Global optimization of data quality checks on 2-D and 3-D networks of GPR cross-well tomographic data for automatic correction of unknown well deviations Page: 1 of 4
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Global optimization of data quality checks on 2-D and 3-D networks of GPR cross-well
tomographic data for automatic correction of unknown well deviations
Douglas S. Sassen*, and John E. Peterson, Earth Science Division, Lawrence Berkeley National Laboratory
logs can sometimes have significant errors.
Mislocation of the transmitter and receiver stations of GPR
cross-well tomography data sets can lead to serious
imaging artifacts if not accounted for prior to inversion.
Previously, problems with tomograms have been treated
manually prior to inversion. In large data sets and/or
networks of tomographic data sets, trial and error changes
to well geometries become increasingly difficult and
ineffective. Our approach is to use cross-well data quality
checks and a simplified model of borehole deviation with
particle swarm optimization (PSO) to automatically correct
for source and receiver locations prior to tomographic
inversion. We present a simple model of well deviation,
which is designed to minimize potential corruption of
actual data trends. We also provide quantitative quality
control measures based on minimizing correlations between
take-off angle and apparent velocity, and a quality check on
the continuity of velocity between adjacent wells. This
methodology is shown to be accurate and robust for simple
2-D synthetic test cases. Plus, we demonstrate the method
on actual field data where it is compared to deviation logs.
This study shows the promise for automatic correction of
well deviations in GPR tomographic data. Analysis of
synthetic data shows that very precise estimates of well
deviation can be made for small deviations, even in the
presence of static data errors. However, the analysis of the
synthetic data and the application of the method to a large
network of field data show that the technique is sensitive to
data errors varying between neighboring tomograms.
Significant errors related to poor time zero estimation, well
deviation or mislocation of the transmitter (TX) and
receiver (RX) stations can render even the most
sophisticated modeling and inversion routine useless.
Previous examples of methods for the analysis and
correction of data errors in geophysical tomography include
the works of Maurer and Green (1997), Squires et al.
(1992) and Peterson (2001). Here we follow the analysis
and techniques of Peterson (2001) for data quality control
and error correction. Through our data acquisition and
quality control procedures we have very accurate control on
the surface locations of wells, the travel distance of both
the transmitter and receiver within the boreholes, and the
change in apparent zero time. However, we often have
poor control on well deviations, either because of economic
constraints or the nature of the borehole itself prevented the
acquisition of well deviation logs. Also, well deviation
Problems with borehole deviations can be diagnosed prior
to inversion of travel-time tomography data sets by plotting
the apparent velocity of a straight ray connecting a
transmitter (TX) to a receiver (RX) against the take-off
angle of the ray (Figure 1).
Correct Well Geometry
-80 -60 -40 -20 0 20 40
Take-off angle (degrees)
Deviated ell Geometry
-80 -60 -40 -20 0 20 40
Take-off angle (degrees)
Figure. Scatter plots of the calculated velocity of a straight ray
between source and receiver pairs verses the take-off angle. The
QC scatter plots for the correct well geometry without static errors
(A), and a for a deviated well (B) show variations in correlation
between apparent velocity and take-off angle.
Issues with the time-zero pick or distances between wells
appear as symmetric smiles or frown in these QC plots.
Well deviation or dipping-strong anisotropy will result in
an asymmetric correlation between apparent velocity and
take-off angle (Figure 1-B). In addition, when a network of
interconnected GPR tomography data is available, one has
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Sassen, D. S. & Peterson, J. E. Global optimization of data quality checks on 2-D and 3-D networks of GPR cross-well tomographic data for automatic correction of unknown well deviations, article, March 15, 2010; Berkeley, California. (https://digital.library.unt.edu/ark:/67531/metadc1015623/m1/1/: accessed March 21, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.