Claim OwnershipData Science and Machine Learning in Oil and Gas
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© Dr. R. A. Leo Elworth
Description
This podcast series was put together by data science intern Leo Elworth to spread knowledge on these hot topics to the broader community. As the buzz around data science and machine learning continues to grow, more and more people are developing a curiosity for these topics, as well as their applications to the specific field of oil and gas. Interviews with expert data scientists and geologists serve to highlight innovative problems and share entertaining anecdotes. Podcast editing assistance was provided by Selena Garza.
14 Episodes
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In part 2, we further discuss the role of the gig economy via the crowd in data science and machine learning, now and in the future.
Richard Copsey introduces the idea of looking "beyond the walls" for data science and machine learning solutions. New approaches like crowdsourcing can bring data science work in from a wide, external talent pool. We also discuss three specific oil and gas problems that were solved in part by the crowd.
In part 2, we get tips from Jenny for how to be a successful entrepreneur in the startup space of data science and machine learning. For instance, we cover the dos and don'ts of navigating a tech showcase as a startup.
Jenny Johnson discuss how to work with emerging technologies in data science and machine learning. In particular, she walks through working with startups, and more generally the startup ecosystem, to solve business pain points requiring new data science and machine learning solutions.
How does a machine learning skeptic turn into a machine learning advocate? In Part 2, Matt recounts his data science and machine learning journey from his initial introduction and skepticism to his acceptance and forward looking thoughts on where these methods will proceed from here in oil and gas.
As a senior geoscientist who has been involved in many data science and machine learning projects, Matt Morris discusses where these fields fit into the work of a geoscientist in oil and gas. He describes what problems are well suited for data science or machine learning and the qualities of the solutions that can make them more useful.
In part two, we find out what led Dilshad to pursue science and how his career took some unexpected turns along the path to becoming a data scientist.
Still new to being a data scientist, Dilshad tells us about his latest work on Geosteering and reveals how an afternoon at a loud cupcake cafe helped with a messy data problem.
In the second half of Ingrid Tobar's interview, she shares with us her love for Environmental Science and how that evolved into working in the Oil and Gas industry.
Data Scientist Ingrid Tobar walks us through one of her most interesting projects, Rapid Basin Evaluation. She discusses this project for shortening the timeline for a basin evaluation and how the team was able to accomplish that goal.
In the second half, we ask Alex about his journey into data science and his recommendations for someone interested in adding data science to their career path.
Alex Bayeh walks us through his current projects, highlighting a project called the Tops Propagation Tool. He shares with us how the project began, their approach, and what examining brain waves from a zebrafish has to do with Well Logs.
After discussing the topic of data science and machine learning in part 1, we dig deeper into the oil and gas industry in part 2 of our interview. In part 2, Data Engineering Manager Vincent LeMoine gives his thoughts on how data science and machine learning can play a role in oil and gas specific problems, as well as some ideas on their place in the industry as a whole.
Data Engineering Manager Vincent LeMoine gives a fantastic introduction to the high-level topics of data science, machine learning, and oil and gas. He shares with us his take on what data science is and what skill sets are required to work in this area. After discussing the topic of data science and machine learning, we dig deeper into the oil and gas industry in part 2 of our interview.
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