利用DLVO模型预测砂岩储层纳米流体处理前后细粒运移临界pH值

R. Muneer, M. Hashmet, P. Pourafshary
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引用次数: 2

摘要

—注入水pH值影响砂岩中细粒的释放。细粒与砂粒之间的力平衡决定了系统中细粒的附着或释放。当pH值高于临界值时,细颗粒会释放出来,堵塞孔隙,造成地层损害。可以通过调节pH值和使用纳米流体来避免微粒的释放。本文引入了DLVO建模的概念,在没有大量实验的情况下估计了纳米流体应用前后的临界pH值。扫描电子显微镜测定从砂岩岩心收集的原位细粒的平均尺寸。制备了11700ppm和0.1wt%的纳米流体注入盐水,在pH值为2 ~ 12的条件下测量了分散砂的zeta电位,并量化了细砂与砂粒之间的表面吸引力和排斥力。建立了DLVO模型,用于预测二氧化硅纳米流体应用前后细颗粒的动员和临界pH值。zeta电位由Zetasizer测量,范围在-5 mV(更少的排斥)到-31 mV(更多的排斥)。此外,纳米流体的应用将zeta电位增加到-3 mV到-24.9 mV的范围,表明双电层的压缩。测量的zeta电位,离子强度和细尺寸用作计算表面力的输入,并开发了DLVO模型。根据模型预测,总DLVO相互作用从负向正转变的临界pH值约为8。DLVO模型还预测,使用纳米流体后,临界pH值提高到11,表明排斥力降低。DLVO建模方法有助于估计应用纳米流体前后的临界pH值,纳米技术验证了纳米颗粒控制细颗粒迁移和提高水驱和碱性驱作业临界pH值的能力。
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Prediction of Critical pH for Fines Migration Pre and Post Nanofluid Treatment in Sandstone Reservoirs using the DLVO Modelling
- Injection water pH affects the release of fines in sandstones. The force equilibrium between fines and sand governs the attachment or release of fines in the system. At a pH higher than a critical value, fines are released and block the pores, causing formation damage. The fines release can be avoided by adjusting the pH and using nanofluids. This paper introduces the concept of DLVO modelling to estimate the critical pH before and after the application of nanofluids without extensive experimentation. Scanning electron microscopy determines the average size of in-situ fines collected from sandstone core. Injection brine of 11700ppm and 0.1wt% nanofluid are prepared, zeta potentials of dispersed sand are measured with varying pH from 2 to 12, and the resulting attractive and repulsive surface forces between fines and sand grains are quantified. The DLVO models are developed to predict the mobilization of fines and a critical pH before and after the application of silica nanofluids. The zeta potentials are measured by a Zetasizer and are in the range of -5 mV (less repulsion) to -31 mV (more repulsion). Furthermore, the application of nanofluids increases the zeta potential to a range of -3 mV to -24.9 mV, indicating a compression in electric double layers. Measured zeta potentials, ionic strength, and fine size are used as inputs to compute surface forces, and DLVO models are developed. The critical pH, at which total DLVO interactions shift from negative to positive, as predicted by the model, is about 8. The DLVO model also predicted an improved critical pH of 11 following the use of nanofluids, demonstrating a reduction in repulsion forces. DLVO modelling approach helps estimate a critical pH before and after applying nanofluids, and nanotechnology validates nanoparticles' ability to control fines migration and improve critical pH for waterflooding and alkaline flooding operations.
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