Research graph
References from Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future. Local targets link to admitted publications; unresolved targets remain external evidence.
What can machine learning do? Workforce implications
10.1126/science.aap8062 · 2017 · External reference
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2019 · External reference
Notes from the AI frontier: Modeling the impact of AI on the world economy
2018 · External reference
Competing in the age of AI
2020 · External reference
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Strategy, not technology, drives digital transformation
2015 · External reference
Applications of artificial intelligence in oil and gas development
2020 · External reference
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Oil and gas 4.0 era: a systematic review and outlook
10.1016/j.compind.2019.06.007 · 2019 · External reference
From disruptively digital to proudly analog: a holistic typology of digital transformation strategies
10.1016/j.bushor.2019.07.002 · 2019 · External reference
Decision support methods and applications in the upstream oil and gas sector
10.1016/j.petrol.2018.10.050 · 2019 · External reference
Decision making in renewable energy investments: a review
10.1016/j.rser.2015.11.021 · 2016 · External reference
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2013 · External reference
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Subsurface velocity inversion from deep learning-based data assimilation
10.1016/j.jappgeo.2019.04.002 · 2019 · External reference
Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data
2019 · External reference
The use of machine learning algorithms in recommender systems: a systematic review
10.1016/j.eswa.2017.12.020 · 2018 · External reference
Predicting porosity, permeability and water saturation applying an optimized nearest-neighbour, machine-learning and data-mining network of well-log data
2019 · External reference
Robotized petrophysics: machine learning and thermal profiling for automated mapping of lithotypes in unconventionals
10.1016/j.petrol.2018.03.110 · 2018 · External reference
Deep convolutions for in-depth automated rock typing
2019 · External reference
Prediction of porosity and permeability alteration based on machine learning algorithms
10.1007/s11242-019-01265-3 · 2019 · External reference
Upscaling relative permeabilities in a structured porous medium
10.1016/j.advwatres.2004.11.009 · 2005 · External reference
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2016 · External reference
Recent progress on reservoir history matching: a review
10.1007/s10596-010-9194-2 · 2011 · External reference
Deep neural networks predicting oil movement in a development unit
10.1016/j.petrol.2019.106513 · 2020 · External reference
10.2118/191593-18rptc-ms
10.2118/191593-18rptc-ms · External reference
A critical review of the use of pseudorelative permeabilities for upscaling
10.2118/35491-pa · 1997 · External reference
Upscaling: a review
10.1002/fld.267 · 2002 · External reference
Multi-stage upscaling: selection of suitable methods
2005 · External reference
Use of machine learning and data analytics to increase drilling efficiency for nearby wells
10.1016/j.jngse.2017.02.019 · 2017 · External reference
Application of machine learning to accidents detection at directional drilling
2019 · External reference
Data-driven model for the identification of the rock type at a drilling bit
10.1016/j.petrol.2019.03.041 · 2019 · External reference
10.15530/urtec-2017-2669988
10.15530/urtec-2017-2669988 · External reference
10.2118/174062-ms
10.2118/174062-ms · External reference
Elements of hydraulic fracturing
2013 · External reference
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Gradient boosting to boost the efficiency of hydraulic fracturing
10.1007/s13202-019-0636-7 · 2019 · External reference
Advanced analytics of self-colmatation in terrigenous oil reservoirs
10.1016/j.petrol.2019.106306 · 2019 · External reference
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Status of data-driven methods and their applications in oil and gas industry introduction to data driven methods
2018 · External reference
Application of artificial intelligence techniques in drilling system design and operations: a state of the art review and future research pathways
2016 · External reference
Application of artificial intelligence techniques in the petroleum industry: a review
10.1007/s10462-018-9612-8 · 2019 · External reference
What artificial intelligence can and can’t do right now
2016 · External reference
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2016 · External reference
Machine learning in oil and gas; a SWOT analysis approach
10.1016/j.petrol.2019.01.113 · 2019 · External reference
Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda
2019 · External reference
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2017 · External reference
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2019 · External reference
The end of petroleum engineering as we know it
2019 · External reference
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Should I interfere? AI-assistants’ interaction with knowledge workers: a case study in the oil & gas industry
2019 · External reference
Strategy for and with AI
2019 · External reference
10.3934/dcdsb.2008.10.925
10.3934/dcdsb.2008.10.925 · External reference
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10.1109/cvpr.2017.658
10.1109/cvpr.2017.658 · External reference
The era of open innovation
2003 · External reference
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2016 · External reference
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2020 · External reference
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What we’ve learned: Managing invention and innovation
1988 · External reference
Five myths about digital transformation
2017 · External reference
Building the AI-Powered organization
2019 · External reference
Artificial intelligence for sustainability: challenges, opportunities, and a research agenda
2020 · External reference
