Data Management in Oil and Gas Business: Case studies

The purpose of the EAGE Local Chapter Paris is to Engage with the multicultural and multidisciplinary geoscience network in Paris and internationally within the EAGE to promote young talent and innovation and to foster the recognition of the geoscientific profession in the community at large.The EAGE Local Chapter Paris Board is composed by:President: Laura MozgaVice-President: Emilio Darriba LeguenSecretary: Pedram MasoudiTreasurer: Guillaume HeninHead of Events: Hayet SerradjiHead of Company Relations: Soraya BornazHead of Communications: Pierluigi NichiloHead of Students Relations: Elias Macuacua

EAGE Local Chapter Paris

Online Event organized by EAGE Local Chapters Paris and Stavanger

This webinar is the second in a series of webinars dedicated to data management in Oil and Gas. It will focus on case studies, from new data formats to Data Analysis and automatized processing achieved thanks to deep learning. Speakers:F. Baillard, CTO of Iraya EnergiesM. Ofstad, Head of R&amp;D at BluwareG. Gigou, Sr Geophysist Project leader at CGG Chairman: Pierluigi Nichilo, Communication Lead of EAGE Local Chapter Paris Agenda Sand Production and Sand Control Benchmarking Through Unstructured Data Analysis with Machine Learning in the North Seaby F. Baillard (Iraya Energies)Sand production has been serving as a bottleneck to the oil and gas industry, contributing to disruption of daily production operations, causing deformation, erosion of well tubing, pipelines, and surface equipment, expediting significant non-productive time (NPT). To administer a holistic basin study for sand production consisting of hundreds of wells with conventional manual methods is considered complex and time consuming. Information and reports may be derived from millions of pages of legacy well reports, documentations, or files from over 40 years ranging from digitized medium to hand-written reports in countless formats. A sustainable strategy of data-driven basis shall be leveraged to address the knowledge and information management issues utilizing the latest advancement of Machine Learning (ML) and data analytics to maximize the potential of unstructured data. Working with seismic data in VDS formatby M. Ofstad (Bluware)Volume Data Store (VDS) is a storage format for fast random access to multi-dimensional (up to 6D) volumetric data. The VDS format was developed by Bluware and an open-source library to read and write the VDS format has been contributed to the Open Subsurface Data Universe (OSDU). VDS may be used to store common subsurface data types such as regularized single-Z horizons/height-maps (2D), seismic lines (2D), pre-stack volumes (3D-5D), post-stack volumes (3D), geobody volumes (3D-5D), and attributes volumes of any dimensionality up to 6D.The format has been designed primarily to support random access and on-demand fetching of data. This enables applications to be responsive and interactive and efficient I/O for high-performance computing or machine learning workloads. We will cover the basics of how the VDS format is used and the tools to work with the format. We will also demonstrate how to use VDS data in a Jupyter notebook. Deep neural networks for ultra-fast track processingby G. Gigou (CGG)To tackle imaging challenges of complex exploration areas, modern acquisitions are heading towards dense datasets with very high spatial resolution. Imaging of these new products requires specific and intricate processing steps that are not easy to handle onboard or through a fast-processing flow.The need for a very fast track sequence arose recently for a Barents Sea survey. The latter aimed at improving the Nordkapp basin and consisted of a widespread hexa-source sitting on top of 18 non-flat streamers. Considering the shot-point density involved, use of classic processing for the fast track would have required time consuming processes and advanced algorithms to preserve its native spatial resolution. As an alternative to conventional processing roads, capabilities of Deep Neural Networks were leveraged to perform the pre-processing stages, ensuring quality in a limited timeframe. Pseudo-synthetic training sets were built from the first batch of received data and data augmentation was applied, with different settings for each processing steps.The result obtained showed an improved resolution with respect to the legacy volume and was used to start interpreting potential hydrocarbon prospects and initiate the velocity model building. The Ultra-Fast Track also served as a helpful tool to assess the remaining challenges to be faced during the full processing of this data.

Data Management in Oil and Gas Business: Case studies

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Data Management in Oil and Gas Business: Case studies

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