模型标定油藏模型试井响应数值模拟方法的验证与改进

Matteo Scapolo, M. Garcia, J. Mathieu, D. Siffert, O. Gosselin, P. Ackerer
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引用次数: 0

摘要

利用地质统计建模来填充储层属性是目前业内最常用的方法,并受到了广泛的关注。地质统计油藏模型定义了一个空间不确定性空间,可以通过生成许多等概率油藏属性实现来探索,这些等概率油藏属性实现是尽可能多的符合静态数据的油藏模型。其中,相关模型为与动态数据匹配的模型,完成了油藏模型标定的可用数据。在空间不确定性空间中寻找相关的储层模型是一个耗时的过程,需要模拟许多储层模型的动态(流动)响应。因此,迫切需要一种快速可靠的模拟方法来加快储层模型标定的进程。在这方面,已经开发并测试了一种新方法。该方法可以在解释试井结果与等效(平均)储层模型属性(透射率(k.h)和渗透率)之间进行简单快速的比较。这种比较可以用来验证或拒绝油藏模型,并获得如何修改该模型以适应试井数据的指示。本文给出了评价其性能并对其进行验证的方法和结果。试井解释渗透率(或透射率)是加权平均渗透率,通过在井周围适当定义的封闭表面上定义的渗透率来计算,其权重取决于流体几何形状。该方法基于稳态流动模拟,将被试井作为模拟域(油藏模型区)中心的源项(生产或注入井)进行。后者必须延伸到足以包含或至少重叠测试井的稳定区域,以估计平均透过率。该方法依赖于三个关键方面:定义与实际试井排水区域一致的模拟域(扩展和形状),定义相关的边界条件,以重现与实际试井产生的流动路径一致的流动路径,使用新的基于有效梯度的平均方法来计算正确定义的封闭表面上的平均渗透率。该方法在各种综合和部分真实的油田案例中进行了测试,首先对瞬态试井响应进行了模拟和解释,然后与使用新方法预测的透射率进行了比较。本文还对计算参数(流模拟域、流量等)进行了敏感性分析,以检验方法的鲁棒性并确定改进途径。结果表明,该方法具有速度快、精度高的特点。该方法旨在作为一种目标函数,对解释后的试井数据进行自动或辅助油藏模型校准。对于需要根据不确定的局部定义裂缝性统计数据计算渗透率张量的天然裂缝性油藏模型来说,该方法尤其有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validation and Improvement of Numerical Methods to Simulate the Well-Test Response of Reservoir Models for Model Calibration Purposes
Using geostatistical modelling to populate reservoir properties is nowadays the most common approach in the industry and it has received a great deal of attention. A geostatistical reservoir model defines a space of spatial uncertainty, which can be explored by generating many equiprobable reservoir property realisations that are as many possible reservoir models complying with static data. Among them, the relevant models are those that also match the dynamic data, which complete the available data for reservoir model calibration. Finding the relevant reservoir models in the space of spatial uncertainty is a time-consuming process that requires simulating the dynamic (flow) response of many reservoir models. Having a fast and reliable simulation method is then highly desirable to speed up the process of reservoir model calibration. In this context, a new approach has been developed and tested. The method allows easy and fast comparison between interpreted well-test results and equivalent (average) reservoir model properties in terms of transmissivity (k.h) and permeability. The comparison can be used to validate or reject a reservoir model, and to obtain indications on how to modify it to fit the well-test data. This paper presents the method and the results obtained to evaluate its performances and to validate it. Well-test-interpreted permeabilities (or transmissivities) are nothing but weighted average permeabilities that are to be calculated from permeabilities defined over closed surfaces properly defined around the well, the weights depending on the flow geometry. The proposed method is based on steady-state flow simulation that is carried out by making the tested well a source term (producing or injecting well) in the centre of a simulation domain (reservoir model region). The latter must be extended enough to contain, or at least overlap, the stabilisation area of the well test in which average transmissivities are to be estimated. The method relies on three key aspects: defining a simulation domain (extension and shape) that is consistent with the actual well-test drainage area, defining relevant boundary conditions to reproduce flow paths that are consistent with those generated by the actual well test, using the new effective-gradient based averaging method to compute average permeabilities over closed surfaces properly defined. The method is tested on various synthetic and partly real field cases, for which the transient well-test responses are first simulated and interpreted, then compared with the transmissivities that are predicted using the new method. Sensitivity analysis is also carried out on calculation parameters (flow simulation domain, flow rates…) to check the robustness of the method and identify improvement avenues. All these results tend to confirm the effectiveness of the method, which can combine speed and accuracy. This method is intended to be used as an objective function to perform automatic or assisted reservoir model calibration on interpreted well-test data. It is expected to be particularly useful to calibrate naturally fractured reservoir models for which permeability tensors are to be calculated from uncertain locally defined fracture property statistics.
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