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SEMI-ANALYTICAL SOLUTION FOR TRANSIENT INFLOW PERFORMANCE RELATIONSHIP OF MULTIPLE-FRACTURE HORIZONTAL WELLS (MFHWS) IN TIGHT OIL RESERVOIRS

巻 10, 発行 3, 2019, pp. 231-243
DOI: 10.1615/SpecialTopicsRevPorousMedia.2019028234
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要約

Steady flow is usually assumed on traditional inflow performance relationship (IPR) curves, not as a function of time. However, tight oil reservoirs may take several years to reach stabilization due to extremely low permeability. As a result, the establishment of transient inflow performance relationship for multi-fracture horizontal wells (MFHWs) for complex production rules conditions is urgently needed. We created a semi-analytical flow model of MFHWs based on trilinear flow and superposition principle. The approach can be used to generate IPR curves with consideration of production history under production rules of constant flowing wellbore pressure, constant production rate, intermittent production, or even more complex production rules conditions. A remarkable advantage of the proposed method is that the model is no longer limited by constant flowing wellbore pressure or production rate conditions in comparison to the previous analytical model, so transient IPR curves can be generated based on production history matching. On a traditional IPR curve, the intercept is the average reservoir pressure. However, the transient IPR intercept is not representative of the average reservoir pressure but equals the well pressure if the well were shut-in for that duration, and the curves are affected by the production history. It is essential to plot transient IPR curves, and the change of production history should be considered in tight oil reservoirs. The methodology and results presented in this article are practical, and can be used directly for production forecasting and nodal analysis of MFHWs in tight oil reservoirs.

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によって引用された
  1. Chen Huiwei, Liu Shumei, Magomedov Ramazan Magomedovich, Davidyants Alla Andronikovna, Optimization of inflow performance relationship curves for an oil reservoir by genetic algorithm coupled with artificial neural-intelligence networks, Energy Reports, 7, 2021. Crossref

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