非混相WAG磁滞参数从核心到满场尺度的实际提升

O. Talabi, J. Moreno, R. K. Malhotra, Boon Keat Tham
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引用次数: 0

摘要

非混相水-交变气(iWAG)驱通常被认为是水驱或即将水驱油藏的三次采油技术,由于其更好的流动性控制和潜在的有利相相对渗透率的滞后变化,可以提高采收率。在这种情况下,通常油藏模拟模型已经存在,并且已经进行了校准,通常会在历史匹配阶段修改饱和度函数。然而,要利用这些模型预测iWAG的性能,可能需要额外的参数。这些可以通过模拟WAG岩心驱油实验得到。虽然在许多已发表的案例中,从匹配实验结果中获得的参数值不加修改地使用,但这可能是不可取的,因为参数仅在获得它们的核心尺度上有效。本文讨论了系统地将在岩心尺度上获得的WAG参数升级到现有的全油田模型的挑战。在这项工作中,我们使用了一个多阶段的放大过程,从核心规模到整个油田规模。第一阶段采用岩心尺度模型拟合“代表性”岩心洪水实验,获得WAG参数。第二种方法使用全油田模型的井对井高分辨率一维剖面,使用岩心尺寸网格块填充,使用从岩心获得的未改变的WAG参数生成“参考”WAG性能。第三阶段使用类似的1D模型,但在全场模型分辨率下使用网格块填充,以匹配参考模型的结果,同时尽可能少地调整WAG参数。最后,利用全场模型分辨率和全场相对渗透率函数(假设已调整为匹配历史并考虑色散)建立模型,与参考模型结果进行匹配,并获得最终的升级WAG参数。在这个多阶段过程结束时得到的升级WAG参数可用于现场规模。该过程允许对与升级过程相关的不确定性进行清晰的量化。第三阶段的模拟表明,一旦全场与核心尺度网格尺寸比超过某一点(2500:1),升级后与参考模型结果的差异就会明显增大。研究发现,如果为了匹配参考模型中的采收率结果而改变全油田模型分辨率模型中的WAG参数,Land参数可能会改变10%,相对渗透率降低系数可能会增加30%,尽管这可能会因情况而异。因此,建议识别和使用全场模型分辨率以尽可能接近阈值。在全油田模型中直接使用岩心尺度iWAG参数的做法可能会低估实际采收率,而高估注入能力。当只考虑WAG机制时,采收率的价值低估了随着注入孔隙体积的增加而增加的价值,在我们的例子中,在注入1孔隙体积的流体后,采收率增加了7%。这种多阶段模拟方法有助于确定油藏模型中模拟iWAG驱油所需的调整和不确定性。这种方法利用了有限差分模拟器中广泛存在的选项,解决了利用现有伪函数的挑战,并提供了一种实用的方法,通过该方法可以改进iWAG性能预测。
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Practical Upscaling of Immiscible WAG Hysteresis Parameters from Core to Full Field Scale
Immiscible water-alternating-gas (iWAG) flooding is often considered as a tertiary recovery technique in waterflooded or about-to-be waterflooded reservoirs to increase oil recovery due to better mobility control and potentially favorable hysteretic changes to phase relative permeabilities. In such cases, typically, reservoir simulation models already exist and have been calibrated, often modifying saturation functions during the history matching stage. However, to utilize such models in forecasting iWAG performance, additional parameters may be required. These can be acquired by simulation of WAG coreflood experiments. While in many published cases, the parameter values obtained from matching experimental results are used without modification, this may not be advisable since the parameters are only valid at the core scale at which they were obtained. This paper discusses the challenge of systematically upscaling WAG parameters obtained at core scale to an existing full field model. In this work, we use a multi-stage upscaling process from core scale to full field scale. The first stage uses a core scale model to match ‘representative’ core flood experiments and obtain WAG parameters. The second uses a well-to-well high-resolution 1D section of the full field model populated using gridblocks of core size to generate ‘reference’ WAG performance using the unaltered WAG parameters obtained from core. The third stage uses a similar 1D model but populated using gridblocks at full field model resolution to match the results from the reference model while adjusting the WAG parameters as little as possible. Finally, a model using the full field model resolution as well as the full field relative permeability functions which, it is assumed, have been tuned to match the history and account for dispersion is used to match the reference model results and obtain final upscaled WAG parameters. The upscaled WAG parameters obtained at the end of this multi-stage process can be used at the field scale. This process allows clear quantification of the uncertainty associated with the upscaling process. Simulations at the third stage showed that once the full field to core scale grid size ratio exceeded a certain point (2500:1), there was a marked increase in the difference between upscaled and reference model results. It was found that if WAG parameters were changed in the full field model resolution model in order to match recovery results in the reference model, Land's parameter could change by up to 10% and relative permeability reduction factor could increase by up to 30% although it is expected that this will vary from case to case. It is therefore recommended to identify and use full field model resolutions to as close to the threshold as possible. The practice of using the core scale iWAG parameters in the full field model directly could under-estimate actual recovery, and overestimate injectivity. When considering the WAG mechanism alone, the value of the recovery underestimate increasing with pore volumes injected and, in our case, by up to 7% after injecting 1 pore volume of fluid. This multi-stage simulation approach helps identify the adjustments required and uncertainties associated with simulating iWAG flooding in reservoir models. This approach utilizes options widely present in commercially available finite difference simulators, addresses the challenge of utilizing existing pseudo functions and provides a practical methodology through which iWAG performance forecasting can be improved.
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