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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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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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Leveraging Legacy Data

Oilfield Technology, March, 2019
Jo Firth | Priyabrata Pradhan
©2019 Palladian Publications Ltd

In recent years, there have been many rapid developments in subsurface imaging, meaning that even data sets that are only two or three years old can benefit from reprocessing. Reprocessing older data, either on its own or in combination with new data, is both practical and cost-effective as new acquisition can be expensive and time-consuming, especially in areas where there are seasonal constraints due to climate, fishing or breeding seasons. The Cornerstone Evolution project in the Central North Sea demonstrates the value achieved by reprocessing a large number of older surveys in conjunction with newer acquisition.

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Australia: Depth Reprocessing Rejuvenates Gippsland Basin

Geo Expro, March, 2019
Peter Baillie | Paul Carter | Jarrad Grahame | Joe Zhou | Nigel Mudge
©2019 GeoPublishing Ltd

The world-class oil and gas fields of the Gippsland Basin, with original recoverable reserves of more than four billion barrels of oil and around ten trillion cubic feet of gas, were discovered following a 1962 2D seismic survey. Despite considerable exploration, it has long been known that unresolved seismic depth imaging issues have had a significant impact on data quality. As a consequence, the province probably has unrealised exploration potential, particularly in the deeper stratigraphic section. The basin-wide Gippsland ReGeneration reprocessing project by CGG has changed the paradigm and the basin is now seen as rejuvenated, with new exploration opportunities and significant upside potential.

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Machine Learning for Better Wells

Hart's E & P, February, 2019
Fred Jenson
©2019 Hart Energy

Machine learning is rapidly becoming a standard technology within the oil and gas industry. This is especially true in petrophysics, where Big Data tend to need more efficient and faster data analysis. The term “machine learning” was coined in 1959 by Arthur Samuel and can be defined as data-driven predictions of behavior rather than rule-based algorithms. Essentially, it is a computer science that uses statistical techniques to give computer systems the ability to learn with data and without being explicitly programmed. A simple example is to record many measurements of the time required for objects of differing attributes to fall various distances and then build a predictive model using linear regression. This predictive model would not be based upon the theory of gravity or the gravitational constant. Instead, through many observations, the model would learn the underlying order in the data. Supplying more data to the model would increase the model’s accuracy. Thus, machine learning models should improve and become better over time as more data become available.

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High-frequency acoustic land full-waveform inversion: a case study from the Sultanate of Oman

First Break, January, 2019
Anna Sedova | Gillian Royle | Thibaut Allemand | Gilles Lambare | Olivier Hermant
©2019 EAGE

In this paper we demonstrate the ability of acoustic land FWI to recover a high-resolution velocity model using reflected waves in addition to diving waves and inverting up to 13 Hz.

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