State-Of-The-Art in Permeability Determination From Well Log Data: Part 2- Verifiable, Accurate Permeability Predictions, the Touch-Stone of All Models

This paper was selected for presentation by an SPE Program Committee following review o information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by th e 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. Permission to copy is restricted to an abstract of not more than 300 words. Illustrations may not be copied. The abstract should contain conspicuous acknowledgment of where and by whom the paper is presented. Write Librarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A. Telex, 163245 SPEUT. Abstract The ultimate test for any technique that bears the claim of perme- ability prediction from well log data, is accurate and verifiable prediction of permeability for wells from which only the well log data is available. So far all the available models and techniques have been tried on data that includes both well logs and the corresponding permeability values. This approach at best is nothing more than linear or nonlinear curve fitting. The objective of this paper is to test the capability of the most promising of these techniques in independent (where corresponding permeability values are not available or have not been used in development of the model) prediction of permeability in a heterogeneous formation. Since the empirical approaches for permeability prediction are mostly directed toward developing mathematical models from given data in particular formations, it has been shown that they lack the required generalization capability for the purposes of this study. These approaches have concentrated on modeling formation permeability as a function of porosity and irreducible water saturation. These models will be briefly discussed. The main focus of this paper will be on two techniques that show potentials in achieving the goal that was mentioned above. These techniques are "Multiple Regression" and "Virtual Measurements using Artificial Neural Networks." For the purposes of this study several wells from a heterogeneous formation in West Virginia were selected. Well log data and corresponding permeability values for these wells were available. In separate tests all data from an entire well were designated and put aside. The techniques were applied to the remaining data and a perme- ability model for the field was developed. The model was then applied to the well that was separated from the rest of the data earlier and the results were compared. This approach will test the generalization power of each technique. After all, this is the way that these techniques are used in the real life situations. The result will show that although Multiple Regression provides acceptable results for wells that were used during model develop- ment, (good curve fitting,) it lacks a consistent generalization capability, meaning that it does not perform as well with data it has not been exposed to (the data from well that has been put aside). On the other hand, Virtual Measurement technique provides a steady generalization power. This technique is able to perform the permeability prediction task even for the entire wells with no prior exposure to their permeability profile.