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http://dx.doi.org/10.7842/kigas.2013.17.2.59

A Study on Production Well Placement for a Gas Field using Artificial Neural Network  

Han, Dong-Kwon (Dept. of Energy and Resource, Dong-A University)
Kang, Il-Oh (Korea Gas Research & Development Division, KOGAS)
Kwon, Sun-Il (Dept. of Energy and Resource, Dong-A University)
Publication Information
Journal of the Korean Institute of Gas / v.17, no.2, 2013 , pp. 59-69 More about this Journal
Abstract
This study presents development of the ANN simulator for well placement of infill drilling in gas fields. The input data of the ANN simulator includes the production time, well location, all inter well distances, boundary inter well distance, infill well position, productivity potential, functional links, reservoir pressure. The output data includes the bottomhole pressure in addition to the production rate. Thus, it is possible to calculate the productivity and bottomhole pressure during production period simultaneously, and it is expected that this model could replace conventional simulators. Training for the 20 well placement scenarios was conducted. As a result, it was found that accuracy of ANN simulator was high as the coefficient of correlation for production rate was 0.99 and the bottomhole pressure 0.98 respectively. From the resultes, the validity of the ANN simulator has been verified. The term, which could produce Maximum Daily Quantity (MDQ) at the gas field and the productivity according to the well location was analyzed. As a result, the MDQ could be maintained for a short time in scenario C-1, which has the three infill wells nearby aquifer boundary, and a long time in scenario A-1. In conclusion, it was found that scenario A maintained the MDQ up to 21% more than those of scenarios B and C which include parameters that might affect the productivity. Thus, the production rate can be maximized by selecting the location of production wells in comprehensive consideration of parameters that may affect the productivity. Also, because the developed ANN simulator could calculate both production rate and bottomhole pressure, respectively, it could be used as the forward simulator in a various inverse model.
Keywords
enhanced oil recovery; ANN; well placement; reservoir simulator; bottomhole pressure; MDQ;
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