Date of Graduation
2001
Document Type
Thesis
Degree Type
MS
Committee Chair
Shahab Mohagehgh
Abstract
Reservoir characterization in the Cotton Valley formation has always been a challenge for the Engineers, Geologist and Geophysists because of its heterogeneity as well as for the fact that well logs and reservoir characteristics are non-correlatable from well to well. This study introduces a new technique to characterize the cotton valley reservoir. Synthetic magnetic resonance imaging logs (MRI) provide information about reservoir characteristics such as effective porosity, fluid saturation and rock permeability more accurately than conventional wireline logs. The fuzzy C- mean analysis tool is utilized to group well logs together based on similarity criteria of the reservoir formation and fuzzy curve analysis is applied to identify the most influential logs for a well. This study also demonstrates that combining fuzzy techniques with artificial neural networks will help to model a complex, poorly defined, non-linear system.
Recommended Citation
Bhuiyan, Mofazzal Hossan, "An intelligent system's approach to reservoir characterization in Cotton Valley." (2001). Graduate Theses, Dissertations, and Problem Reports (ETD). 10597.
https://researchrepository.wvu.edu/etd/10597