near surface by analyzing the dispersion curves of guided waves. In this paper, we propose a robust inversion scheme to reduce the non-unique solutions and discuss the usage of GWI for full waveform inversion. This method is applied to a North Sea towed steamer survey. The FWI converge faster with GWI and the updates are more geologically plausible. GWI improves the seismic images and gather flatness at both shallow and deep targets.