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IPR Modeling for Coning Wells
Norwegian University of Science and Technology, Faculty of Engineering Science and Technology, Department of Petroleum Engineering and Applied Geophysics.
2012 (English)MasteroppgaveStudent thesis
Abstract [en]

In this study, based on the work of Vogel, we generated the Inflow Performance Relationship (IPR) curves and its dimensionless form at any stage of depletion using black-oil simulator results. The IPR was generated for horizontal well with gas and water coning problems, producing from thin oil reservoir sandwiched between gas cap and aquifer. Two empirical IPR equations adopted from SPE paper by Whitson was also presented here. The first empirical relationship was developed based on simulated data for each reservoir pressure (stage of depletion) while the second relationship was developed based on all generated data. A fully implicit black-oil Cartesian model with total grid number of 1480 and 150 ft total thickness was used as reservoir model. The horizontal well extends through the full length of reservoir in y-direction with only one grid number along the horizontal section which makes the model a 2D problem. Sensor reservoir simulator and Pipe-It software were utilized to generate the IPR data. This work also includes a sensitivity study to understand the effect of several parameters to gas and water coning behavior, well placement optimization, coning collapse study, and the effect of coning to maximum well production rate. In coning collapse study, a relationship between flowing bottom-hole pressure and reservoir pressure when the cone collapse is provided in graphical form. This could be useful in field application where chocking the well to lower flowing bottom-hole pressure has become one alternative to reduce coning problems.

Place, publisher, year, edition, pages
Institutt for petroleumsteknologi og anvendt geofysikk , 2012. , 79 p.
Keyword [no]
ntnudaim:8256, MSG1 Petroleum Engineering, Reservoir Engineering
URN: urn:nbn:no:ntnu:diva-19518Local ID: ntnudaim:8256OAI: diva2:570796
Available from: 2012-11-20 Created: 2012-11-20

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