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Exploration and production companies continuously strive to find and develop new recoverable hydrocarbon reserves. For every segment, activating critical data faster can give companies a competitive advantage. From limited infrastructure, barriers to scale, limited transmission bandwidth and network capacity, and improved clock time from data acquisition to processing, as well as the difficulty of aggregating, transporting, and activating large data sets, presents a clear challenge to exploration and production companies’ turnaround time. As the evolution of data acquisition technologies has outpaced approaches to data management in the field, this problem has only compounded over time. This paper will address the key challenges to energy industry data management and provide useful solutions for data aggregation, transportation and activation. As data becomes more and more of a significant strategic resource for exploration and production enterprises, it will become vital that service providers discover a new approach to acquiring and processing data in the field. This solution will need to be a cost-effective, efficient, and scalable physical data transfer solution that is built both for mass-capacity storage in the field and frictionless physical transfer to any cloud service.

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Distributed acoustic sensing (DAS) data recording a large amount of the subsurface information become more promising for real-time seismic monitoring. Therefore, fast and accurate imaging techniques are required to handle large datasets. Besides the issue of the computation cost, most of the migration methods, such as reverse-time migration (RTM) and Kirchhoff migration suffer from the artifacts, influencing the quality of the image, because of the limited-aperture data. Here, we perform a migration, based upon the Fresnel volume on the simulated geophone VSP and DAS VSP acquired by newly developed fibre-optic cables in Canada. We show that the Fresnel volume migration with a competitive runtime is superior and robust compared to the RTM and Kirchhoff migration. The angle-domain common-image gathers (ADCIGs) extracted from the Fresnel volume migration is more reliable and cleaner than that of the conventional Kirchhoff migration, used further for the AVO analysis and inversion.

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Summary As part of the research project i2MON – ‘Integrated Impact MONitoring for the detection of ground and surface displacements caused by coal mining’ funded by HORIZON 2020 through the RFCS (Research Fund for Coal and Steel), the leading author, in collaboration with various European institutions, is working on the development of an integrated monitoring service for the identification and assessment of ground and slope movements associated with coalmining. The focus is on the correct integration (reliability, accuracy and integrity) of a long-range laser scanner into a continuous web-based monitoring system from an engineering geodetic point of view. The objective of this work is the development of an integrated monitoring service for the identification and evaluation of ground and slope movements in the context of coalmining, the prevention of natural hazards and protection of infrastructure. The focus is on the integration of a long-range laser scanner (1) into a continuous web-based monitoring system and (2) from an engineering geodetic point of view. In the Vals Valley in Tyrol, a permanently installed RIEGL laser scanner was successfully operated and web-based in the area of a rockfall in the summer of 2020 and 2021. The data was visualised in near real-time on a monitoring platform. This paper will describe the practical benefits of this installation. In addition to the potential of automatic data acquisition, possibilities for multi-temporal analysis with regard to spatio-temporal changes will be discussed.

In recent years our industry has progressed to acquiring ever more densely sampled seismic data. The motivation for this very costly undertaking is to improve both the resolution and reliability of the images obtained from these data used to locate hydrocarbon prospects, assist with carbon capture and storage or wind-farm turbine foundation design. However, we know from experience in other applications that images can often be subsampled, or compressed, without significant loss in image quality. Viewed from another perspective, if we can indeed compress images without significant loss in quality, this raises the question as to whether we really need to acquire very densely sampled data in the first place. Here I will review some aspects of data sampling and recent developments in optimal sparse sampling of data known as compressive (or compressed) sensing.

In this paper we present a play-based methodology applied to CO2 storage site screening in the UK North Sea. The study develops and refines existing CO2 storage prospectivity evaluations as well as identifying new opportunites. The work demonstrates the value of incorporating seismic data and the role of seismic interpretation in geological storage assessments.

For many developed fields, not all drilled sections have measured sonic log data acquired in them, necessitating their prediction using well-established conventional methods. The empirical Greenberg-Castagna relation and theoretical rock physics models like Xu-Payne are two examples of these methods. However, considering recent developments and the exposure of numerous machine learning techniques, we used our own internally developed deep-learning-based shear prediction method for a thick Jurassic carbonate sequence and compared the outcomes with those of more conventional methods to evaluate the performance. Although it takes some time at first to correctly train the datasets and calibrate the machine learning parameters, the deep learning prediction method had an accuracy similar to the conventional approaches but outperformed the traditional approaches in terms of workflow efficiency.