This paper illustrates a practical systematic approach to determine the reservoir flow characteristics and reserves for both conventional and unconventional gas wells. Currently, there is an industry assortment of production analysis methods ranging from exponential decline and typecurve matching to rate-pressure normalization techniques and detailed production history matching. Through real life cases studies it will be shown that it is possible that a simpler reservoir model, such as a single well completed in the center of a circular reservoir, could be used to represent far more complex reservoirs, and still provide some representative reservoir characterization, as well as accurate reserves analysis and production forecasting. As a result, it possible that engineers and the like can avoid some of the more labor intensive production data analysis (PDA) techniques, and use more a methodology similar in operation to traditional decline.

Case studies and experience presented in this paper will demonstrate that a simple approach of production analysis methods will allow for a) proper identification of flow regimes, b) reliable evaluation of drainage area and OGIP, and c) the prediction of future deliverability and depletion. Case studies will also show that up-scaled and aggregate reservoir properties can provide a real measure of gas well deliverability (therefore a simpler, time-efficient model analysis can be used). Data uncertainty, unconventional gas (i.e. coal bed methane, tight gas, shale gas), stimulation appraisal, and other factors will be discussed in the context of the case studies.


The most common reason for analyzing gas production data is to estimate reserves and future production of gas wells. In forecasting, a variety of methodologies exist ranging from simple decline to complex numerical simulation. In many instances, even pressure transient analysis (PTA) is used to form the basis of a forecast model. However, usually due to time limitations, only empirical methods such volumetrics, and conventional decline analysis are used. As a result, there is challenge is to get the most out of information embedded in production and flowing pressure data to provide improved mechanistic predictions of future gas well performance.

Again, sophisticated methods, such as numerical simulation, have been available for decades and may provide the answers that industry needs. Although their predictive capabilities are proven, the accessibility (i.e. ease of use) of these methods is the issue. Of course, if time permits, all other techniques should be used.


Early attempts to linearize and extrapolate production history were limited. Future production could be estimated if one assumed that the production trend remained linear and constant for the remaining life of the well (i.e. stable fluid properties, constant flowing bottom-hole pressure (BHP) etc.). The difficulty of applying this type of decline analysis for gas cases is that these assumptions are severely restrictive and are therefore frequently violated. Again, given business time constraints, the aforementioned methods are the norm for industry.

In order to address the deficiency of standard decline analysis, typecurve analysis has been developed over many years. Typecurves are plots of theoretical solutions to flow equations (usually constant flow rate, or constant BHP) and can be generated for any kind of reservoir model for which a solution describing the flow behavior is available. Typecurve analysis theoretically allows one to estimate gas-in-place and gas reserves at some abandonment condition, as well as flowing characteristics of individual wells (i.e. permeability and skin). A common set of decline typecurves are those presented by Fetkovich (1980). Although more reservoir information is learned using Fetkovitch typecurves, they were still limited by the assumption of constant BHP, and constant fluid properties. Carter (1985) offered improved accuracy by using a plotting function that included the changes in fluid properties with average pressure. These curves were still limited to the assumption of constant flowing pressure. Carter's approach was similar to the pseudo-time function introduced by Agarwal (1979) in which the focus was to account for pressure dependant fluid properties in the near wellbore region during a flow and buildup analysis. Fraim and Wattenbarger (1987) also introduced a pseudo-time function to transfer a gas system into a single phase liquid system.

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