Changes

Hydrocarbon microseepage data, whether soil gas or microbial or other geochemical measurements, are inherently noisy and require adequate sample density to distinguish between anomalous and background areas. Matthews<ref name=ch18r26 />) reviews the importance of sampling design and sampling density in target recognition. He states that undersampling is probably the major cause of ambiguity and interpretation failures involving surface geochemical studies.

Hydrocarbon microseepage data, whether soil gas or microbial or other geochemical measurements, are inherently noisy and require adequate sample density to distinguish between anomalous and background areas. Matthews<ref name=ch18r26 />) reviews the importance of sampling design and sampling density in target recognition. He states that undersampling is probably the major cause of ambiguity and interpretation failures involving surface geochemical studies.

[[file:surface-geochemical-exploration-for-petroleum_fig18-4.png|thumb|{{figure number|1}}. Copyright: Geo-Microbial Technologies, Inc.]]

 

[[file:surface-geochemical-exploration-for-petroleum_fig18-4.png|thumb|{{figure number|1}}Illustration of the value of geochemical grids over geochemical traverses for anomaly recognition. Copyright: Geo-Microbial Technologies, Inc.]]

Defining background values adequately is an essential part of anomaly recognition and delineation; Matthews<ref name=ch18r26 /> suggests that as many as 80% of the samples collected be obtained outside the area of interest. This is a good recommendation for reconnaissance and prospect evaluation surveys. However, for very small targets such as pinnacle reefs or channel sandstones, optimum results are obtained when numerous samples are collected in a closely spaced grid pattern, (100–160-m sample interval or less) over the feature of interest.<ref name=ch18r41>Schumacher, D., Hitzman, D., C., Tucker, J., Roundtree, B., 1997, Applying high-resolution surface geochemistry to assess reservoir compartmentalization and monitor hydrocarbon drainage, in Kruizenga, R., J., Downey, M., W., eds., [[Applications]] of Emerging Technologies: Unconventional Methods in Exploration for Oil and Gas V: Dallas, Texas, Southern Methodist Univ. Press, p. 309–322.</ref>

Defining background values adequately is an essential part of anomaly recognition and delineation; Matthews<ref name=ch18r26 /> suggests that as many as 80% of the samples collected be obtained outside the area of interest. This is a good recommendation for reconnaissance and prospect evaluation surveys. However, for very small targets such as pinnacle reefs or channel sandstones, optimum results are obtained when numerous samples are collected in a closely spaced grid pattern, (100–160-m sample interval or less) over the feature of interest.<ref name=ch18r41>Schumacher, D., Hitzman, D., C., Tucker, J., Roundtree, B., 1997, Applying high-resolution surface geochemistry to assess reservoir compartmentalization and monitor hydrocarbon drainage, in Kruizenga, R., J., Downey, M., W., eds., [[Applications]] of Emerging Technologies: Unconventional Methods in Exploration for Oil and Gas V: Dallas, Texas, Southern Methodist Univ. Press, p. 309–322.</ref>

==Example==

==Example==

The recognition of surface geochemical anomalies improves by increasing sample number and reducing sample spacing. [[:file:surface-geochemical-exploration-for-petroleum_fig18-4.png|Figure 1]], from Oklahoma, illustrates the value of geochemical grids over geochemical traverses for anomaly recognition.

The recognition of surface geochemical anomalies improves by increasing sample number and reducing sample spacing. [[:file:surface-geochemical-exploration-for-petroleum_fig18-4.png|Figure 1]], from Oklahoma, illustrates the value of geochemical grids over geochemical traverses for anomaly recognition.

==See also==

==See also==

* [[Designing surface geochemical surveys]]

* [[Designing surface geochemical surveys]]