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The benefits of CGG technologies and services are regularly featured in the industry press. Find out more by consulting our e-library of published industry articles. Narrow your search by entering at least one search criterion:

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Machine learning and geophysical inversion – A numerical study

The Leading Edge, July, 2019
Brian Russell
©2019 SEG

Much recent work has been done on comparing machine learning and geophysical inversion techniques to the extraction of model parameters from seismic reflection data. In our profession we are used to analyzing the physics of geophysical problems in detail. However, in many of the recent studies the machine learning algorithms are treated almost as “black boxes”. In this study l I will use a straightforward numerical example to illustrate the difference between geophysical inversion and machine learning inversion. In doing so I will try to “demystify” machine learning algorithms and show that, like inverse problems, they have a definite mathematical structure that can be written down and understood. The example used is this tutorial is the extraction of the underlying reflection coefficients from an overlapping wavelet response that was created by convolving a reflection coefficient dipole with a symmetric wavelet. In discussing the solution to this problem I will cover the topics of deconvolution, recursive inversion, linear regression and nonlinear regression using a feedforward neural network. I will present both the full inverse approach as well as gradient descent algorithms, which can be applied to both linear and nonlinear problems. This will lead to a description of the backpropagation algorithm, which is used to train a feedforward neural network. In the final section of the tutorial I will look at the impact of local minima in the search for a global minimum in the backpropagation algorithm.

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Integration in Exploration

Oilfield Technology, May, 2019
Carl Watkins | Daniel Carruthers | Pedro Martinez Duran | Simon Otto | Mark Cowgill
©2019 Palladian Publications Ltd

Integrated exploration, new workflows for new challenges highlighting JumpStart Gabon, Marine Source Predictions and Encontrado reprocessing projects.

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Consolidating rock-physics classics: A practical take on granular effective medium models

The Leading Edge, May, 2019
Fabien Allo
©2019 SEG

The paper presents a review of classic rock physics models used for clastic sedimentary rocks and how they have been combined into extended models through the introduction of a few parameters associated with a compositional or textural property of the rock. The models are used on a variety of real data sets to showcase how rock properties can be inferred from elastic properties.

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3D fault imaging using windowed Radon transforms: an example from the North Sea

First Break, May, 2019
Geoffrey Dorn
©2019 EAGE

The interpretation of fault surfaces is key to understanding the subsurface geology represented in 3D seismic volumes. The geologic structure represented by seismic reflections can be auto-tracked in the volume. Faults, however, are imaged as discontinuities or changes in curvature in the seismic data. For many years, fault interpretation involved manually picking fault cuts on orthogonal slices through the seismic volume. These fault cuts were grouped into conceptual faults, and 3D fault surfaces were created from the fault cuts.

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Full-waveform inversion for salt: A coming of age

The Leading Edge, March, 2019
Ping Wang | Zhigang Zhang | Jiawei Mei | Feng Lin | Rongxin Huang
©2019 SEG

Full-waveform inversion (FWI), proposed by Lailly and Tarantola in the 1980s, is considered the most promising data-driven tool to automatically build velocity models. Many successful examples have been reported using FWI to update shallow sediments, gas pockets, and mud volcanoes. However, successful applications of FWI to update salt structures had almost only been seen on synthetic data until recent progress at the Atlantis field, Gulf of Mexico (GoM). We revisited some aspects of FWI algorithms to minimize cycle-skipping and amplitude discrepancy issues and derived an FWI algorithm that is able to build complex salt velocity models. We applied this algorithm to a variety of data sets including WAZ (wide-azimuth) and FAZ (full-azimuth) streamer data as well as OBN (ocean bottom node) data with different geologic settings in order to: 1) demonstrate the effectiveness of the method for salt velocity updates, and 2) examine some fundamentals of the salt problem. We observe that in multiple cases, salt velocity models from this FWI produce subsalt images of superior quality. We demonstrate with one FAZ streamer data example in Keathley Canyon that we probably do not need very high frequency in FWI for subsalt imaging purposes. Based on this observation, we envision that sparse node for velocity (NFV) acquisition may provide appropriate data to handle large and complex salt bodies with FWI. We believe that the combination of advanced FWI algorithms and appropriate data acquisition will bring a step-change to subsalt imaging.

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