Reservoir characterization using intelligent seismic inversion

Integrating different types of data having different scales is the major challenge in reservoir characterization studies. Seismic data is among those different types of data, which is usually used by geoscientists for structural mapping of the subsurface and making interpretations of the reservoir's facies distribution. Yet, it has been a common aim of geoscientists to incorporate seismic data in high-resolution reservoir description through a process called seismic inversion.;In this study, an intelligent seismic inversion methodology is presented to achieve a desirable correlation between relatively low-frequency seismic signals, and the much higher frequency wireline-log data. Vertical seismic profile (VSP) is used as an intermediate step between the well logs and the surface seismic. Generalized regression neural network (GRNN) is used to build two correlation models between; (1) Surface seismic and VSP, (2) VSP and well logs both using synthetic seismic data, and real data taken from the Buffalo Valley Field.

Name thesis:degree_name

MS

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Petroleum and Natural Gas Engineering

Year dc:date.available

2005

Author dc:creator

• Artun, F. Emre

Contributors dc:contributor

• Shahab Mohaghegh.

• Petroleum engineering
• Geology
• Geophysics

Identifier

https://researchrepository.wvu.edu/etd/1620

OAI identifier oai:identifier

oai:researchrepository.wvu.edu:etd-2623