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岩石润湿性影响因素研究综述

Insights into Rock Wettability Influencing Factors: A Review.

作者信息

Deng Xiao, Bashir Ahmed, Kamal Muhammad Shahzad, Raza Arshad, Patil Shirish, Zhou Xianmin, Mahmoud Mohamed, Shakil Hussain Syed Muhammad

机构信息

Center for Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Saudi Arabia.

Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Saudi Arabia.

出版信息

ACS Omega. 2024 Dec 5;9(50):48899-48917. doi: 10.1021/acsomega.4c07387. eCollection 2024 Dec 17.

DOI:10.1021/acsomega.4c07387
PMID:39713650
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11656604/
Abstract

Wetting characteristics of a hydrocarbon reservoir are generally quantified for cost-effective field development. The wetting process of rock by oil is a complex process involving reactions among compounds (rock, oil, and brine), the impact of environmental conditions (temperature, pressure, etc.), and treatment history (coring, transportation, etc.). There has not been much attention given to understanding the mechanisms causing different rock wetting states to quantify rock's wettability. This work aims to provide an in-depth insight into rock wettability influencing factors including CO & H. In addition, advanced computational approaches such as molecular dynamics simulation, computational fluid dynamics, and machine learning for wettability have also been reviewed to govern the undiscovered interactions and mechanisms of this complex process. The key observation is that the polarity of organic components (asphaltenes and long-chain acids) determines the oil wetness in crude oil. In addition, acidic polar organics dominate oil-wetting in carbonate rocks; basic polar organics are key in sandstone. Also, environmental factors such as water films, brine salinity, and pH influence wettability significantly.

摘要

为了实现经济高效的油田开发,通常需要对油气藏的润湿性进行量化。原油对岩石的润湿过程是一个复杂的过程,涉及化合物(岩石、原油和盐水)之间的反应、环境条件(温度、压力等)的影响以及处理历史(取芯、运输等)。目前,对于导致岩石呈现不同润湿状态的机制以及如何量化岩石润湿性,人们关注较少。这项工作旨在深入了解包括CO和H在内的影响岩石润湿性的因素。此外,还综述了分子动力学模拟、计算流体动力学和机器学习等先进的计算方法,以探究这一复杂过程中尚未被发现的相互作用和机制。关键观察结果是,有机成分(沥青质和长链酸)的极性决定了原油中的油湿性。此外,酸性极性有机物在碳酸盐岩的油湿过程中起主导作用;碱性极性有机物则是砂岩油湿的关键因素。此外,水膜、盐水盐度和pH值等环境因素对润湿性也有显著影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95b1/11656604/b2bd7600d791/ao4c07387_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95b1/11656604/ecab865f3e50/ao4c07387_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95b1/11656604/02500ca794c4/ao4c07387_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95b1/11656604/90568170a44f/ao4c07387_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95b1/11656604/b2bd7600d791/ao4c07387_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95b1/11656604/ecab865f3e50/ao4c07387_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95b1/11656604/02500ca794c4/ao4c07387_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95b1/11656604/90568170a44f/ao4c07387_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95b1/11656604/b2bd7600d791/ao4c07387_0004.jpg

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本文引用的文献

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Predicting wettability of mineral/CO/brine systems via data-driven machine learning modeling: Implications for carbon geo-sequestration.通过数据驱动的机器学习建模预测矿物/CO2/盐水体系的润湿性:对碳封存的影响。
Chemosphere. 2023 Dec;345:140469. doi: 10.1016/j.chemosphere.2023.140469. Epub 2023 Oct 17.
2
A Guide for Selection of Aging Time and Temperature for Wettability Alteration in Various Rock-Oil Systems.不同岩石-油体系中润湿性改变的老化时间和温度选择指南
ACS Omega. 2023 Aug 16;8(34):30790-30801. doi: 10.1021/acsomega.3c00023. eCollection 2023 Aug 29.
3
Data-driven models to predict shale wettability for CO sequestration applications.
用于 CO2 封存应用的基于数据驱动的页岩润湿性预测模型。
Sci Rep. 2023 Jun 22;13(1):10151. doi: 10.1038/s41598-023-37327-2.
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Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage.利用先进的机器学习技术和受自然启发的方法改进页岩润湿性预测:对碳捕集利用和封存的影响。
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Molecular Dynamics Investigation of Wettability Alteration of Quartz Surface under Thermal Recovery Processes.分子动力学研究热回收过程中石英表面润湿性的变化。
Molecules. 2023 Jan 24;28(3):1162. doi: 10.3390/molecules28031162.
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Surface Wettability Prediction Using Image Analysis and an Artificial Neural Network.基于图像分析和人工神经网络的表面润湿性预测
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The Influence of Oil Composition, Rock Mineralogy, Aging Time, and Brine Pre-soak on Shale Wettability.原油成分、岩石矿物学、老化时间及盐水预浸泡对页岩润湿性的影响
ACS Omega. 2021 Dec 20;7(1):85-100. doi: 10.1021/acsomega.1c03940. eCollection 2022 Jan 11.
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A Surface Charge Approach to Investigating the Influence of Oil Contacting Clay Minerals on Wettability Alteration.一种研究油接触粘土矿物对润湿性改变影响的表面电荷方法。
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