Estimation of nitrogen-crude oil interfacial tension: Application of hybrid machine learning algorithms

IF 4.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth Pub Date : 2025-08-12 DOI:10.1016/j.pce.2025.104056

Jian Shen , Anupama Yadav , Farag M.A. Altalbawy , Mohammad Alaa Hussain Al-Hamami , Jayaprakash B , S Srinadh Raju , Nizomiddin Juraev , Hameed Hassan Khalaf , Ahmed Safa'a Tariq Habeeb , Nada Qasim Mohammed , Saif Hameed Hlail , Merwa Alhadrawi , Mohammad Mahtab Alam , Mahmood Kiani

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引用次数: 0

Abstract

Nitrogen gas injection has been identified as an effective Enhanced Oil Recovery (EOR) technique in the last years. Precise measurement of interfacial tension (IFT) under reservoir conditions is crucial for planning an effective gas-based EOR process. Conversely, measuring IFT through experimental means is expensive and difficult, requiring complex interpretation and costly devices and arduous procedures. This highlights the necessity of developing accurate and affordable models for estimating IFT. This article aims to propose a smart method using Adaptive Neuro-fuzzy Interference System (ANFIS) optimized by Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to precisely predict the N₂-crude oil IFT considering pressure, temperature, and density difference. The IFT system's sensitivity analysis revealed that the density difference of the phases is the most impactful parameter. Moreover, nearly all the collected experimental data is considered trustworthy for building the model. In conclusion, ANFIS-PSO was suggested to be more trustworthy than ANFIS-GA in relation to determination coefficient (ANFIS-PSO: 0.88 and ANFIS-GA: 0.78), and average absolute relative error (ANFIS-PSO: 11.08 and ANFIS-GA: 21.98) when validated against test data. The results obtained indicate that the ANFIS-PSO model developed can reliably be utilized to precisely estimate N₂-crude oil IFT for EOR studies and optimizations tasks.

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氮-原油界面张力的估计：混合机器学习算法的应用

近年来，氮气注入被认为是一种有效的提高石油采收率（EOR）技术。油藏条件下界面张力（IFT）的精确测量对于规划有效的气基EOR工艺至关重要。相反，通过实验手段测量IFT是昂贵和困难的，需要复杂的解释和昂贵的设备和艰巨的程序。这突出了开发准确和负担得起的模型来估计IFT的必要性。本文提出了一种基于遗传算法（GA）和粒子群算法（PSO）优化的自适应神经模糊干扰系统（ANFIS）来精确预测考虑压力、温度和密度差异的n2原油IFT的智能方法。系统的灵敏度分析表明，相密度差是影响最大的参数。此外，几乎所有收集到的实验数据都被认为是可信的，可以用来建立模型。综上所述，当对测试数据进行验证时，anfiss - pso在决定系数（anfiss - pso: 0.88和anfiss - ga: 0.78）和平均绝对相对误差（anfiss - pso: 11.08和anfiss - ga: 21.98）方面比anfiss - ga更可信。结果表明，所建立的anfiss - pso模型可以可靠地准确估计n2 -原油的IFT，用于提高采收率研究和优化任务。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physics and Chemistry of the Earth 地学-地球科学综合

CiteScore

5.40

自引率

2.70%

发文量

176

审稿时长

31.6 weeks

期刊介绍： Physics and Chemistry of the Earth is an international interdisciplinary journal for the rapid publication of collections of refereed communications in separate thematic issues, either stemming from scientific meetings, or, especially compiled for the occasion. There is no restriction on the length of articles published in the journal. Physics and Chemistry of the Earth incorporates the separate Parts A, B and C which existed until the end of 2001. Please note: the Editors are unable to consider submissions that are not invited or linked to a thematic issue. Please do not submit unsolicited papers. The journal covers the following subject areas: -Solid Earth and Geodesy: (geology, geochemistry, tectonophysics, seismology, volcanology, palaeomagnetism and rock magnetism, electromagnetism and potential fields, marine and environmental geosciences as well as geodesy). -Hydrology, Oceans and Atmosphere: (hydrology and water resources research, engineering and management, oceanography and oceanic chemistry, shelf, sea, lake and river sciences, meteorology and atmospheric sciences incl. chemistry as well as climatology and glaciology). -Solar-Terrestrial and Planetary Science: (solar, heliospheric and solar-planetary sciences, geology, geophysics and atmospheric sciences of planets, satellites and small bodies as well as cosmochemistry and exobiology).