Hybrid and automated machine learning approaches for oil fields development: The case study of Volve field, North Sea

Nikitin, Nikolay O.; Revin, Ilia; Hvatov, Alexander; Vychuzhanin, Pavel; Kalyuzhnaya, Anna V.

doi:10.1016/j.cageo.2022.105061

Cited by 17 publications

(2 citation statements)

References 35 publications

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“…environmental impact assessment [134], waterlogging risk estimation [135], water storage estimation [30], water potential mapping [136], meteorological forecasting [137], ocean behaviour prediction [138]), geology (e.g. oil well placement [139], soil roughness estimation [32], soil moisture estimation [31], landslide risk estimation [140,141]), transportation (e.g. road health inspection [142]), and public health (e.g.…”

Section: Sdss With Automlmentioning

confidence: 99%

Spatial Decision Support Systems with Automated Machine Learning: A Review

Wen¹,

Li²

2022

Preprint

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Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness were discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems were also discussed to address these challenges. This paper argues that integrating spatial decision support systems with automated machine learning can not only encourage user adoption, but also mutually benefit research in both fields — bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.

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Section: Sdss With Automlmentioning

confidence: 99%

Spatial Decision Support Systems with Automated Machine Learning: A Review

Wen¹,

Li²

2022

Preprint

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“…By creating a dynamic virtual representation of real-world objects and systems, DT technology, in concert with ML, opens up new possibilities for operational optimization, analysis, and monitoring in real-time, [1]. This integration, much like the one described in [2], significantly improves operational efficiency and predictive accuracy, enhancing oil recovery and simplifying drilling operations, [3], [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Development of an Intelligent Oil Field Management System based on Digital Twin and Machine Learning

Tasmurzayev,

Amangeldy,

Nurakhov

et al. 2023

Wseas Transactions on Electronics

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This article introduces an innovative approach to oil field management using digital twin technology and machine learning. A detailed experimental setup was designed using oil displacement techniques, equipped with sensors, actuators, flow meters, and solenoid valves. The experiments focused on displacing oil using water, polymer, and oil, from which valuable data was gathered. This data was pivotal in crafting a digital twin model of the oil field. Utilizing the digital twin, ML algorithms were trained to predict oil production rates, detect potential equipment malfunctions, and prevent operational issues. Our findings highlight a notable 10-15% improvement in oil production efficiency, underscoring the transformative potential of merging DT and ML in the petroleum industry.

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