用于拟合生产动态数据的现有储层模型重构(英文)

Reconstruction of an existing reservoir model for its calibration to dynamic data

计算机能力的提升和历史拟合方面的最新进展促进了对先前建立的储层模型的重新检验。为了节省工程师和CPU的时间,我们开发了4种独特的算法,来允许无需重新进行储层研究而重建现有模型。这些算法涉及的技术包括:优化、松弛、Wiener滤波或序贯重构。基本上,它们被用来确定一个随机函数和一系列随机数。给定一个随机函数,一族随机数将产生一个实现,这个实现和现有的储层模型十分接近。一旦随机数已知,现有的储层模型将被提交到一个历史拟合过程中,以此来改进数据拟合度或者考虑新收集到的数据。我们关注的是先前建立的相储层模型。虽然我们对模型模拟的方式一无所知,但是我们可以确定一系列随机数,再用多点统计模拟方法来建造一个和现有储层模型十分接近的实现。然后运行一种新的历史拟合程序来更新现有的储层模型,使其拟合两口新生产井的流量数据。

The increase in computer power and the recent developments in history-matching can motivate the reexamination of previously built reservoir models. To save the time of engineers and the CPU time, four distinct algorithms, which allow for rebuilding an existing reservoir model without restarting the reservoir study from scratch, were formulated. The algorithms involve techniques such as optimization, relaxation, Wiener filtering, or sequential reconstruction. They are used to identify a stochastic function and a set of random numbers. Given the stochastic function, the random numbers yield a realization that is close to the existing reservoir model. Once the random numbers are known, the existing reservoir model can be submitted to a new history-matching process to improve the data fit or to account for newly collected data. This article focuses on a previously built facies reservoir model. Although the simulation procedure is unknown to the authors, a set of random numbers are identified so that when provided to a multiple-point statistics simulator, a realization very close to the existing reservoir model is obtained. A new history-matching procedure is then run to update the existing reservoir model and to integrate the fractional flow rates measured in two producing wells drilled after the building of the existing reservoir model.