![](images/pdficon.gif)
GCQuantifying Confidence in Horizon-Picking*
Thang Ha¹ and Kurt J. Marfurt¹
Search and Discovery Article #41405 (2014).
Posted July 30, 2014
*Adapted from the Geophysical Corner column, prepared by the authors, in AAPG Explorer, July, 2014. Editor of Geophysical Corner is Satinder Chopra ([email protected]). Managing Editor of AAPG Explorer is Vern Stefanic.
¹University of Oklahoma, Norman, Oklahoma ([email protected])
Risk analysis is a crucial task in making drilling decisions and involves many factors, such as well logs, modeling results, production maps and interpretation quality. In his book on 3-D seismic interpretation, AAPG award-winning member Alistair Brown presents a workflow for the quantification of interpretation confidence. In this workflow, picks at 0, 1, and 2s indicated low, medium and high reflector quality. The interpreter then generates a confidence map from a coarse grid of picked lines.
In practice, such interpretation confidence maps are commonly excluded from risk analysis, simply because such quantification is not easy. In this article we demonstrate the quantification of horizon-picking confidence, using two seismic attributes that are sensitive to chaotic features – namely the Sobel-filter and disorder attributes.
♦General statement ♦Figures ♦Method ♦Example ♦Conclusion ♦Acknowledgments
♦General statement ♦Figures ♦Method ♦Example ♦Conclusion ♦Acknowledgments
♦General statement ♦Figures ♦Method ♦Example ♦Conclusion ♦Acknowledgments
♦General statement ♦Figures ♦Method ♦Example ♦Conclusion ♦Acknowledgments |
Our study area is located within the Halten Terrace, Norwegian North Sea. The area involves rift-related geologic structure, particularly a system of listric faults with a weak, soft layer of salt between basement and the upper sedimentary rocks. Figure 1a shows the time structure map of an interpreted horizon in the study area. Figure 2 shows representative vertical slices through the seismic amplitude data. While the horizon is relatively easy to pick in many areas, there are other areas where it is contaminated by steeply dipping migration alias artifacts. Autopickers work poorly on this horizon. In order to quantify the confidence of the horizon picking task, we calculate attributes that are sensitive to chaotic features, such as salt, karst and seismic noise. The general idea is that the noisier the data, the less confidence the interpreter will have in picking a horizon. The Sobel-filter implementation of
Figure 1b and Figure 1c show the results of the Sobel filter and disorder attributes extracted and smoothed along the same horizon in Figure 1a. Most of the horizon corresponds to relatively low In line AA' shown in Figure 2a, the right part of the image corresponds to a smooth time-structure map and high values of Interestingly, the horizon on the west side of line CC' (Figure 1c), shows high In summary, seismic attributes that are sensitive to chaotic features and noisy data, such as While both attributes are a measure of data quality along a picked reflector, they are not a measure of erroneously picking a more coherent neighboring reflector. Such interpreter error may be the biggest risk of all in the final map. Thanks to Debapriya Paul for providing geologic information and seismic interpretation data of the study area. AASPI and Petrel were used in this project. Seismic data were provided courtesy of CGG. |