从实验室规模到现场规模的低盐度效益提升——具有相对渗透率不确定范围的模型集合

Aboulghasem Kazemi Nia Korrani, G. Jerauld
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引用次数: 2

摘要

低矿化度相对渗透率曲线是评估油田低矿化度水驱效益的必要条件。低矿化度效益是通过岩心驱液(即在桥塞尺度上)来测量的,在全油田模型中,通常假设从高矿化度曲线(通常是伪曲线)生成低矿化度曲线也具有同样的效益。研究了这一假设的有效性。我们介绍了低盐度效益的不确定性分布如何通过全场模型的集合传播,其中每个模拟案例都可以有一组独特的高盐度伪值。使用了0.5英尺的垂直分辨率扇区和10英尺的升级版扇区。岩心驱油的低矿化度效益被用于高分辨率区块的低矿化度相对渗透率。岩石曲线(岩心注水的相对渗透率曲线)用于高分辨率领域,以创建“真实”剖面。通过历史匹配高矿化度和增量低矿化度真值剖面,可以生成放大段的伪高、低矿化度曲线。将升级模型的低盐度效益与高分辨率扇区(“真值”模型)的低盐度效益进行了比较。在高分辨率模型中加入毛细压力是至关重要的。在研究的案例中,模拟数据和已发表的数据被用于绘制低盐度毛细管压力曲线。我们的研究结果表明,利用岩心驱油的低盐度效益为高盐度伪曲线生成的低盐度曲线略微低估了现场尺度上的真实低盐度效益(即高分辨率模型估计的低盐度效益)。这一结论在测试的两种极端相对渗透率情况下是一致的(即高总流动性-不利的分流和低总流动性-有利的分流)。我们展示了如何将一组从岩心流体中获得的高盐度相对渗透率数据(包括分数流动和总流动性的范围)适当地包括在集合模型中,以及如何估计这种集合的低盐度效益。对于边界高盐度曲线集,生成低盐度曲线是足够的。然后,边界低盐度曲线可用于估计任何插值高盐度曲线的低盐度曲线。这大大简化了为模型集合生成低盐度效益概率分布函数(pdf)的过程,其中每个模型具有不同的高盐度相对渗透率。我们解释了伪化过程,以及如何为高矿化度相对渗透率生成对应的低矿化度曲线,以实现岩心注水的低矿化度效益。我们介绍了如何为具有独特的高盐度曲线的模型集合构建低盐度效益的pdf,每个模型都具有低盐度效益。该工作流程简化了描述低矿化度水驱效益不确定性的过程。
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Upscaling Low Salinity Benefit from Lab-Scale to Field-Scale - An Ensemble of Models with a Relative Permeability Uncertainty Range
Low salinity relative permeability curves are required to estimate the benefit of low salinity waterflooding at the field-level. Low salinity benefit is measured from corefloods (i.e., at the plug scale) and the same benefit is often assumed in full field models to generate low salinity curves from high salinity curves (often pseudo curves). The validity of this assumption is investigated. We present how uncertainty distribution of low salinity benefit can be propagated through an ensemble of full field models in which each simulation case could have a set of distinctive high salinity pseudos. A 0.5-ft vertical resolution sector and its 10-ft upscaled counterpart are used. Low salinity benefit from corefloods is used to generate low salinity relative permeabilities for the high-resolution sector. Rock curves (relative permeability curves from corefloods) are used in the high-resolution sector to create "truth" profiles. Pseudo high and low salinity curves are generated for the upscaled sector by history matching high salinity and incremental low salinity truth case profiles. Low salinity benefit from the upscaled model is compared against that of high-resolution sector ("truth" model). It is crucial to include capillary pressure in high resolution models. In the case studied, analogue and published data are used to produce low salinity capillary pressure curves. Our results show that generating low salinity curves for high salinity pseudos using low salinity benefit from corefloods slightly underestimates the true low salinity benefit at field-scale (i.e., low salinity benefit estimated from high-resolution models). This conclusion is consistent for two extreme relative-permeability scenarios tested (i.e., a high total mobility-unfavorable fractional flow and low total mobility-favorable fractional flow). We demonstrate how a set of high salinity relative-permeability data obtained from corefloods, which encompasses a range for fractional flow and total mobility, can be included in ensemble modeling appropriately, and how low salinity benefit could be estimated for such an ensemble. It is adequate to generate low salinity curves for bounding high salinity sets of curves. The bounding low salinity curves can then be used to estimate low salinity curve for any interpolated high salinity curve. This significantly simplifies the process of generating a probability distribution function (pdf) of low salinity benefit for an ensemble of models, where each model has a different high salinity relative permeability. We explain the pseudoization process and how to generate a counterpart low salinity curve for a high salinity relative permeability that honors an estimated low salinity benefit from corefloods. We present how a pdf of low salinity benefit can be built for an ensemble of models with distinctive high salinity curves that each honors the low salinity benefit. The workflow simplifies the process of describing the uncertainty in the benefit of low salinity waterflooding.
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