BEGIN:VCALENDAR VERSION:2.0 PRODID:-//swoogo.com//NONSGML kigkonsult.se iCalcreator 2.27.21// CALSCALE:GREGORIAN BEGIN:VEVENT UID:0fdaea9cb96bfc4ac719fddc7db9c1fe101d17cf@swoogo.com DTSTAMP:20240329T003606Z DESCRIPTION:Talk to any oil and gas executive and you may be privy to a hid den concern: that the industry is playing catch-up in the race to apply ad vanced data science — including machine learning and artificial intelligen ce (AI) — to its most critical problems. In an industry that frequently op erates at the cutting edge of science and engineering it is perhaps surpri sing that there are so many opportunities still to be realized from harnes sing data-driven analytics. Yet unlike other industries\, oil and gas face s challenges ranging from the enormous complexity of drilling operations a nd the high cost of failure to the difficulty of obtaining the quantity an d quality of data required to create machine learning algorithms. In this presentation\, we will discuss some of the obstacles to effective use of d ata science and how oil and gas companies are overcoming them. Specificall y\, we will delve into four elements that in our experience must be in pla ce in order to reap the benefits of digital technologies: 1) accurate prob lem formulation\; 2) data readiness\; 3) expertise availability\; and 4) o rganizational enablement. For each element we will present real-life case examples of what steps different companies have taken to optimize them. Fo r many executives\, determining where and how to launch an effective data science initiative can be daunting. In an industry as complex as oil and g as\, it makes sense to think in terms of smaller short-term initiatives th at can then be broadened into more ambitious longer-term efforts. We will end our presentation with a discussion of how to structure and manage shor t-term projects while laying the groundwork for longer-term initiatives th at\, while riskier\, are likely to yield broader and more lasting benefits .\n DTSTART:20201111T193000Z DTEND:20201111T200000Z LAST-MODIFIED:20240329T003606Z LOCATION: SEQUENCE:0 STATUS:CONFIRMED SUMMARY:Industrial Digital Success Factors: Lessons for the Upstream Indust ry #Analytics\, #Best Practices TRANSP:OPAQUE X-ALT-DESC;FMTTYPE=text/html:
Talk to any oil and gas executive and you m ay be privy to a hidden concern: that the industry is playing catch-up in the race to apply advanced data science — including machine learning and a rtificial intelligence (AI) — to its most critical problems. In an industr y that frequently operates at the cutting edge of science and engineering it is perhaps surprising that there are so many opportunities still to be realized from harnessing data-driven analytics. Yet unlike other industrie s\, oil and gas faces challenges ranging from the enormous complexity of d rilling operations and the high cost of failure to the difficulty of obtai ning the quantity and quality of data required to create machine learning algorithms. In this presentation\, we will discuss some of the obstacles t o effective use of data science and how oil and gas companies are overcomi ng them. Specifically\, we will delve into four elements that in our exper ience must be in place in order to reap the benefits of digital technologi es: 1) accurate problem formulation\; 2) data readiness\; 3) expertise ava ilability\; and 4) organizational enablement. For each element we will pre sent real-life case examples of what steps different companies have taken to optimize them. For many executives\, determining where and how to launc h an effective data science initiative can be daunting. In an industry as complex as oil and gas\, it makes sense to think in terms of smaller short -term initiatives that can then be broadened into more ambitious longer-te rm efforts. We will end our presentation with a discussion of how to struc ture and manage short-term projects while laying the groundwork for longer -term initiatives that\, while riskier\, are likely to yield broader and m ore lasting benefits.
BEGIN:VALARM ACTION:DISPLAY DESCRIPTION:Talk to any oil and gas executive and you may be privy to a hid den concern: that the industry is playing catch-up in the race to apply ad vanced data science — including machine learning and artificial intelligen ce (AI) — to its most critical problems. In an industry that frequently op erates at the cutting edge of science and engineering it is perhaps surpri sing that there are so many opportunities still to be realized from harnes sing data-driven analytics. Yet unlike other industries\, oil and gas face s challenges ranging from the enormous complexity of drilling operations a nd the high cost of failure to the difficulty of obtaining the quantity an d quality of data required to create machine learning algorithms. In this presentation\, we will discuss some of the obstacles to effective use of d ata science and how oil and gas companies are overcoming them. Specificall y\, we will delve into four elements that in our experience must be in pla ce in order to reap the benefits of digital technologies: 1) accurate prob lem formulation\; 2) data readiness\; 3) expertise availability\; and 4) o rganizational enablement. For each element we will present real-life case examples of what steps different companies have taken to optimize them. Fo r many executives\, determining where and how to launch an effective data science initiative can be daunting. In an industry as complex as oil and g as\, it makes sense to think in terms of smaller short-term initiatives th at can then be broadened into more ambitious longer-term efforts. We will end our presentation with a discussion of how to structure and manage shor t-term projects while laying the groundwork for longer-term initiatives th at\, while riskier\, are likely to yield broader and more lasting benefits .\n TRIGGER:-PT15M END:VALARM END:VEVENT END:VCALENDAR