The use of logging-while-drilling (LWD) imaging tools in real-time decision
making and post-drilling analysis has become commonplace. However, image noise
and processing errors because of inherent measurement physics can propagate
errors and, thus, complicate interpretation of openhole log data and images.
For example, standard density images tend to amplify image noise from small
borehole irregularities. Among different borehole irregularities, spiraling is
known to occur more frequently with conventional rotary assemblies, steerable
motor assemblies, and rotary-steerable assemblies. When the cyclic-noise
amplitude from spiraling becomes large relative to the measurement of the
primary interest, it grossly affects the quality of the recorded formation bulk
density, photoelectric factor, and neutron porosity.
This paper shows novel methods to remove cyclic noise from
formation-evaluation (FE) images by applying frequency-domain filtering.
Although the initial attempt of fast Fourier transforms (Zhang et al. 2010)
illustrates the straightforward concept, it is seldom used because of
implementation issues requiring interactive filter design and intensive
operator intervention. Recently, a new method (Sugiura et al. 2011) has been
designed to improve the filtering process. Additionally, the new adaptive
filter designs automate the cyclic-noise-removal process from the FE images.
Standalone software has been developed to process the entire image logs from
one well, without human supervision. The software adaptively modifies the
filter behavior as borehole-oscillation noise characteristics change with
formation, drilling assemblies, hole inclination, and depth.
To examine its validity qualitatively, the algorithm is then applied to
various field data, not only on low-resolution density images but also on
higher-resolution density images. The new algorithm has been proved by bringing
considerable improvement to the image quality without any artificial
interruptions. The rugosity effect is reduced significantly, and the apparent
resolution of bed-boundary features is increased.
Furthermore, comparative field examples illustrate the improvement in
image-feature extraction. The method described here is critical, for example,
for small-fracture detection and more-precise definition of formation-property
contrasts indicative of bed boundaries in irregular boreholes. This new method
not only is effectively used for density images but also is applicable to any
other borehole images, such as resistivity and ultrasonic images.
© 2012. Society of Petroleum Engineers
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- Original manuscript received:
14 November 2011
- Meeting paper published:
16 November 2011
- Revised manuscript received:
15 February 2012
- Manuscript approved:
10 April 2012
- Published online:
18 June 2012
- Version of record:
7 August 2012