Titled “Securing a sustainable future together”, the Technical Program for this year’s annual EAGE conference in Vienna, 5-8 June, again incorporates the SPE Europe Energy Conference (EuropEC). Representing Europe’s largest multidisciplinary geoscience and engineering event, particular emphasis is being given to collaboration, research, and innovation to advance decarbonization. SPE EuropEC will cover capture, utilization, and storage of fluids in the subsurface; extraction of heat (geothermal) and materials (e.g., metals) from the subsurface fluids; carbon-efficient reservoir management; machine learning, AI, and digitization for more efficient operations; sustainability in the energy industry; and net zero energy economics. The broader EAGE 2023 technical program correspondingly incorporates dedicated SPE, Geophysics, Geology, Reservoir Engineering, Integrated Subsurface, Energy Transition, and Mining and Infrastructure streams.

PGS is Contributing to 10 Oral Presentations in the Technical Program.

Three presentations will showcase the benefits of simultaneous inversion for velocity and reflectivity; effectively a combination of Full Waveform Inversion (FWI) and Least-squares Reverse Time Migration (RTM). “Prospectivity insights from simultaneous velocity and reflectivity inversion, Offshore Newfoundland and Labrador, Canada” by Montevecchi et al. (OilCo) showcases how the delivery of high-resolution velocity data, true vector reflectivity volumes, and estimates of relative impedance and density, enabled better geological understanding and aiding the de-risking of new prospects. Two distinct areas were examined in different geological settings, one in the Orphan Basin and another in the Salar Basin. This work has improved signal to noise levels in data-challenged areas and refined knowledge about compaction trends, velocity anomalies, and rock properties. Similarly, “Application of simultaneous inversion (FWI) and nonlinear LS-RTM) for improved imaging” by Pankov et al. demonstrates how the inversion applied to seismic data from the Outer Vøring basin in the Norwegian Sea, improved structural imaging, healed fault shadow zones, provided better seismic to well ties, and allowed for the direct derivation of reservoir properties such as relative density. Finally, “High resolution quad source acquisition and processing for improved imaging around the Wisting field, Barents Sea” by Dhelie et al. (AkerBP) applies an alternative multi-parameter FWI imaging solution to a novel acquisition solution that included four sources towed within a dense 12-streamer setup and with a -250m offset in the Barents Sea area.

Machine Learning

Huang et al. introduce a new machine learning solution to estimate velocity models directly from seismic shot records in “Deep learning velocity model building using Fourier Neural Operators”. Fourier Neural Operators (FNOs), initially created for solving Parametric Partial Differential Equations (PDEs), utilize global convolutions efficiently calculated through Fast Fourier Transforms, enabling them to represent non-linear and non-local operators more accurately than Convolutional Neural Network (CNN) architectures. An adapted FNO architecture was used in this work to estimate velocity models from 40,000 synthetic shot gathers, generated using realistic field acquisition parameters. Applied to seismic data from offshore Newfoundland, Canada, the deep learning FNO-based workflow has the potential to significantly automate the model building process and reduce the turnaround time of imaging projects.

Continuing the focus on machine learning solutions, “Multisensor noise attenuation with RIDNet” by Farmani et al. presents new workflows for attenuating noise in seismic data from multisensor streamers using a convolutional neural network called Real Image Denoising Network (RIDNet). The workflows, extensively validated using different survey data, are fully automated, require no user interaction or re-training, and are now available for production use.

Norwegian Sea

“Reprocessed reservoir optimized seismic supports understanding and development of PL586 Fenja Field (Norwegian Sea)” by Otterbein et al. is a collaborative seismic rejuvenation project between Neptune Energy Norge and PGS over the Fenja Field in the Norwegian Sea. Reservoir-oriented processing was executed in phases, with Phase 1 focusing on processing for the Fenja area to optimize well placement, and Phase 2 involving full reprocessing to form the basis for further work. The results have been utilized for optimizing drilling campaigns, with the next phase set to use the updated geomodel for dynamic flow simulation and optimization as production data becomes available from Q1 2023.

New Energy 

Three presentations related to towed streamer acquisition highlight the innovative applications of PGS technology to developing “New Energy” markets, reducing sound emissions, and reducing carbon emissions. “Advanced high-resolution 3D streamer seismic acquisition solutions for new energy applications” by Widmaier et al. describes how towing configurations using multi-sources and multi-sensor streamer spreads have been increasingly utilized for hydrocarbon exploration and other studies like carbon capture and storage (CCS) site characterization. These configurations enhance near offset coverage and spatial sampling, enabling high-resolution imaging of both shallow and deep geological structures in a cost-effective way. “The acoustic wavefield generated by a vessel sailing over ocean bottom cables” by Hegna et al. builds upon previous applications of this novel method that uses the acoustic wavefield generated by a vessel, rather than traditional airgun sources, to image the subsurface. Ocean bottom cable (OBC) data acquired for AkerBP on the Norwegian continental shelf, was shown to reveal most reflectors found in the active-source data. These findings suggest that the method can be employed for monitoring purposes, offering a low-cost, low-impact solution with more frequent acquisition of time-lapse (4D) data, particularly in areas with permanent receiver installations. “Mitigation of CO2 emissions from marine seismic surveys via drag reduction and digital transformation initiatives” by Long et al. describes digital transformation initiatives that optimize marine seismic survey performance and reduce fuel consumption and associated Greenhouse Gas (GHG) through the real-time deployment of machine learning solutions. These efforts involve the development of long-term insights for better survey design, vessel management practices, and the reduction of drag forces, thereby creating more sustainable operations, reducing physical waste, and optimizing shooting plans and maintenance of physical assets.

“Interactive rock physics for CCS and near field exploration, a UK Southern North Sea case study” by Reiser and Ruiz discusses the importance of finding, screening and characterizing Carbon Capture and Storage (CCS) sites as part of a suite of approaches suitable for monitoring, measurement, and verification (MMV) of subsurface CO2 storage. A workflow was developed in the Southern Gas Basin in the UK Continental Shelf, allowing for an interactive rock physics analysis to evaluate seismic sensitivity to CO2 injection. This workflow can simulate the impacts of CO2 injection on the rock frame, offering a more accurate assessment of seismic sensitivity to changes in CO2 saturation and pressure. This will assist in making decisions about acquiring, reprocessing, and licensing new seismic data for relevant projects.