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By Rafiq Islam, S.H. Moussavizadegan, Shabbir Mustafiz, Jamal H. Abou-Kassem

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Zatzman and Islam (2007a) investigated the possibilities about the true potential of a given resource in the process of collecting during actual production information about dynamic changes in reservoir conditions in situ. The idea would be to eliminate much of the guesswork built into current demand-based modeling of energy prices. In general, these models project costs well into the future to "take care" of the margin of error incorporated in the guesswork. It can be considered a shift from the demand-based to the supply-based modeling, from control exercised downstream over national production companies upstream to a profound challenge against such control.

33 RESERVOIR SIMULATOR-INPUT/OUTPUT Geological and geophysical modeling Rock studies: - Lithology - Depositional origin - Reservoir rock type Framework studies: - Structure Continuity - Gross thickness trends Reservoir characterization Production data H I Reservoir quality studies: - Quality profiles - Reservoir zonation - Net thickness trends Integration studies: - Pore volume -Tranmisibility Real system History-matching Geological framework Rock and fluid properties kr<¡ = k,0 (x) φ = φ(χ) k=k(x) km = k„(x) Sw=SJx) k,a = k,3(x) and x = (x,y,z).

B. Although the approximation to the second derivative by central difference operator increases accuracy because of a second order approximation, it still suffers from the truncation problem. c. As the spacing size reduces, the truncation error approaches to zero more rapidly. Therefore, a higher order approximation will eliminate the need of same number of measurements or discrete points. RESERVOIR SIMULATION BACKGROUND 11 It might maintain the same level of accuracy; however, less information at discrete points might be risky as well.

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Advanced Petroleum Reservoir Simulation (Wiley-Scrivener) by Rafiq Islam, S.H. Moussavizadegan, Shabbir Mustafiz, Jamal H. Abou-Kassem

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