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Application of Artificial Neural Networks to Downhole Fluid AnalysisNormal access

Authors: P. Hegeman, C. Dong, N. Varotsis and V. Gaganis
Event name: IPTC 2007: International Petroleum Technology Conference
Session: A Changing World - Interdependence, Innovation and Implementation
Publication date: 04 December 2007
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 506Kb )
Price: € 20

Summary:
Reservoir characterization and asset management require
comprehensive information about formation fluids. Obtaining
this information at all stages of the exploration and
development cycle is essential for field planning and
operation. Traditionally, fluid information has been obtained
by capturing samples and then measuring the
pressure/volume/temperature (PVT) properties in a laboratory.
More recently, downhole fluid analysis (DFA) during
formation testing has provided real-time fluid information.
However, the extreme conditions of the downhole
environment limit the DFA tools to measuring just a small
subset of the fluid properties provided by a laboratory.
Nevertheless, these tools are valuable in predicting other PVT
properties from the measured data. These predictions can be
used in real time to optimize the sampling program, help
evaluate completion decisions, and understand flow assurance
issues.


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