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Data Science Applications in Oil and Gas Industry

Data Science Applications in Oil and Gas Industry

MACSPro'2019 - Modeling and Analysis of Complex Systems and Processes, Vienna
21 - 23 March 2019

Dr. Fedor Krasnov

Conference website http://macspro.club/

Website https://exactpro.com/
Linkedin https://www.linkedin.com/company/exactpro-systems-llc
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Exactpro

March 21, 2019
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  1. Digital platforms are not just a matter of “fashion” for

    companies but also a reflection of deep changes in the economy. 2 49 %  2% Non tech brands
  2. STC operates on services market 4 Global growth Global recession

    Shrinking resource base Alternative energy Degree of influence The demand for the services of STC Demand growth factors Factors of falling demand Rising oil prices Reduction of investment programs Degree of influence Falling oil prices Production decline
  3. The balance of supply and demand for R&D 5 STC

    Academics HQ License renewal Suggestions to improve oil recovery Problems with an oilfield Subsidiaries Innovation institutes
  4. Three principles of digital platform for STC • DATA AUTONOMY

    • ALGORITHMS STORE • «EVERYTHING IS A SPREADSHEET» 7
  5. The principle of data autonomy 8 • Data sources are

    multi-variant • Data is quasi stationary • Calculations demand a data coherency Cross-functional calculations 30+ different data sources Backward transparency
  6. ERA:GRAD embedded in Scientific Ecosystem 11 It does not matter

    who works as an ERA:GRAD agent: an employee, a student performing laboratory work, a developer of a new algorithm or an Infobot engaged in data quality control. Research organizations Software development companies Academic departments Universities Scientific Engineering in STC Research labs Innovation institutions Research foundations Service company Ministry of science Scientific conferences Professional community Oilfield service companies Oil companies Skolkovo, Rosatom Rusnano Russian Foundation for Basic Research, Russian Science Foundation SPE, SEG
  7. THE ADDITIVE NATURE OF RESEARCH Using results (“outputs”) of one

    study as inputs for another study is a key issue to build continuous processes and reduce multiple repetitions of the same actions (to avoid the “every time from scratch” situation). 13
  8. AUTOMATION OF ROUTINE INTELLIGENT OPERATIONS The routine operations identification process

    cannot be carried out “from the top downwards” because any regulation built on the basis of production of corporate process hypotheses is only a simplified model that replaces the routine operations automation tasks with the own relevance declaration tasks. The basis for the routine intellectual operations automation effort is the systemic understanding of the essence of research activities and the development of best practices in direct dialogue with researchers (users). 14 How we do it: Common mistake:
  9. THE HYPOTHESIS OF THE ROLL OUT In the eyes of

    researchers, there is a competition between commercial software products and in-house systems. So it is impossible to neglect the laws of competition when planning a digital platform roll out. Substitution in the implementation should be understood as a two-way displacement of expensive and underutilized options and simple operations from commercial products into the company’s own software systems. 15 Common mistake: How we do it:
  10. THE HYPOTHESIS OF MULTI-VARIANT DATA ON RESEARCH SUBJECT A digital

    platform should provide the ability to track the complete history of data from the moment of their acquisition and version through the use of algorithms, to the final result. 16
  11. THE HYPOTHESIS OF ONTOLOGY Any new algorithm must find its

    place in the existing structure and become its integral part. 17
  12. THE HYPOTHESIS OF TECHNOLOGIES From a strategic viewpoint the choice

    of a particular technology and “religious worship” to its advantages and disadvantages compared to other technologies is not so important. 18