Background

Simulation plays a pivotal role in the design of enhanced oil recovery (EOR) processes based on reservoir and in-situ fluid conditions. A robust compositional model, using a complicated multi-component nanoemulsion injection fluid, was developed to describe the performance of nanoemulsion flooding to predict their feasibility for pilot oilfield projects.

Method

Gemini surfactant/polymer/nanoparticle stabilized Pickering nanoemulsions were prepared by high-energy method and characterized to assess core-flooding performance. During simulation, a Cartesian grid model with fixed bulk volume, injection flow rate, well completion parameters and rock-fluid properties was employed. Core-flooding experiments were performed in steps, involving ~2.16 pore volume (PV) brine injection, ~0.90 PV nanoemulsion injection and ~1.50 PV chase water injection.

Significant findings

Oil saturation map and relative permeability data analyses showed that the wetting nature of sandstone core altered from intermediate-wet to strongly water-wet condition. Tertiary recoveries were obtained in the range of 21-27% of the original oil in place (OOIP) for different surfactant/polymer/nanoparticle (SPN) compositions of injected nanoemulsion fluids. Flooding simulation studies showed good history matching of production data within ± 6% between experimental and simulated results. In summary, the efficacy of SPN nanoemulsions as an EOR fluid was corroborated with the aid of numerical simulation analyses.

中文翻译：

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表面活性剂-聚合物-纳米颗粒（SPN）纳米乳液辅助提高采收率的成分模拟模型及历史匹配分析

背景

模拟在基于储层和原位流体条件的提高石油采收率 (EOR) 工艺设计中起着关键作用。使用复杂的多组分纳米乳液注入流体开发了一个稳健的成分模型来描述纳米乳液驱的性能，以预测其在油田试点项目中的可行性。

方法

Gemini 表面活性剂/聚合物/纳米颗粒稳定的 Pickering 纳米乳液通过高能方法制备并表征以评估岩心驱油性能。在模拟过程中，采用了具有固定体积、注入流量、完井参数和岩石流体特性的笛卡尔网格模型。岩心驱油实验分步骤进行，包括约 2.16 孔体积 (PV) 盐水注入、约 0.90 PV 纳米乳液注入和约 1.50 PV 追逐注水。

重要发现

含油饱和度图和相对渗透率数据分析表明，砂岩岩心的润湿性质由中湿状态转变为强水湿状态。对于注入的纳米乳液流体的不同表面活性剂/聚合物/纳米颗粒 (SPN) 组合物，三次采收率范围为原始石油 (OOIP) 的 21-27%。洪水模拟研究表明，在实验和模拟结果之间，生产数据的历史匹配良好，误差在 ± 6% 以内。总之，借助数值模拟分析证实了 SPN 纳米乳液作为 EOR 流体的功效。