--> An Integrated Stratigraphic Framework from Modified Global Cycle Chart Nomenclature, by Chengjie Liu, John W. Snedden, and Tom D. Davies; #90052 (2006)

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An Integrated Stratigraphic Framework from Modified Global Cycle Chart Nomenclature

Chengjie Liu1, John W. Snedden2, and Tom D. Davies1
1 ExxonMobil Exploration Company, Houston, TX
2 ExxonMobil Upstream Research Company, Houston, TX

Use of a modified SEPM Global Cycle Charts (GCC) nomenclature at ExxonMobil indicates that the GCC can serve as a unified global framework for stratigraphic integration. There are two schools of thought in sequence stratigraphic nomenclature. One approach emphasizes sequence boundaries (SBs) because these surfaces are interpreted to bound packages of genetically related strata. The other emphasizes maximum flooding surfaces (MFS) because they are easily identified from well information and are readily correlated between wells. Once the age-diagnostic events are calibrated to the GCC, the following nomenclatural modification is proposed to get advantages while avoid pitfalls from both schools: 1. Identify the relevant cycles from the GCC interpreted to correspond to the chronostratigraphic interval under consideration (e.g., Chattian_2 or Ch2); 2. Identify corresponding SBs with the suffix “_sb” (Ch2_sb); 3. Identify corresponding MFS with the suffix “_mfs” (Ch2_mfs); 4. Name major age-diagnostic events as higher order FSs with ordinal implications for the depositional cycle in which they occur (Ch2_200fs); 5. Accommodate regional “abnormality” with additional affix (e.g., Ch2Z); 6. Use “-X” designation where chronostratigraphic constraint is insufficient (Ch2X).

The primary benefit of this practice is that it provides a methodology for subdivision of the entire stratigraphic column into a global standard that is easy to implement and update. In turn, such a stratigraphic standard enables unambiguous communication in the geoscience community, improves database consistency, promotes integration of all forms of data, and facilitates learning. Examples of this approach and application in several different basins are provided.