Guest Editorial: Digital Drilling Disruption - Understand Downhole, Gain Control
- Gerhard Thonhauser (Montanuniversitaet Leoben / Chairman and Founder of TDE Group)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- November 2018
- Document Type
- Journal Paper
- 14 - 15
- 2018. Copyright is retained by the author. This document is distributed by SPE with the permission of the author. Contact the author for permission to use material from this document.
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The shift toward lower oil prices in the oil and gas industry has resulted in the necessity for more wells to be drilled at lower cost, either to sustain production or to generate new, low-cost reserves. With a continued shift in demand for hydrocarbon energy sources, this development is expected to continue with an improvement goal of 30% or more by 2025.
Because drilling costs are a significant contribution to the cost per barrel, the industry is being pressured into finding ways to lower cost per foot drilled. Numerous other industries have shown that they are able to constantly reduce cost per unit; the aviation sector has reduced the cost per mile and passenger, and the automotive industry has constantly reduced the cost per car produced.
Where do we stand today and what will the future possibly look like?
Thirty years ago, it was common to analyze morning reports, drill a few wells, learn from the results, and improve well time. The famous learn curve concept was introduced by Brett and Millheim (SPE 15362) when they studied entire sequences of wells drilled.
In the past decade, we have reached a level where we observe a rig operation for a few days to obtain sufficient data to improve the rig operation utilizing automated operations detection from time-based rig sensor data (Wallnoefer et al., SPE 99880). Morning reporting is now moving toward an automated process driven by machine sensor data.
With this information it is possible to study the consistency of rig operations (Andersen et al., SPE 119746) following, for example, Six Sigma concepts widely used in the manufacturing industry for decades. Many operators and contractors have learned to optimize rig operations toward well-defined performance targets.
Automatically generated and accurate performance data is used to generate a performance gap analysis, which highlights the improvement potential if a well was delivered targeting performance KPIs removing invisible lost time, eliminating problem time as lost time, and optimizing the process (e.g., drillings shoe to shoe).
Closing the gap, as shown in Fig. 1, would likely result in well cost improvement of more than 30%. The factors creating such a performance gap are documented as a result of a highly automated analysis but are based on surface measurements and observations.
Still, today we hardly see these performance gaps closed from a global perspective. After analyzing more than 25,000 wells, using time-based rig surface sensor data, it is apparent that there are several areas of improvement where surface data-driven analysis is not sufficient to close the gap.
Surface data may be able to generate improvement from 3 to 10%, if used as part of a properly exercised performance initiative focusing on operational consistency, which seems to be accepted in the industry. Evidence for accepting such a limit means “living with” comparably rudimentary rig sensor systems and rather bad surface data quality. In other words, we can accept working with imperfect hookload measurements because even a better measurement would still be a surface measurement.
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