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We discuss challenges of Distributed Acoustic Sensing (DAS) data in a Vertical Seismic Profile (VSP) setting and propose processing and imaging solutions to overcome these. The context is to review processing challenges and benefits of DAS VSP as a potential cost-effective solution for CO2 storage monitoring. DAS VSP data acquired during a monitor surface seismic acquisition over the Johan Sverdrup field in 2021 provides the means to assess this, with both learnings and uncertainties from this study informing on the potential of this technology in other geological settings. Here, an initial feasibility assessment conducted using a baseline and repeat monitor survey, acquired a few weeks later, indicated the achievable levels of repeatability with this data type. An inclusive pre-processing flow and use of both up-going and down-going wavefields in a tailored imaging routine showcases the level of subsurface illumination and high signal-to-noise levels for 3D reservoir imaging. Finally, subsequent CO2 modelling work provides an understanding of the potential of DAS VSP surveys for future 4D monitoring work for conventional or un-conventional reservoir monitoring

We evaluate how two-dimensional (2D) seismic and controlled source electromagnetic (CSEM) imaging, both vintage and modern, might help improve geological understanding of, and exploration for, SMS deposits along part of the Mohns Ridge segment of the ultraslow-spreading Arctic Mid-Ocean Ridge. Mohns Ridge has been the subject of academic studies for the past 60 years. These have revealed that the plate boundary has a complex morphology, rifting and spreading history. Mohns Ridge is in deep water (2000-3500 m) and is an ultraslow-spreading ridge which contains significant proven resources of seafloor massive sulfide (SMS) mineral deposits.

The geological storage of carbon dioxide (CO2) and its benefits in abating unfavourable climate change have existed for decades. Even though the development of technical solutions has been slow, there has been some progress in key areas including several storage projects around the world, nations setting mandates with the hope of reaching net-zero in specific timelines, and establishment of policies and regulations to support the drive. The capture and subsequent storage of CO2 from emitters such as power stations and industrial processes, among others, play a major role in curtailing this threat to the ecosystem. A recent study indicates that the utilisation of Carbon Capture and Storage (CCS) technology could reduce CO2 emissions by 20% by 2050 (Aminu, 2017; Tomi? et al., 2018). The aim of this study is to determine the order of suitability of potential CO2 storage formations in the Gulf of Mexico Outer Continental Shelf (OCS), including both shallow and deep waters. The objectives of the study include screening of the formations with respect to geological suitability and ranking of the screened formations based on technical and techno-commercial considerations.

Quantifying and classifying pore systems in carbonates is notoriously challenging, particularly in rocks associated with complex diagenetic histories. Here we report novel computed tomography (CT) core and thin section image analyses through key reservoir intervals in the Buah Formation and Khufai Formation, both part of the Precambrian (Ediacaran) Nafun Group, from two wells located onshore Oman. Our primary objective is to constrain the volume, shape, connectivity, and distribution of vugs down-core in two and three dimensions – this is a key control on reservoir quality. We combine classic sedimentological core descriptions with image analysis on a range of data types. In this paper we focus on the analysis of core CT scan data, but we also introduce a high-level analysis of thin section images from discrete samples from the same boreholes, where available (sidewall core plugs, ditch cuttings and conventional cores). Finally, we integrated the results to (1) provide a holistic understanding of pore systems in the Buah and Khufai Formations; (2) identify the key uncertainties and weaknesses in our approach, and (3) plan for further reservoir assessment.

There have been many advances in natural language processing in recent years but most of the work have been focused on texts from a general domain or medicine and so datasets in the geology domain are sadly lacking. We demonstrate how existing taxonomy and geological texts can be used to address this issue and also show how named entity recognition and object detection can be used to retrieve information from a large number of documents.

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.

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

Geotools™ is a modern, interactive platform for the analysis of magnetotelluric (MT) and controlled source audio magnetotelluric (CSAMT) data. Continuously expanded and carefully maintained, Geotools offers a complete analysis workflow for your data — from QC, editing and 1D inversion modeling all the way to 3D inversion modeling on the Viridien Cloud.

Outcome-as-a-Service (OaaS) is a results-based model, delivering guaranteed pricing for your AI and HPC production. OaaS is available on Viridien's industrial AI and HPC cloud, fully configured and optimized for each client.