碳酸盐岩储层增产液放置与流动模拟

Sam Wilson, P. Hammonds, G. Graham, D. Nichols, Hanen Ben Abdallah Bellio, F. Azuddin, Y. A. Sazali, A. Sauri
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引用次数: 0

摘要

我们报告了一种支持石灰岩油藏基质增产处理的模型的开发,该模型直接从岩心驱油过程中获得的数据中获取信息,这样该模型就可以在直接代表候选油田的条件下针对各种新型增产流体进行校准。该模型建立在先前为碎屑储层开发的增产模型的基础上,该模型主要将增产作为地层损害去除现象;它保留了早期模型的三维方面,但在将破坏-溶解反应与虫洞形成相关的水动力现象耦合时,包含了更大的复杂性。虫孔是增产碳酸盐岩储层的理想方法(在没有大规模水力压裂的情况下),但它们的形成是随机的、各向异性的,并且涉及更大的形态变化。因此,增产成功与否取决于配方化学成分、施用量、岩石形态、压力和温度。该初始模型已经过校准,以描述一系列非标准增产液的行为,并通过岩心驱替性能对其进行了部分评估。这些新型流体的反应速率数据是从一系列岩心驱油实验中提取出来的,并进行了流出液和形态分析。用户界面提供了简单的条件输入和选择,并提供了清晰的结果输出。未来的发展将把该模型扩展到更广泛的条件和化学配方。
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Modelling of Stimulation Fluid Placement and Flow in Carbonate Reservoirs
We report the development of a model to support matrix-based stimulation treatments in limestone reservoirs that takes information directly from data obtained during core flooding, such that the model can be calibrated against a variety of novel stimulation fluids under conditions directly representative of the candidate field. The model builds on an earlier stimulation model developed for clastic reservoirs, which primarily addressed stimulation as a formation-damage-removal phenomenon; it maintains the 3-dimensional aspects of the earlier model but incorporates the substantially greater complexity required in coupling the damage-dissolution reactions to the hydrodynamic phenomena associated with the formation of wormholes. Wormholes are an ideal method of stimulating carbonate reservoirs (in the absence of massive hydraulic fracturing) but their formation is stochastic, anisotropic, and involves greater morphological changes. Hence, successful stimulation depends on formulation chemistry, application rates, rock morphology, pressure, and temperature. This initial model has been calibrated to describe the behaviour of a selection of non-standard stimulation fluids, which have been evaluated in part through core-flood performance. The reaction-rate data for these novel fluids was abstracted from a series of core flood experiments with effluent and morphological analyses. The user interface provides easy condition input and selection and provides a clear output of results. Future developments will expand the model to a broader range of conditions and chemical formulations.
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