Oil & Natural Gas Projects
Exploration and Production Technologies
Automatic Flaw Detection and Identification for Coiled Tubing
The purpose of this project is to develop an automatic detection algorithm to
correlate between signal data acquired during coiled tubing (CT) inspections
and physical flaws in coiled tubing samples. The automatic detection algorithm
will be integrated with a previously developed lifetime prediction model to
provide essentially real-time assessment of coiled tubing(CT) reliability.
Idaho National Laboratory (INL)
Idaho Falls, ID
University of Tulsa (TU)
Signal analysis was completed in FY2003. The project developed CT inspection
software. The project identified initial correlation between magnetic flux leakage
(MFL) signal features and manufactured flaws. Due to the signal analysis completion,
a redesign of the sensor head began in FY2005.
The redesign of the sensor head includes more circumferential sensors. This
will allow for better circumferential location of detected flaws in coiled tubing.
Current CT field inspection technology is relatively crude, consisting of rolling
friction wheels to monitor depth and limited systems that monitor diameter and
ovalit and MFL systems to identify defects but having little reliability in
identifying the type of defect present (cracks, corrosion, etc.) or any dimensional
information. CT drilling is operated in an extremely severe mechanical environment,
where large bending strains are combined with significant internal pressure.
This can cause diametrical growth, wall thinning, ovality, elongation, residual
stresses, and low-cycle fatigue cracks. Surface defects can shorten operation
life spans significantly.
The reliability of CT has been enhanced by extensive refinements to its manufacturing
process. CT strings are now making as many as 80 trips into boreholes, resulting
in a wide variety of service-induced defects. Additionally, there are often
long periods of inactivity (storage) during which time corrosion can enhance
the damage to the tube surfaces. This extended operational life of the tubing
can be partly attributed to laboratory experimentation and theoretical work
involving fatigue modeling. Sophisticated plasticity and fatigue-damage models
predict life for discrete sections along the entire string throughout its service
history. However, used CT tends to fail sooner than predicted, due to the presence
of defects incurred through mechanical damage and/or corrosion.
The project tasks break out as:
- Task 1, algorithmic developments (FY2005-2006). This entails processing a
CT MFL signal library and developing a flaw detection algorithm.
- Task 2, flaw identification engine (FY 2005). This task calls for developing
a flaw characterization algorithm and the initial release of an automatic flaw
detection, characterization, and acquisition software system.
- Task 3, evaluation (FY 2006). Here the researchers are to conduct blind validation
studies of flawed CT strings.
- Task 4, integration (FY 2006-2007). The project performers are to report results
from blind validation studies of flawed CT strings, develop a method to map
the state of the CT string for TU's lifetime prediction model, and integrate
a lifetime prediction model into data-acquisition/analysis system software.
- Task 5, project management (ongoing). This involves general project management,
e.g., monthly reports, annual reports, etc.
Data acquisition was accomplished in FY2002. Researchers acquired a data
system for inspection of CT and installed the INL-designed and -built interface
on commercially available inspection heads. They acquired inspection data
on virgin and flawed CT.
Current Status (July 2006)
The project was felt to contain patentable material, and no public information
will be released until a disposition on the patent can be made.
Project Start: March 27, 2002
Project End: May 9, 2006
Anticipated DOE Contribution: $238,000
Performer Contribution: $165,000 (63% of total)
NETL - Virginia Weyland (email@example.com or 918-699-2041)
INL - Charles Tolle (firstname.lastname@example.org or 208-526-1895)
INL - David Weinberg (email@example.com or 208-526-9822)