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The AAPG/Datapages Combined Publications Database

GCAGS Transactions

Abstract


Gulf Coast Association of Geological Societies Transactions
Vol. 53 (2003), Pages 19-24

Meta-Attributes: A New Concept for Reservoir Characterization and Previous HitSeismicNext Hit Anomaly Detection

Fred Aminzadeh

ABSTRACT

Fault cubes, salt bodies, sand channel volumes, and gas chimney data extracted from 3-D Previous HitseismicNext Hit are rapidly becoming valuable tools for exploration and field development. These Previous HitseismicNext Hit anomalies can be highlighted using a new technique that analyzes data with combinations of Previous HitseismicNext Hit attributes. Computer algorithms can be developed to search through data volumes looking for specific types of anomalous Previous HitseismicNext Hit data using carefully designed criteria or "meta-attributes." Meta-attributes are an aggregation of a number of Previous HitseismicNext Hit attributes combined with the interpreter's insight through a neural network to detect a particular feature. One of the main features of the meta-Previous HitattributeNext Hit concept is combining "artificial intelligence" of neural networks with the "natural intelligence" of an interpreter. This leads to a more comprehensive integration of geological, petrophysical and Previous HitseismicNext Hit data. Non-linear interrelationships between data as well as knowledge versus geologic features and reservoir properties are defined implicitly at the natural scale level. Meta-attributes extracted from multiple input Previous HitseismicNext Hit volumes and derived attributes are used to predict porosity lithology or fluid saturation, as well as for detecting faults, fractures, channel facies or salt bodies. The meta-Previous HitattributeTop approach is placed in the historical context, the technology is explained and examples are given.


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