Date of Graduation

1999

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Petroleum and Natural Gas Engineering

Committee Chair

Shahab Mohaghegh

Committee Member

Samuel Ameri

Committee Member

Jim Ammer

Abstract

Magnetic Resonance Imaging (MRI) is used in logging to provide the log analyst an additional tool in establishing reservoir characteristics. Magnetic Resonance Imaging Logging (MRIL) provides information on the Free Fluid, Irreducible Fluid, Total Porosity, Effective Porosity, and Permeability that is far more accurate than Conventional Wireline Log interpretation, enabling better completion practices that will further enhance total recovery. What this feasibility study seeks to show is that by using Neural Networks, it is possible to generate synthetic MRI Logs using conventional Wireline Log data. This study also demonstrates that this practice is possible in different reservoirs having entirely different lithologies. This study further emphasizes that given the encouraging results achieved here, it is possible to expand this procedure to develop field-wide MRI Logging using Conventional Wireline logs.

Share

COinS