ABSTRACT

The most common data that engineers can count on, specially in the case of mature fields is production data. Practical methods for production data analysis have come a long way since their introduction to the industry several decades ago and they all fall into two categories: Decline Curve Analysis (DCA) and Type Curve Matching (TCM). While Decline Curve Analysis is independent of any reservoir characteristics Type Curve Matching is a very subjective procedure.

State of the art in production data analysis can provide reasonable reservoir characteristics but it has two major shortcomings:

For reservoir characterization, the process requires bottom-hole or well-head pressure data in addition to rate data. Bottom-hole or well-head pressure data are not usually available in most of the mature fields.

A technique that would allow the integration of results from hundreds of individual wells into a cohesive field-wide or reservoir-wide analysis for business decision making is not part of today's production data analysis toolkit.

In order to overcome these shortcomings a new methodology is introduced in this paper that has three unique specifications:

  1. It does not require pressure data (bottom-hole or well-head);

  2. It integrates decline curve analysis, type curve matching, and numerical reservoir simulation (history matching) in order to iteratively converge to a near unique set of reservoir characteristics for each well;

  3. It uses fuzzy pattern recognition technology in order to achieve field-wide decisions from the findings of the analysis.

INTRODUCTION

Techniques for production data analysis (PDA) have improved significantly over the past several years. These techniques are used to provide information on reservoir permeability, fracture length, fracture conductivity, well drainage area, original-gas-in-place (OGIP), estimated ultimate recovery (EUR), and skin. Although there are many available methods identified in order to characterize the reservoir, there is no one clear method that always yields the most reliable answer.

Decline Curve Analysis (DCA) is a method to fit observed production rates of individual wells, group of wells, or reservoirs by a mathematical function in order to predict the performance of the future production by extrapolating the fitted decline function. Arps[1] introduced the decline curve analysis method in 1940s using mathematical equations. The method is a mathematical equation with no physical basis other than the equation shows a declining trend. The function introduced by Arps is characterized by three parameters; initial flow rate (qi), initial decline rate (Di), and decline exponent (b). When b=0, the decline is exponential. When b=1, the decline is harmonic. When 0<b<1, the decline is said to be hyperbolic. Arps' method is still being used because of its simplicity and since it is an empirical method, it does not need any reservoir or well parameters.

In early 1980s, Fetkvoich[2] introduced decline curve analysis by type curves. Type curve matching is essentially a graphical technique for visual matching of production data using pre-plotted curves on a log-log paper. Fetkovich used Arps decline curves along with type curves for transient radial symmetric flow of low-compressibility liquids at constant bottom-hole pressures. Fetkovich related Arps decline parameters to some reservoir engineering parameters for production against constant bottom-hole pressures. Fetkovich recommended harmonic decline curves to be used for gas wells.

In 1985, Carter[3] presented gas production rate results in type curve form for finite radial and linear flow system producing at a constant bottom-hole pressure. Carter used a variable ? identifying the magnitude of the pressure drawdown in gas wells. A curve with a ? value of 1 corresponds to b=0 in Fetkovich liquid decline curves and represents a liquid system curve with an exponential decline. Curves with ?=0.5 and 0.75 are for gas wells with an increasing magnitude of pressure drawdown.

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