Copyright 1998, Society of Petroleum Engineers Inc. This paper was prepared for presentation at the 1998 SPE Eastern Regional Meeting held in Pittsburgh, PA, 9–11 November 1998. This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435. Abstract MRI logs are well logs that use nuclear magnetic resonance to accurately measure free fluid, irreducible water (MBVI), and effective porosity (MPHI). Permeability is then calculated using a mathematical function that incorporates these measured properties. This paper describes the methodology developed to generate synthetic Magnetic Resonance Imaging logs using data obtained by conventional well logs such as SP, Gamma Ray, Caliper, and Resistivity. The synthetically generated logs are named Virtual Magnetic Imaging Logs or "VMRI" logs for short. This methodology incorporates artificial neural networks as its main tool. Virtual MRI logs for irreducible water saturation (MBVI) and effective porosity (MPHI) as well as permeability (MPERM) were generated for four wells. These wells are located in East Texas, Gulf of Mexico, Utah, and New Mexico. The results are quite encouraging. It is shown that MPHI, MBVI, and MPERM logs can be generated with a high degree of accuracy. For each case, 30% of the data were used to develop the neural model. The model was then tested on the remaining 70% of the data for verification. The models provide VMRI logs with approximately 80 to 97 percent accuracy using data not employed during model development. This methodology does not supersede the need for performing MRI logging in a field. It is designed to supplement the process by reducing the cost of using MRI logging on an entire field. The natural application of this process is in fields that have conventional logs for all of the wells but MRI logs for only a few wells. To generate the Virtual MRI logs for every well in a field, data from wells that have both conventional and MRI logs are used in the model development and verification. The model is then applied to the other wells in the field to generate the virtual MRI logs for these wells. Using this process, the operator can log a few strategically chosen wells using physical MRI tools and produce virtual MRI logs for the entire field. This will allow the development of an accurate representation of effective porosity, free fluid, irreducible water saturation, and permeability for the entire field. Introduction John M. Austin and Tom L. Faulkner 1 published an superb paper in August 1993 in "The American Oil & Gas Reporter" providing some valuable information about the Magnetic Resonance Imaging logging technique and its benefits to low resistivity reservoirs. The MRI log measures effective porosity - total porosity minus the clay bound porosity - as well as irreducible water saturation. The irreducible water SPE 51075 Virtual Magnetic Imaging Logs: Generation of Synthetic MRI Logs from Conventional Well Logs S. Mohaghegh, M. Richardson, S. Ameri, West Virginia University