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Over two million files, containing geochemical information, have been collected from tens of thousands of wells drilled during decades of exploration in the Gulf of Mexico (GOM) and are available to geoscientists in the public domain. While these files represent a vast knowledgebase covering subsurface geology and petroleum systems, data extraction, systematic compilation and quality control was previously too cumbersome to harness the full power of the data to make basin wide correlations, uncover new trends and ultimately opportunities. A novel machine learning approach was employed to automate data classification and extraction across three protraction areas for all public domain geochemistry and PVT documents to provide a single consistent database from un-tagged, legacy formats stored in entirely different subfolders. The resulting database provides the ability to rapidly screen and integrate data from multiple disciplines over a large scale, in terms of data volume as well as geospatial coverage. This in turn opens up petroleum systems analysis work to a wider user base by acting as a bridge between disciplines, such as reservoir engineering and geochemistry. Removing disciplines from silos is critical to enhancing collaboration between teams, improving efficiencies around specific workflows such as fluid property prediction and therefore reducing uncertainty.

A subsurface volume that can be reliably interpreted in terms of geologically-relevant attributes is a reasonable objective for products from depth inversion workflows. Commonly the field geophysics data available are inherently non-unique and deficient (noise, aliasing, etc.), so an implementation of some type of constraint is required to encourage reasonable inversion outputs. We illustrate an implementation of cross-gradient inversion where surface geological information is included in the input data set. The basic application covers the usual structural similarity objective – comparing the gradient fields of distinct property volumes derived from different geophysical domains – but a particular advantage comes when including gradients derived from surface or subsurface geology, or any ancillary property set, providing reference gradient control during single or joint domain inversions of geophysical data.

CGG’s Dr. Ellie MacInnes looks at how geoscience can help deliver economical, efficient geothermal energy in line with global sustainability targets.

The development of time-lag FWI (TLFWI) in recent years has enabled the use of the full wavefield (primary reflection, multiple, ghost, and diving waves) in inversion. With this advance it is now possible to include ever more detail in the velocity model, ultimately reaching the point of deriving from the velocity a migration-like reflectivity image, called the FWI Image. When the FWI maximum inverted frequency is increased, velocity model details can reveal superior reservoir information than present in recent conventional imaging results. Two case studies will be discussed, the first in the Greater Castberg area where the 150 Hz FWI Imaging greatly surpassed the Q Kirchhoff pre-stack depth migration imaging from the water bottom level down to the reservoir, located at a depth of around 1.5 km. For the second, over the Nordkapp basin, use of the full wavefield for shallow ultra-high resolution (UHR) imaging run at 200 Hz revealed reverse faulting and pockmark details that were invisible with either KPSDM or RTM. By using additional information present in multiple, ghost and diving waves, a spatial resolution of 2 m was achieved, making it possible to image very thin features without the need for a dedicated high-resolution acquisition design. The current UHR FWI Image obtained in the near-surface can then be used to de-risk and plan well placement as well as the foundations for wind turbines, providing important velocity information in addition to the reflectivity image.

Although the resolution of a seismic image is ultimately bound by the spatial and temporal sampling of the acquired seismic data, the seismic images obtained through conventional imaging methods normally fall far short of this limit. Conventional seismic imaging methods take a piecemeal approach to the imaging problem, with many steps designed in preprocessing, velocity model building, migration, and post-processing to solve one or a few specific problems at each step. The inefficacies of each step and the disconnects between them lead to various issues, such as velocity errors, residual noise and multiples, illumination holes, and migration swings, that prevent conventional imaging methods from obtaining a high-resolution image of good S/N and well-focused details. In contrast, full-waveform inversion (FWI) Imaging, which models and uses the full-wavefield data, including primaries and multiples, reflections, and transmission waves, to iteratively invert for the velocity and reflectivity in one go, is a systemic approach to address the imaging issues. FWI Imaging has proven to be a superior method over conventional imaging methods for providing seismic images of greatly improved illumination, S/N, focusing, and thus higher resolution. We demonstrate with a towed-streamer data set and an OBN data set that FWI Imaging with a frequency close to the temporal resolution limit of seismic data (100 Hz or higher) can provide seismic images of unprecedented resolution from the acquired seismic data, which has been impossible to achieve with conventional imaging methods. Moreover, incorporating more accurate physics into FWI Imaging, for instance, upgrading the modeling engine from acoustic to elastic, can substantially improve the seismic resolution further. Elastic FWI Imaging can further reduce the mismatch between modeled and recorded data, especially around bodies of large impedance contrast, such as salt, and appreciably improves the S/N and therefore the resolution of the inverted images. We show with an OBN data set in the Gulf of Mexico that elastic FWI Imaging further improves the resolution of salt models and subsalt images over its acoustic counterpart.

Paper written in association with SNH for the March 2018 additions of First Break. Paper focusses on understanding why well success was low for the offshore Douala basin and how the interpretation of the results of the GeoSpec Regrid will enable a better understanding of the prospectivity. The paper pulls together the work of GeoSpec, SRC and Robertsons to show an integrated approach to reviewing the prospectivity.

The increasing focus on de-risking near-field exploration in the Northern Viking Graben area demands data with improved imaging. This has led to CGG initiating a new multi-year acquisition programme to acquire east–west triple-source multi-sensor seismic data over a 24,000 km2 area, adding a second azimuth to the existing 44,000 km2 north-south survey. Leveraging the two azimuths in conjunction with the latest imaging and processing technologies produced a significant uplift over the 2018 single-azimuth legacy data. New DAZ data achieved improved structural imaging, illumination, resolution and SNR but also greater confidence in interpretability and seismic inversion studies.

Acting as a technology-agnostic, standards-based data platform, the OSDU has reduced energy data silos and provided the capability for applications developers to build new solutions and data ingestion services. The current OSDU schemas are primarily created to store file metadata to allow users to query common business content that can be extracted from the files. We utilized a machine-learning and subject matter expert classification process to auto-generate detailed file metadata for millions of files and ingest them directly to the user OSDU instance with source files. The file classification process currently generates a graph database representation of files and rich metadata labels at a data-object level. The classification results, alongside data lineage and quality, are stored in OSDU work product components and datasets and ready to migrate to the OSDU platform. The process prevents users having to manually fill or supply the file manifests during file ingestion to their OSDU implementation. With over 700 distinct data types and 250,000 entities of subsurface terminologies, millions of ingested files can be enriched with highly granular metadata manifests that guarantee rapid data search and access to high-quality data.

The scope for using EO satellites for routine monitoring of offshore operations is continually expanding in ways that make factors of cost and time more feasible than ever before. Near-real-time processing and delivery remain the most appropriate solution for emergency scenarios; however, proactive monitoring programs can be undertaken to routinely acquire imagery over offshore infrastructure and environmentally sensitive areas, thus enhancing situational awareness for all interested parties. Increased awareness of activities and incidents occurring around offshore infrastructure can be compiled into a continually growing database to demonstrate good practise, provide an historical account of incidents, and feed into response scenarios.