Copyright 2006, Society of Petroleum Engineers This paper was prepared for presentation at the 2006 SPE Eastern Regional Meeting held in Canton, Ohio, U.S.A., 11–13 October 2006. 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 The U.S. Geological Survey estimates tight-gas sands and shales may contain up to 460 trillion cubic feet (Tcf) of gas in the U.S. alone - almost three times the amount of currently proven gas reserves - and that some 135 Tcf may be technically recoverable. Since natural fractures are the main source of permeability in gas shales, key to producing this vast resource is locating areas where natural fractures are abundant. By placing infill wells in such locations operators can significantly increase gas recovery. In most cases, in fields that produce from gas shales, monthly production rate is the only data that is available. This paper introduces a new strategy for estimation of major and minor natural fracture using production data. The framework includes the use of Geographic Information System as an environment to analyze the production data where the preliminary estimation of natural fracture trends is developed. Then by superimposing the results of the preliminary estimation on reservoir quality indices developed using a fuzzy pattern recognition technique, the uncertainty associated with the initial estimation is reduced. This technique is validated using a set of gas shale wells in Kentucky and West Virginia. INTRODUCTION In most cases, in fields that produce from gas shales, the monthly production rate is the only data that is available. Shale provides a significant amount of gas. The U.S. Geological Survey estimates tight-gas sands and shales may contain up to 460 trillion-cubic-feet (Tcf) of gas in the U.S. alone--almost three times the amount of currently proven gas reserves--and that some 135 Tcf may be technically recoverable. Shale production is tied to existence of a natural fracture system and not conventional geloagy, one well may come on at 10 3 mcf/d, while its neighbor may yield nothing 1 . The high-rate of production in shale reservoir is achieved by communicating the wellbore with the natural fractures, which are the trap and transport sytem for the reservoir fluids 2 . Identification of infill drilling locations has been challenging with mixed results. This is due to the random nature of the natural fractures in the shale. In this study, natural fractures of the shale are treated as a random variable. The most logical approach is to use an integrated approach of the probability theory and intelligent system. Also, there must be an understanding that a deterministic approach is almost impossible to develop, no matter how many resources are used to solve this problem. All we can do is to use the history of development to increase our probability of success. There will be hits and misses in our prediction. Our goal is to increase the probability of hits and decrease the probability of misses. Due to the nature of the intelligent systems’ approaches, our methodology gets better with time. This technique is validated using a set of gas shale wells in Kentucky and West Virginia (Figure 1). METHODOLOGY The new technique discussed in this article combines both the Geographic Information System (GIS) and Intelligent Production Data Analysis (IPDA). GIS provides an SPE 104554 Estimating major and Minor Natural Fracture pattern in Gas Shales Using Production Data Gaskari, R., Mohaghegh, S. D., West Virginia University Figure 1. Location of the wells that drilled prior to 2000.