Proceedings of the Workshop on Data Mining for Oil and Gas
Alipio Jorge, German Larrazabal, Pablo Guillen, Rui L. Lopes

TL;DR
This paper discusses the potential and challenges of applying data mining, machine learning, and data analytics throughout the oil and gas industry lifecycle to improve efficiency and decision-making.
Contribution
It highlights the largely unexplored opportunities for data mining in the digital oil-field and emphasizes the need for knowledge sharing among data scientists in this domain.
Findings
Data mining supports various phases of oil and gas lifecycle.
Challenges include managing data growth and integrating intelligence tools.
The workshop fosters collaboration among researchers in this field.
Abstract
The process of exploring and exploiting Oil and Gas (O&G) generates a lot of data that can bring more efficiency to the industry. The opportunities for using data mining techniques in the "digital oil-field" remain largely unexplored or uncharted. With the high rate of data expansion, companies are scrambling to develop ways to develop near-real-time predictive analytics, data mining and machine learning capabilities, and are expanding their data storage infrastructure and resources. With these new goals, come the challenges of managing data growth, integrating intelligence tools, and analyzing the data to glean useful insights. Oil and Gas companies need data solutions to economically extract value from very large volumes of a wide variety of data generated from exploration, well drilling and production devices and sensors. Data mining for oil and gas industry throughout the…
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