Over-simplification of the hydraulic component of the field production gathering network can often introduce serious errors in the reservoir simulation forecast. One principal problem involves determining the number of wells required to achieve a desired production target rate. Neither the engineer nor the automated drilling queue logic in the reservoir simulator can easily determine the effects of increased surface backpressure on existing wells caused by the connection of new wells to the surface gathering network. The integrated simulation of the reservoirs and pipeline gathering network connecting wells to the 200 MMscfd (5635 E03 m3/d) Sexsmith, Alberta sour gas processing plant demonstrates an approach taken to provide answers to problems typical of gas production operations. The paper reviews alternate approaches pursued prior to committing to the integration of a reservoir simulator and a pipeline network model. A review of the history matching procedures conducted to "tune" the linked models to match historical production rates and pressure losses in the network are presented. Information which is "passed" between models is presented as well as suggested software improvements to the integration of the reservoir and pipeline network models. P. 291
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.
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