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PhD projects open for application with Iapetus Doctoral Training Partnership

GeoDataScence group is involved in PhD projects short-listed by Iapetus  Doctoral Training Partnership open for applications: Modelling Natural Hydrogen systems using Agent Based modelling (IAP-24-121) supervised by Dr Daniel Arnold (HWU), Dr Simon Gregory (BGS), Prof Vasily Demyanov (HWU), Dr Uisdean Nicholson (HWU) In this project, we aim to pioneer a novel tool for natural […]


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AutoWell paper featured in JPT September 2024 issue

SPE-218470-MS A New Automated Workflow for Well Monitoring Using Permanent Pressure and Rate Measurements, by Shchipanov, A., Cui, B., Starikov, V., Muradov, K., Khrulenko, A., Zhang, N. & Demyanov, V., was featured in Journal of Petroleum Technology overview by Editor Chris Carpenter, J Pet Technol 76 (09): 86–89 , Paper Number: SPE-0924-0086-JPT


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Overview presentations at GEOSTATS 2024 and RING 2024 meeting on Uncertainty in AI-based reservoir modelling workflows

Modern developments of AI tech open outstanding opportunity to handle generic tasks with the methods designed to handle diverse and noisy data: starting from interpretation of exploration data, coming up with reservoir modelling concepts, describing reservoir characteristics and property distribution, integration of dynamic data and model calibration and update as new data become available. AI […]


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Feature Extraction and Pattern Recognition in Time-lapse Pressure Transient Responses – a new paper published in Geoenergy Science and Engineering

Feature Extraction and Pattern Recognition in Time-lapse Pressure Transient Responses, by V. Starikov, A. Shchipanov, V. Demyanov, K. Muradov, was accepted for publication and available online with Geoenergy Science and Engineering. The publication is one of the outcomes of AutoWell project –  Automated Well Monitoring and Control, 2022-2025,  PETROMAKS2 funded project  supported by Research Council […]


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Gleb Shishaev’s PhD thesis on history matching with Graph VAE finalised and approved.

Dr Gleb Shishaev’s  PhD thesis has been done and dusted – minor correction completed and approved by the examiners. The PhD Thesis —  History Matching and Uncertainty Quantification of Reservoir Performance with Generative Deep Learning and Graph Convolutions was examined by Prof Denis Voskov (external, TU Delft), Prof Ahmed ElSheikh (internal) and found worthy a PhD degree. […]


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ICURe Discover Award for Vitaly Starikov

Vitalii Starikov has been honoured with ICURe Discover Award from Innovate UK for his new R&D project idea in NetZero. The start-up idea, AI Methane Tracker, is to develop an intelligent system to detect and monitor methane emissions using satellite imagery. Vitalii’s ICURe Award  secures essential funding for early market discovery activities, further advancing the […]


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GeoDataScience talks at EAGE Petoleum Geostatistics, Porto

Three data science talks were contributed to EAGE Petroleum Geostatistics programme: Vitaliy Starikov presented a talk Unsupervised Classification of Flow Regime Features in Pressure Transient Responses sharing the progress of the AutoWell consortium. Dr Chao Sun and Gleb Shishaev were not able to present their work in person because of the visa logistics. However, their […]


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3D realistic fluvial facies modelling with GANs

This new Chao Sun’s publication competes his PhD thesis passed successfully earlier this year. A conditional GAN-based approach to build 3D facies models sequentially upwards  by Chao Sun, Vasily Demyanov, Daniel Arnold published in Computers & Geosciences Volume 181, December 2023, 105460 extends the earlier GAN papers and  presents an alternative way of simulating 3D […]


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Athos Nathanail successfully passed his PhD viva with the thesis – Capturing interpretational uncertainty of depositional environments with Artificial Intelligence

Congratulations to Athanasios A. Nathanail with passing his PhD viva with “flying colours” examined by Prof Dorrik Stow and Prof Mikhail Kanevski. The thesis offers a comprehensive insight into how AI can aid capture deposition interpretational uncertainty from outcrop patterns to make inferences about the conceptual model uncertainty based on the limited in core data […]


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