非等温和波动pH条件下砂岩岩心内低盐度增强微生物驱油LSAMF提高采收率数值研究

S. Chakraborty, S. Govindarajan, S. Gummadi
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引用次数: 0

摘要

在能源需求不断增加、油田数量不断减少、全球原油价格波动的时代,大多数石油公司都希望采用经济高效、环境可持续的提高采收率(EOR)技术,如低矿化度水驱(LSWF)和微生物提高采收率(MEOR)技术。本研究通过数值模拟研究了在时空波动的pH和温度条件下,砂岩岩心内同时发生LSWF和微生物驱油对第三纪模式下原位MEOR的综合影响。所建立的黑油模型包括五大耦合子模型:非线性热输运模型;离子传输耦合多重离子交换(MIE),涉及未络合的阳离子和阴离子;pH值随盐度和温度的变化;微生物最大比生长率随温度、盐度和pH值的变化而变化;LSWF和生物膜沉积导致界面张力降低和润湿性改变(WA)的相对渗透率和分数流动曲线变化。采用有限差分法求解控制方程。采用算子分割和二分法求解mie传输模型。该模型在数值上是稳定的,并且与以前发表的实验和分析结果吻合得很好。在提出的mie输运机制中,注入水矿化度(IWS)从2.52 M降低到0.32 M,导致Ca2+解吸增强,使岩石表面更湿润。因此,当含水饱和度为0.5时,与初始油湿状态相比,油相对渗透率(kro)增加,水分流(fw)减少55%以上。进一步降低IWS至0.03 M, Ca2+吸附使表面润湿性更亲油,从而使fw增加52%。地层水盐度(FWS)对WA的影响较小,将IWS从2.52 M降低到0.32 M的影响为63%,进一步降低IWS和FWS的影响可以忽略不计。这可能是由于限制非等温(40至55°C)和营养物质的可用性条件。LSAMF引起了显著的WA, kro增加,fw降低>84%。pH值从8.0增加到8.9,但对微生物代谢的影响较小。在注入点附近观察到的生物堵塞对地层的损害,可以通过生物表面活性剂在砂岩岩心深处的有效运移来补偿。本研究是一项新颖的尝试,旨在通过同时解决典型砂岩地层中复杂的原油-岩石-盐水化学和控制MEOR效率的关键热力学参数,展示LSAMF与LSWF在提高岩心尺度原油流动性和采收率方面的协同效应。该模型具有较低的计算成本和运行时间,提高了预测能力,为成功实现LSAMF预先选择潜在的候选者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Numerical Investigation on Low Salinity Augmented Microbial Flooding LSAMF within a Sandstone Core for Enhanced Oil Recovery Under Nonisothermal and Fluctuating pH Conditions
In an era of increasing energy demand, declining oil fields and fluctuating crude oil prices globally, most oil companies are looking forward to implementing cost effective and environmentally sustainable enhanced oil recovery (EOR) techniques such as low salinity waterflooding (LSWF) and microbial EOR (MEOR). The present study numerically investigates the combined influence of simultaneous LSWF and microbial flooding for in-Situ MEOR in tertiary mode within a sandstone core under spatiotemporally fluctuating pH and temperature conditions. The developed black oil model consists of five major coupled submodels: nonlinear heat transport model; ion transport coupled with multiple ion exchange (MIE) involving uncomplexed cations and anions; pH variation with salinity and temperature; coupled reactive transport of injected substrates, Pseudomonas putida and produced biosurfactants with microbial maximum specific growth rate varying with temperature, salinity and pH; relative permeability and fractional flow curve variations due to interfacial tension reduction and wettability alteration (WA) by LSWF and biofilm deposition. The governing equations are solved using finite difference technique. Operator splitting and bisection methods are adopted to solve the MIE-transport model. The present model is found to be numerically stable and agree well with previously published experimental and analytical results. In the proposed MIE-transport mechanism, decreasing injection water salinity (IWS) from 2.52 to 0.32 M causes enhanced Ca2+ desorption rendering rock surface towards more water wet. Consequently, oil relative permeability (kro) increases with >55% reduction in water fractional flow (fw) at water saturation of 0.5 from the initial oil-wet condition. Further reducing IWS to 0.03 M causes Ca2+ adsorption shifting the surface wettability towards more oil-wet thus increasing fw by 52%. Formation water salinity (FWS) showed minor impact on WA with <5% decrease in fw when FWS is reduced from 3.15 to 1.05 M. During LSAMF, biosurfactant production is enhanced by >63% on reducing IWS from 2.52 to 0.32 M with negligible increase on further reducing IWS and FWS. This might be due to limiting nonisothermal (40 to 55 °C) and nutrient availability conditions. LSAMF caused significant WA, increase in kro with fw reduction by >84%. Though pH increased from 8.0 to 8.9, it showed minor impact on microbial metabolism. Formation damage due to bioplugging observed near injection point is compensated by effective migration of biosurfactants deep within sandstone core. The present study is a novel attempt to show synergistic effect of LSAMF over LSWF in enhancing oil mobility and recovery at core scale by simultaneously addressing complex crude oil-rock-brine chemistry and critical thermodynamic parameters that govern MEOR efficiency within a typical sandstone formation. The present model with relatively lower computational cost and running time improves the predictive capability to pre-select potential field candidates for successful LSAMF implementation.
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