A Regression Approach to Estimating Gas In Place for Gas Fields
- R.H. Rossen (Exxon Production Research Co.)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- October 1975
- Document Type
- Journal Paper
- 1,283 - 1,289
- 1975. Society of Petroleum Engineers
- 0 in the last 30 days
- 217 since 2007
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Extrapolating the early portion of a plot of p/Z vs volume of gas produced is not always adequate for reservoirs characterized by abnormal pressure or substantial aquifer support. The procedure outlined in this paper applies nonlinear regression analysis to observed pressure and production history to obtain reliable estimates of gas in place and other reservoir properties. properties. Introduction
Estimation of the volume of gas in place (GEP) plays an important role in the evaluation and analysis of gas reservoirs. Soon after the discovery of a new gas field, a reliable estimate of the volume of gas available for production is needed for planning long-range contracts and production is needed for planning long-range contracts and commitments to supply gas users. In addition, knowledge of the GIP, as well as other reservoir properties, is necessary for scheduling well drilling, compression installation, and other investments that will maximize profit over the life of a field. profit over the life of a field. The principal methods of predicting GIP are the volumetric method and the material-balance method. The volumetric method requires geologic data defining the physical limits of the reservoir and core or log data physical limits of the reservoir and core or log data describing the distribution of fluids within the reservoir rock. These data can be quite sketchy, especially in the early history of a field, and the estimate of GIP derived from them can be in error by as much as a factor of 2. The material-balance method uses pressure data in the early production period to estimate GIP. Under ideal conditions, a plot of p/Z vs gas production will be a straight line that can be extrapolated to p=0 to determine GIP, as illustrated in Fig. 1. There are, however, several phenomena that prevent the data falling on a straight line. phenomena that prevent the data falling on a straight line. Common examples are illustrated in Figs. 2 through 4.
First, if the gas reservoir is in contact with a sizable aquifer, reduction of the reservoir pressure may be accompanied by influx of water and, consequently, the rate of pressure decline may decrease, resulting in a leveling off of the p/Z curve. The rate of water influx and, thus, the shape of the p/Z curve, depend on the physical properties of the aquifer. In addition, extrapolation of p/Z to properties of the aquifer. In addition, extrapolation of p/Z to zero is not valid since the field will be depleted and wells will be watered out at some positive reservoir pressure. Second, if the reservoir initially has abnormally high formation compressibility, as observed in high-pressure reservoirs, the rate of pressure decline might increase with time because the compaction of the reservoir will provide pressure support at the higher pressure level. provide pressure support at the higher pressure level. Finally, if the reservoir gas is near its dew point, retrograde condensation may occur; and because the liquid density is higher than the gas density, the pressure decline may accelerate. In such instances, extrapolation of p/Z data can give erroneous results, sometimes in error by more than 100 percent.
The volumetric method and the material-balance method are the two most prevalent procedures for estimating GIP, but there is no guarantee that the estimate generated using either procedure will be even close to the correct value. A more reliable method is needed for determining the volume of gas existing in real reservoirs that may be influenced by one or more of the physical phenomena mentioned above and that may exhibit highly phenomena mentioned above and that may exhibit highly nonlinear p/Z behavior. Fortunately, the effects of these factors on the reservoir pressure can be estimated quantitatively and can be included in a mathematical model of the system.
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