A Comparison of Forecasting Methods for Gas Production

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractAs the demand for natural gas has increased, so has the need for accurately forecasting reservoir deliverability. Some common methods for forecasting include (a) production decline analysis; (b) a tank type reservoir model linked to a surface network model; and, (c) a reservoir simulator linked to a surface network model. The selection of the most appropriate method should consider the economic risk associated with the forecast and the availability of pertinent data necessary for the method being applied. In reality, other considerations such as time constraints, availability of certain in-house models, or the familiarity the engineer may have with one or more forecasting methods may be the governing factor in deciding which method might be applied. The paper compares the production forecasts generated for the three forecasting methods referenced evaluating such parameters as reservoir permeability and geometry and the effect of changing backpressure and well spacing. An assessment of the impact of these parameters and the relative magnitude difference of the forecast are presented to help provide guidelines for selecting a method, and appreciating the accuracy of methods in certain circumstances.This study found that production decline analysis generates the most conservative forecast of the three methods unless information is available to characterize the transient period in advance of the depletion decline. Production decline analysis, by virtue of its inherent assumptions, is unable to accurately predict the changes in volumes that result from changing surface operating conditions. A tank reservoir model linked to a surface network model is a good tool for field optimization, as long as the reservoirs are homogeneous and have good permeability. However, reservoir geometry can severely influence the interpreted results from such models, leading to erroneous conclusions. A reservoir simulator linked with a surface network model is the preferred choice for forecasting and optimizing the system for heterogeneous and/or low permeability reservoirs as long as sufficient reservoir data are available.

M2P2 I: Maser Monitoring Parkes Program data description and Stokes-I OH maser variability

“…High-mass ( 8 M ) star-forming regions (HMSFRs) tend to host main-line OH masers at 1 665 and 1 667 MHz, though as shown in this and previous works (e.g. Caswell 1999) they can also host satellite-line masers at 1 612 and 1 720 MHz.…”

GNOMES II: Analysis of the Galactic diffuse molecular ISM in all four ground state hydroxyl transitions using Amoeba

“…Both features in the sum rule residuals towards G344.43+0.05 and those at and km s towards G353.41-0.30 are due primarily to features seen in the 1612 MHz line and resemble the profile of a ‘double-horned’ maser (see e.g. Figure 2 in Caswell 1999, for representative examples). These double-horn masers arise in evolved stellar envelopes (e.g.…”

Development of a program to gather and process data from oil and gas fields

“…Depending on the situation one method may be better than another. (13) Predicting the future production of a well is fundamental to planning future operations and estimating future income. An accurate estimate of future production will help the operator determine the most profitable method in which to produce the well.…”