Machine learning in geo-and environmental sciences: From small to large scale
In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
A survey on industry 4.0 for the oil and gas industry: upstream sector
The market volatility in the oil and gas (O&G) sector, the dwindling demand for oil due to the
impact of COVID-19, and the push for alternative greener energy are driving the need for …
impact of COVID-19, and the push for alternative greener energy are driving the need for …
[HTML][HTML] Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future
D Koroteev, Z Tekic - Energy and AI, 2021 - Elsevier
We analyze how artificial intelligence changes a significant part of the energy sector, the oil
and gas industry. We focus on the upstream segment as the most capital-intensive part of oil …
and gas industry. We focus on the upstream segment as the most capital-intensive part of oil …
[HTML][HTML] Enhancing construction safety: Machine learning-based classification of injury types
The construction industry is a hazardous industry with significant injuries and fatalities. Few
studies have used data-driven analysis to investigate injuries due to construction accidents …
studies have used data-driven analysis to investigate injuries due to construction accidents …
[HTML][HTML] Rock mass structural recognition from drill monitoring technology in underground mining using discontinuity index and machine learning techniques
A procedure to recognize individual discontinuities in rock mass from measurement while
drilling (MWD) technology is developed, using the binary pattern of structural rock …
drilling (MWD) technology is developed, using the binary pattern of structural rock …
FaultFace: Deep convolutional generative adversarial network (DCGAN) based ball-bearing failure detection method
Failure detection is employed in the industry to improve system performance and reduce
costs due to unexpected malfunction events. So, a good dataset of the system is desirable …
costs due to unexpected malfunction events. So, a good dataset of the system is desirable …
Use of machine learning and data analytics to detect downhole abnormalities while drilling horizontal wells, with real case study
A Alsaihati, S Elkatatny… - Journal of …, 2021 - asmedigitalcollection.asme.org
The standard torque and drag (T&D) modeling programs have been extensively used in the
oil and gas industry to predict and monitor the T&D forces. In the majority of cases, there has …
oil and gas industry to predict and monitor the T&D forces. In the majority of cases, there has …
Fault diagnosis based on feature clustering of time series data for loss and kick of drilling process
With the increase of drilling depth, complicated geological environments lead to a high risk
of loss and kick. Fault diagnosis plays an essential role in minimizing the financial and …
of loss and kick. Fault diagnosis plays an essential role in minimizing the financial and …
Identifying applications of machine learning and data analytics based approaches for optimization of upstream petroleum operations
Over the past few years, machine learning and data analytics have gained tremendous
attention as emerging trends in the oil and gas industry. The usage of modern tools and high …
attention as emerging trends in the oil and gas industry. The usage of modern tools and high …
[PDF][PDF] On increasing the productive time of drilling oil and gas wells using machine learning methods
AN Dmitrievsky, AG Sboev, NA Eremin… - Georesursy …, 2020 - academia.edu
The article is devoted to the development of a hybrid method for predicting and preventing
the development of troubles in the process of drilling wells based on machine learning …
the development of troubles in the process of drilling wells based on machine learning …