Acceleration of 4IR Driven Digital Transformation Through Open Source: Methods and Parallel Industries Knowledge Reapplication in the Field
- Sammy Sarblund Haroon (AlphaX Decision Sciences) | Aruna Viswanathan (AlphaX Decision Sciences) | Sergey Alyamkin (AlphaX Decision Sciences) | Ramachandra Shenoy (AlphaX Decision Sciences)
- Document ID
- Offshore Technology Conference
- Offshore Technology Conference, 4-7 May, Houston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2020. Offshore Technology Conference
- industrial revolution, open source, artificial intelligence, data analytics, digital transformation
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- 62 since 2007
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In the age of open source, oil and gas companies that invest in licensing proprietary platforms to build artificial intelligence (AI) solutions will have to closely guard against obsolescence. While the mathematics behind AI has existed for decades, its rapid adoption in virtually all sectors has been powered by open source, where a global community of academicians and data scientists are continuously developing and improving the mathematics behind the predictive and prescriptive solutions of the future.
We posit that the existence of AI software development, which we describe as data driven analytical solutions providing predictive and prescriptive answers to questions, is solely due to the academia's focus on research and its pedagogical goals driving the dissemination of the research to the open source community to move the research to development. Indeed, all data science coursework is steeped in training on open source platforms and tools. When models do not depend on platforms, organizations can deploy flexible and agile software that has the ability to leverage better methods that come out in the future. Borrowing ideas from other industries we can see that just as Wordpress and its open source community of designers and developers revolutionized how websites are created, software that can quickly deploy the best available technology (and be mobile) while maintaining the security of the data is poised for user adoption and success where legacy systems and traditional dashboards fail.
While the mathematics of physics does not change our confidence in its accuracy, in comparison, the mathematics of an AI algorithm is an approximation. This mathematics is being continuously improved though rarely achieving 100% accuracy. Algorithms are built upon open source AI libraries that are continuously improving. Similarly, the methods and techniques for developing the AI algorithms in oil and gas are continuously improving through two main sources, academia and adjacent industries reapplication of AI methods. We provide examples of open source technologies already part and parcel of all data scientists' repertoire of development, who are currently working in oil and gas. We further showcase reapplication of solutions from other industries, such as, medical image data analysis to seismic data processing and subsurface characterization.
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"Wordpress," [Online]. Available: https://wordpress.com/.[Accessed 31 Jan 2020].
Schlumberger Corporation, "Petrel E&P Software Platform," [Online]. Available: https://www.software.s1b.com/products/petrel. [Accessed 31 January 2020].
S. Haroon, "ebusiness standards: pervasive or invasive to innovation?," 10 April 2014. [Online]. Available: http://www.pidx.org/wp-content/uploads/2014/05/3.-Baker-Hughes-Sammy-Haroon-US-Spring-Conference-2014.pdf.
B. Gates, "An Open Letter to Hobbyists," 3 February 1976. [Online]. Available: https://commons.wikimedia.org/wiki/File:Bill_Gates_Letter_to_Hobbyistsjpg.
C. Tozzi, "What the Hack? Tracing the Origins of Hacker Culture and the Hacker Ethic," 13 March 2017. [Online]. Available: https://www.channelfutures.com/open-source/what-the-hack-tracing-the-origins-of-hacker-culture-and-the-hacker-ethic.
R. Stallman, "Initial GNU Announcement," Free Software Foundation, 27 September 1983. [Online]. Available: https://www.gnu.org/gnu/initial-announcement.html.
Microsoft Corporate Blogs, "Microsoft completes GitHub acquisition", 26 October 2018. [Online]. Available: https://blogs.microsoft.com/blog/2018/10/26/microsoft-completes-github-acquisition/.
Red Hat PR, "IBM Closes Landmark Acquisition of Red Hat for $34 Billion; Defmes Open, Hybrid Cloud Future," 9 July 2019. [Online]. Available: https://www.redhat.com/en/about/press-releases/ibm-closes-landmark-acquisition-red-hat-34-billion-defines-open-hybrid-cloud-future.
"The State of the Octoverse," GitHub.com, [Online]. Available: https://octoverse.github.com.
F. I. w. I. AT, "The Power of Open Source Al," 22 May 2019. [Online]. Available: https://www.forbes.com/sites/insights-intelai/2019/05/22/the-power-of-open-source-ai/. [Accessed 31 Jan 2020].
G. Alregib, "OLIVES — Research," [Online]. Available: https://ghassanalregibinfo. [Accessed January 2020].
I. S. a. G. H. A. Krizhevsky, "ImageNet Classification with Deep Convolutional Neural Networks," 2012. [Online]. Available: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf.
TGS, "TGS Salt Identification Challenge," 2019. [Online]. Available: https://www.kaggle.com/c/tgs-salt-identification-challenge. [Accessed 31 Jan 2020].