We are at the cusp of Industrial Revolution 4.0, with the cyber-physical system establishing a synergy between computation and physical components. In lockstep with the technologies, as we move toward Well Construction 4.0 coupled with 360° well-engineering optimization for the ultimate delivery of partial to full automation, an important but complex component to be considered is tubulars in the well.
Tubulars are associated with the drilling system, which is complex and dynamic, with a ratio of length to diameter greater than 100,000. For comparison, for a human hair to have the same length-to-diameter ratio, it would have to be 60 ft long. So, reliability is important in such a highly oscillatory, uncertain, high-dimensionality framework. Tubular modeling should bridge deterministic models and data-informed models. This calls for data-informed, engineering-guided, reinforced reliability- or risk-supported models to describe the non-Gaussian drilling system more realistically.
Risk-based design is very important, especially for these complex systems, because a deterministic approach may not provide a strong underpinning to the reliability estimation of the downhole tools. We have to reduce the probability of failure. One of the areas that needs to be considered is a risk-based design for tubulars that formally identifies the elements and areas of risks by using probability of failure to capture uncertainties rather than incorporating safety factors in the deterministic calculations. Risk should be considered during the design phase, which will allow more freedom to make adjustments, and during the drilling phase. Doing so will also help improve the 360° well-engineering optimization and the additional computational components needed for the future cyber-physical system.
I have selected papers that are structured toward this approach with example problems and solutions.
This Month's Technical Papers
Recommended Additional Reading
SPE 188321 Intelligent Drilling System: Expanding the Envelope of Wired Drillpipe by O. Sehsah, Saudi Aramco, et al.
Robello Samuel, SPE, Technology Fellow, Halliburton
18 May 2018
Exponential thinking is called the “exponential surprise factor.” These underpinnings are observed on the tubular mechanics side also through data analytics, machine learning, artificial intelligence, and cognitive processes.
OTC Event Selects 10 Startups Worth Watching
The results are in. Here are this year’s “Most Promising” startups as decided by upstream investors and oil company innovation teams
Operator Executives Share Haynesville Development Strategies
Indigo Natural Resources, Aethon Energy, and Rockcliff Energy are among the most active operators in the revived Haynesville Shale of North Louisiana and East Texas. And most people outside of the region likely have never heard of them.
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