Seventeenth SPE Improved Oil Recovery Symposium
April 24-28 2010
Renaissance Hotel, Tulsa, Oklahoma
SPE 129965
Application of the Multiwell Productivity Index-Based Method to Evaluate Interwell Connectivity
D. Kaviani, SPE, U. of Calgary; P.P. Valkó, SPE, Texas A&M U.; and J.L. Jensen, SPE, U. of Calgary
Numerous studies have concluded that connectivity is one of the most important factors controlling success of improved oil recovery processes. Interwell connectivity evaluation can help identify flow barriers and conduits and provide tools for reservoir management and production optimization. The multiwell productivity index (MPI)-based method provides the connectivity indices between well pairs based on injection/production data. By decoupling the effects of well locations, skin factors, injection rates, and the producers’ bottomhole pressures from the calculated connectivity, the heterogeneity matrix obtained by this method solely represents the heterogeneity and possible anisotropy of the formation. Previously, the MPI method was developed for bounded reservoirs with limited numbers of wells. In this paper, we extend the MPI method to deal with cases of large numbers of wells and open reservoirs. To handle open reservoirs, we applied some modifications to the MPI method by adding a virtual well to the system and by modifying the pore volume. We applied the modifications in two nonvolumetric systems where there was either a leaking zone or an isolated zone, and found the approaches using the virtual well could predict the reservoir performance accurately. In cases with large numbers of wells, the required computational time to calculate the heterogeneity matrix may make the problem intractable. Therefore, we applied a model reduction strategy based on the location of the wells, called windowing. This technique ignores the parameters that have smaller effects on reservoir performance. We applied windowing to two cases with large numbers of wells (16 and 41 wells). We observed that, by selecting the proper window size, we can predict the reservoir performance accurately (R2 values greater than 99%) and decrease the CPU time up to a factor of 20 for the studied cases. The approaches described enabled us to provide realistic interpretations of interwell connectivity for complex cases where the simple MPI method would be difficult to apply. Integration of these approaches with the MPI method can quickly and efficiently model field data to optimize well patterns and flood parameters.
大量研究已經證實，連通度是影響提高原油采收率的最重要的因素之一。評價井間連通度有助于識別原油流動隔層和原油流動管道，為油藏管理和開采最優化提供了手段。基于多井生產指數(MPI)的方法提供了基于注水/生產數據的井對之間的連通度指數。通過消除井眼位置、表皮系數、注水率、計算的連通度得出的井底壓力等因素的影響，這樣得到的非均勻性模型就可反映地層的非均勻性和各向異性。以前，針對封閉儲層根據少數井的數據就得出多井生產指數(MPI)。本文中，我們擴展了MPI方法，處理了數目很多的井和不封閉油層。為了處理不封閉油層數據，我們通過把一個模擬井加入系統并修改孔隙體積，對MPI方法作了修正。我們在要么是漏失區或者是封閉區的兩個沒有測定體積的系統應用這一修正，發現使用了虛擬井的方法能夠精確預測油藏特性。在數目很多的井的實例中，通過調整計算非均勻性所要求的計算次數可以解決難以處理的問題。因此，我們應用了一個基于井眼位置的模型規約策略，叫做開窗術。這個方法忽略了對油藏特性影響小的參數。我們把開窗術應用于數目很多的井(16口井和41口井)的兩個實例。針對所研究的實例，通過選擇合適的油井最佳工作范圍，我們就可精確地預測油藏性能，降低計算機處理時間。相對于簡單的MPI方法很難應用的復雜實例，上述方法使我們能夠逼真地解釋井間連通度。綜合使用MPI方法能夠快速和有效地模擬現場數據，優化井網及注水參數。
譯自 SPE 129965 第17次SPE提高原油采收率座談會
