Using Analytics to Improve Production
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Tuesday, September 23, 2014
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Using analytics to transform the way we drill and produce more shale plays - Are we doing the right things and are we doing them the right way?
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Fred Kunzinger
Noah Consulting
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Talk Description
The challenges of drilling more wells to meet growing production targets in an unconventional drilling factory model, decreasing environmental footprints and bridging the Big Crew Change’s experience turnover, means that what is good today, may not be good enough for tomorrow.
While the assembly line was institutionalized by Henry Ford a little more than 100 years ago, it is just now making its mark on the oil and gas industry. Historically, much of the data required to optimize the assembly line in shale production ? dates, the time gaps between them, and the causes of those gaps ? has been neglected or not treated as important in larger offshore projects that simply do not have the same challenges. Shorter production cycles are not a priority for offshore; but, in shale, time to market is a real issue. Shale wells tend to lose 2/3 of their production in the first 2 years vs. offshore wells, which routinely produce for 30 years. For example, by understanding the time it takes between the well having been completed and being put online, then reducing this time by 2 days per well, would result in 720 days a year, assuming 30 wells are drilled per month. This is a substantial amount of potential production time wasted. Accurately collecting this information and making it available for analytical use becomes the basis for optimization scenarios.
More timely analysis to the causes of NPT (or non-productive time) in drilling operations will give an operator greater insight to areas that need improvement and areas where this insight can produce real bottom line value.
You may have a remote drilling operations center to help you drill faster wells, but do you have a business process monitoring capability to help you create a faster time to value?
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Keith R Holdaway - Upstream Domain SAS Global Oil and Gas
Case study; Using data-driven models to optimize production
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Chris Lenzsch - Solutions Manager, Upstream Analytics EMC
Improving production with life-cycle based analytics
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Henk Poulisse - Mathematical Consultant Communicative Algebra
Production analytics based on dynamical symmetry
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