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 […]
Author: Vasily Demyanov
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 […]
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. […]
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 […]
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 […]
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 […]
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 […]
Chao Sun successfully passed his viva with a thesis on GAN fluvial facies distribution modelling
Congratulations to Chao Sun with his successful PhD viva as the result of 4 years of hard work as a NERC CDT in Oil and Gas PhD student supervised by prof. V. Demyanov and Dr. D. Arnold. The examiners – Prof Guillaume Rongier (TU Delft) and Prof. Ahmed Elsheikh (HWU) have assessed the work in […]
Quentin Corlay successfully passed his viva with a thesis on detection of geobodies in 3D seismic with ML
Congratulations to Quentin Corlay with his most successfull viva as the result of 4 years of hard work as a NERC CDT in Oil and Gas PhD student supervised by prof. V. Demyanov, Dr Dave McCarthy (BGS), Dr. D. Arnold. The examiners – Prof Guillaume Caumon (Uni Lorraine) and Dr Uisdean Nicholson (HWU) have assessed […]
A new GAN study for fluvial facies modelling got published in Computational Geosciences as an outcome of Chao Sun’s PhD
Geological realism in Fluvial facies modelling with GAN under variable depositional conditions by Chao Sun, Vasily Demyanov, Daniel Arnold Computational Geosciences, 10.1007/s10596-023-10190-w This study investigates generative adversarial networks (GANs)’ capacity to model multi-facies distributions of meandering systems. Earlier works showed that GANs outperform geostatistical methods in reproducing complex geometry, like the shapes of fluvial channels. […]