Predicting and optimizing CO2 foam performance for enhanced oil recovery: A machine learning approach to foam formulation focusing on apparent viscosity and interfacial tension

IF 3.7 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
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Abstract

Carbon dioxide foam injection stands as a promising method for enhanced oil recovery (EOR) and carbon sequestration. However, accurately predicting its efficiency amidst varying operational conditions and reservoir parameters remains a significant challenge for conventional modeling techniques. This study explores the application of machine learning (ML) methodologies to develop a robust model for matching experimental values in CO2 foam flooding scenarios. Leveraging a comprehensive dataset encompassing diverse surfactants and rock types, with varied porosity and permeability, our model demonstrates accurate predictions across a wide spectrum of conditions. By focusing on key parameters such as foam apparent viscosity, interfacial tension (IFT), injected foam volume, initial oil saturation, porosity, and permeability, we unveil the pivotal role of these factors in determining CO2 foam EOR performance. Through rigorous analysis, we identify the relative importance of each input parameter, with injected foam volume, apparent viscosity, and IFT emerging as dominant factors. The most accurate model was deep neural network (DNN) (R2 value of 0.99). Higher foam viscosity and lower IFT were found to significantly enhance oil recovery rates, though their effects plateau beyond certain thresholds (apparent viscosities above 1200 cP and IFT values below 0.2 mN/m). The findings underscore the potential of ML-driven approaches in enhancing CO2 foam EOR predictions, offering insights crucial for optimizing foam flooding performance across diverse reservoir settings.

Abstract Image

预测和优化用于提高石油采收率的二氧化碳泡沫性能:以表观粘度和界面张力为重点的泡沫配方机器学习方法
二氧化碳泡沫注入法是一种前景广阔的提高石油采收率(EOR)和固碳方法。然而,在不同的作业条件和储层参数下准确预测其效率,对于传统建模技术来说仍然是一项重大挑战。本研究探索了机器学习(ML)方法的应用,以开发一种稳健的模型,用于匹配二氧化碳泡沫淹没场景中的实验值。利用包含各种表面活性剂和岩石类型以及不同孔隙度和渗透率的综合数据集,我们的模型在各种条件下都能做出准确的预测。通过关注泡沫表观粘度、界面张力(IFT)、注入泡沫量、初始石油饱和度、孔隙度和渗透率等关键参数,我们揭示了这些因素在决定二氧化碳泡沫 EOR 性能方面的关键作用。通过严格的分析,我们确定了每个输入参数的相对重要性,其中注入泡沫量、表观粘度和 IFT 成为主导因素。最准确的模型是深度神经网络(DNN)(R2 值为 0.99)。研究发现,较高的泡沫粘度和较低的 IFT 能显著提高采油率,但超过一定阈值(表观粘度高于 1200 cP 和 IFT 值低于 0.2 mN/m)后,其效果会趋于平缓。这些发现强调了以 ML 为驱动的方法在提高 CO2 泡沫 EOR 预测方面的潜力,为在不同储层环境中优化泡沫淹没性能提供了至关重要的见解。
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来源期刊
Marine and Petroleum Geology
Marine and Petroleum Geology 地学-地球科学综合
CiteScore
8.80
自引率
14.30%
发文量
475
审稿时长
63 days
期刊介绍: Marine and Petroleum Geology is the pre-eminent international forum for the exchange of multidisciplinary concepts, interpretations and techniques for all concerned with marine and petroleum geology in industry, government and academia. Rapid bimonthly publication allows early communications of papers or short communications to the geoscience community. Marine and Petroleum Geology is essential reading for geologists, geophysicists and explorationists in industry, government and academia working in the following areas: marine geology; basin analysis and evaluation; organic geochemistry; reserve/resource estimation; seismic stratigraphy; thermal models of basic evolution; sedimentary geology; continental margins; geophysical interpretation; structural geology/tectonics; formation evaluation techniques; well logging.
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