Digital transformation
To develop improved predictive models of complex real-world problems, one needs to pursue a balanced perspective. Ultimately, the physics we know needs to rely on data to unmask the physics that we do not yet know.
The market may be different from what we have previously experienced, but the path to a successful digital transformation is durable and the core principles of success have not changed.
Start-and-stop data management initiatives and a mishmash of partial solutions are no longer viable for managing the digital oil field. Data management should be transformed from a cost center to a cash-flow-generating function.
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With slopes, pies, points, and lines in your illustration arsenal, how do you choose the chart that best tells your data story? -
This column is intended to provide a starting point and a roadmap for professionals who want to learn data science and are struggling with the question, “Where do I start?”
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A recent datathon and the team that took home the grand prize help paint a picture of both the industry’s’ digital transformation and how oil and gas engineers are embracing it to navigate uncertain times.
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With much of the workforce cooped up at home for 8 months or so, tech skills are now a virtual necessity. Many of us have had to get increasingly tech-savvy in a hurry.
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Your career is yours to manage. Be approachable and be bold to be sure you are noticed virtually. Do not wait for the pandemic to pass.
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Analytics is a key untapped transformational lever that will drive a meaningful impact in the short-term and a core competitive advantage for companies in the long term.
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When as an engineer Sidd Gupta noticed the amount of time and money oil and gas companies spend looking for answers in data they already have, he saw it as an opportunity to create a startup company that can help reduce that.
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The industry worldwide has been swayed by the digitization wave. This column previews the multifaceted digital transformation of petroleum engineering from an intradisciplinary perspective, using specific examples from SPE’s seven technical disciplines.
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As current sources of competitive advantage erode and new ones emerge, perhaps the only constant in a highly uncertain world is the criticality of good talent for success. Upskilling the workforce for analytics, therefore, needs to be a one of the top priorities as we sail unchartered waters.
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If you have used the random forest algorithm, then you already have used the Ensemble Machine Learning (EML) method, probably without realizing it. This article will explain, in very simple terms, the principle behind this relatively new ML paradigm.
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