应用基于多井生产指数的方法评价井间连通度

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.

大量研究已经证实，连通度是影响提高原油采收率的最重要的因素之一。评价井间连通度有助于识别原油流动隔层和原油流动管道，为油藏管理和开采最优化提供了手段。基于多井生产指数（MPI)的方法提供了基于注水/生产数据的井对之间的连通度指数。通过消除井眼位置、表皮系数、注水率、计算的连通度得出的井底压力等因素的影响，这样得到的非均匀性模型就可反映地层的非均匀性和各向异性。以前，针对封闭储层根据少数井的数据就得出多井生产指数（MPI）。本文中，我们扩展了MPI方法，处理了数目很多的井和不封闭油层。为了处理不封闭油层数据，我们通过把一个模拟井加入系统并修改孔隙体积，对MPI方法作了修正。我们在要么是漏失区或者是封闭区的两个没有测定体积的系统应用这一修正，发现使用了虚拟井的方法能够精确预测油藏特性。在数目很多的井的实例中，通过调整计算非均匀性所要求的计算次数可以解决难以处理的问题。因此，我们应用了一个基于井眼位置的模型规约策略，叫做开窗术。这个方法忽略了对油藏特性影响小的参数。我们把开窗术应用于数目很多的井（16口井和41口井）的两个实例。针对所研究的实例，通过选择合适的油井最佳工作范围，我们就可精确地预测油藏性能，降低计算机处理时间。相对于简单的MPI方法很难应用的复杂实例，上述方法使我们能够逼真地解释井间连通度。综合使用MPI方法能够快速和有效地模拟现场数据，优化井网及注水参数。

译自SPE 129965第17次SPE提高原油采收率座谈会

译者简介：杜悫（1964-），男，工程师，1987年毕业于华东石油学院（现华东石油大学）开发系钻井工程专业，目前在中国石油吐哈油田公司工程技术研究院科研管理科工作，主要进行网络信息和国外英文钻井资料翻译工作。