Quantifying Confidence in Horizon-Picking

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.


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 coherence (the same Sobel filter as in your digital camera software) independently computes first derivatives of the seismic amplitudes between neighboring traces along the X and Y directions and combines them to form a coherence-like image. Disorder, on the other hand, cascades second derivatives in the X, Y and time directions.

Coherence algorithms are designed to emphasize continuous reflectors disrupted by incoherent structural and stratigraphic edges. In contrast, the disorder algorithm is design to emphasize noise and considers edges to be signal.

Both noise estimates are computed along local reflector dip and are normalized by the energy of the data within the analysis window.


Figures 1b and 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 coherence and high disorder, suggesting that seismic data quality is generally low.

Such data quality impacts the continuity of time-structure maps.

In line AA' shown in figure 2a, the right part of the image corresponds to a smooth time-structure map and high values of coherence and low values of disorder (appearing as green in figures 1b and c) corresponding to a smoother part of the map in figure 1a.

In contrast, line CC' in figure 1c exhibits poor data quality at the target horizon that gives rise to lower coherence and higher disorder displayed as yellow and red in figures 1b and c, and also results in a less smooth time-structure map in figure 1a.

Interestingly, the horizon on the west side of line CC' (figure 1c), shows high coherence (in green) but medium disorder (in yellow). Note that while the horizon is picked as a (white) peak it is overlain by a higher coherence event that appears as a (black) trough. The coherence algorithm appears to measure the continuity of this higher amplitude neighboring reflector.

In this example, the disorder attribute represents data quality more accurately.


In summary, seismic attributes that are sensitive to chaotic features and noisy data, such as coherence and disorder, can be used to quantify horizon-picking confidence. Of the two attributes, disorder is relatively insensitive to faults and provides the more accurate result.

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.

Authors' note: 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.

(Editor's note: AAPG member Thang Ha is a master's student in geophysics at the University of Oklahoma; AAPG member Kurt Marfurt is his adviser there.)

Figure 2 - Three vertical slices through the seismic amplitude volume showing the yellow picks used to make the map in figure 1a. (a) In line AA' the horizon on the east (right) side is relatively continuous and easy to pick. (b) In line BB' the data quality is poor along the entire picked line. (c) In line CC' the left side of yellow horizon is also noisy, but corresponds to a high coherence (green) area in figure 1b. In this example, the coherence map is sensitive to the overlying, higher amplitude continuous (black) trough.

Figure 1- (a) Time-structure map of yellow horizon shown in figure 2 (below), and corresponding horizon slices through the (b) coherence and (c) Disorder volumes. Coherence is sensitive to structural and stratigraphic edges as well as noise. By design, disorder is insensitive to edges and only sensitive to chaotic noise.

Comments (0)

 

Geophysical Corner - Thang Ha

AAPG member Thang Ha is a master's student at the University of Oklahoma, Norman, Okla.

Geophysical Corner - Kurt Marfurt
AAPG member Kurt J. Marfurt is with the University of Oklahoma, Norman, Okla.

Geophysical Corner

The Geophysical Corner is a regular column in the EXPLORER that features geophysical case studies, techniques and application to the petroleum industry.

VIEW COLUMN ARCHIVES

Image Gallery

See Also: Book

Desktop /Portals/0/PackFlashItemImages/WebReady/book-m100-Tectonics-and-Sedimentation-Implications-for-Petroleum-Systems.jpg?width=50&h=50&mode=crop&anchor=middlecenter&quality=90amp;encoder=freeimage&progressive=true 4049 Book
Desktop /Portals/0/images/_site/AAPG-newlogo-vertical-morepadding.jpg?width=50&h=50&mode=crop&anchor=middlecenter&quality=90amp;encoder=freeimage&progressive=true 4375 Book

See Also: Bulletin Article

Estimation of the dimensions of fluvial geobodies from core data is a notoriously difficult problem in reservoir modeling. To try and improve such estimates and, hence, reduce uncertainty in geomodels, data on dunes, unit bars, cross-bar channels, and compound bars and their associated deposits are presented herein from the sand-bed braided South Saskatchewan River, Canada. These data are used to test models that relate the scale of the formative bed forms to the dimensions of the preserved deposits and, therefore, provide an insight as to how such deposits may be preserved over geologic time. The preservation of bed-form geometry is quantified by comparing the alluvial architecture above and below the maximum erosion depth of the modern channel deposits. This comparison shows that there is no significant difference in the mean set thickness of dune cross-strata above and below the basal erosion surface of the contemporary channel, thus suggesting that dimensional relationships between dune deposits and the formative bed-form dimensions are likely to be valid from both recent and older deposits.

The data show that estimates of mean bankfull flow depth derived from dune, unit bar, and cross-bar channel deposits are all very similar. Thus, the use of all these metrics together can provide a useful check that all components and scales of the alluvial architecture have been identified correctly when building reservoir models. The data also highlight several practical issues with identifying and applying data relating to cross-strata. For example, the deposits of unit bars were found to be severely truncated in length and width, with only approximately 10% of the mean bar-form length remaining, and thus making identification in section difficult. For similar reasons, the deposits of compound bars were found to be especially difficult to recognize, and hence, estimates of channel depth based on this method may be problematic. Where only core data are available (i.e., no outcrop data exist), formative flow depths are suggested to be best reconstructed using cross-strata formed by dunes. However, theoretical relationships between the distribution of set thicknesses and formative dune height are found to result in slight overestimates of the latter and, hence, mean bankfull flow depths derived from these measurements.

This article illustrates that the preservation of fluvial cross-strata and, thus, the paleohydraulic inferences that can be drawn from them, are a function of the ratio of the size and migration rate of bed forms and the time scale of aggradation and channel migration. These factors must thus be considered when deciding on appropriate length:thickness ratios for the purposes of object-based modeling in reservoir characterization.

Desktop /Portals/0/PackFlashItemImages/WebReady/deposits-of-the-sandy-braided-saskatchewan-river.jpg?width=50&h=50&mode=crop&anchor=middlecenter&quality=90amp;encoder=freeimage&progressive=true 3712 Bulletin Article

See Also: Energy Policy Blog

As the U.S. increases its production of crude oil, pressure continues to build to allow crude exports. U.S. Energy Information Administration (EIA) has been asked to study the economic impacts of potentially lifting the ban and those studies are expected to be released in the late 2014/early 2015 timeframe.
Desktop /Portals/0/PackFlashItemImages/WebReady/Update-on-Crude-Oil-Exports-2014-10-15-hero.jpg?width=50&h=50&mode=crop&anchor=middlecenter&quality=90amp;encoder=freeimage&progressive=true 12992 Energy Policy Blog

See Also: Online e Symposium

Projects in several shales will be discussed, including Marcellus, Eagle Ford, Haynesville, Fayetteville, Montney, and Barnett, as will several seismically-detectable drivers for success including lithofacies, stress, pre-existing fractures, and pore pressure.

Desktop /Portals/0/PackFlashItemImages/WebReady/oc-es-seismic-reservoir-characterization-of-us-shales.jpg?width=50&h=50&mode=crop&anchor=middlecenter&quality=90amp;encoder=freeimage&progressive=true 1477 Online e-Symposium