--> Abstract: The Organization of Exploration Data at the Basin, Play, and Prospect Level, by P. D. Carragher; #91004 (1991)
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The Organization of Exploration Data at the Basin, Previous HitPlayNext Hit, and Prospect Level

CARRAGHER, PETER D., Amoco Production Co., Houston, TX

Geologists and geophysicists have developed numerous classification schemes over the past 150 years. Examples of classifications include rock types, basins, hydrocarbon trap types, and sequence stratigraphy. A common theme is hierarchy, moving from a general classification, such as sandstone, to increasingly more specific ones, such as nonmarine, aeolian, and dune. The creation of large relational data bases has brought the disparate schemes together in a single computing environment.

Our scheme at Amoco begins with broad-based data on the world's basins, including general geology, exploration history, and drilling statistics. The more detailed Previous HitplayNext Hit level is defined in terms of source rock, reservoir, seal, and trap type, together with hydrocarbon resource data on any existing fields in the Previous HitplayNext Hit. Each of the major objects used in Previous HitplayNext Hit definition, such as source rock, has numerous hierarchical detailed attributes such as TOC and depositional environment. Commercial data bases are maintained alongside proprietary data. Statistical Previous HitanalysisNext Hit is routinely performed on the field size data at either the Previous HitplayNext Hit or basin level. Finally, prospects are linked to parent plays by using site specific data from surrounding seismic and well control.

The data bases store a portion of a corporation's knowledge about many areas. Current search capabilities provide powerful analogy, finding tools which help develop new plays. Artificial intelligence (A.I.) developments will help the explorer navigate the large hierarchical data base more effectively. A blend of A.I. interfaces to guide explorers, coupled with statistical Previous HitanalysisTop of both descriptive and numeric data, offers the promise of very powerful analogy-finding machines. The goal of this work is to improve exploration effectiveness and efficiency.

 

AAPG Search and Discovery Article #91004 © 1991 AAPG Annual Convention Dallas, Texas, April 7-10, 1991 (2009)