Comprehensive investigation of low-salinity waterflooding in sandstone reservoirs

Snosy, M. Fouad; Ela, Mahmoud Abu El; El-Banbi, Ahmed; Sayyouh, Helmy

doi:10.1007/s13202-020-00862-z

Cited by 30 publications

(5 citation statements)

References 82 publications

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“…In addition, Alotaibi and Nasr-El-Din (2019) suggested that there is a critical salinity (not the lowest), at which the IFT between oil and brine is at a minimum level. Snosy et al (2020) and Snosy et al (2021) reported the same results from the sandstone reservoir.…”

Section: Salinity Range For the Lswf Applicationssupporting

confidence: 63%

Comprehensive investigation of low salinity waterflooding in carbonate reservoirs

Snosy

Ela

El-Banbi

et al. 2021

J Petrol Explor Prod Technol

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Waterflooding has been practiced as a secondary recovery mechanism for many years with no regard to the composition of the injected brine. However, in the last decade, there has been an interest to understand the impact of the injected water composition and the low salinity waterflooding (LSWF) in oil recovery. LSWF has been investigated through various laboratory tests as a promising method for improving oil recovery in carbonate reservoirs. These experiments showed diverse mechanisms and results. In this study, a comprehensive review and analysis for results of more than 300 carbonate core flood experiments from published work were performed to investigate the effects of several parameters (injected water, oil, and rock properties along with the temperature) on oil recovery from carbonate rock. The analysis of the results showed that the water composition is the key parameter for successful waterflooding (WF) projects in the carbonate rocks. However, the salinity value of the injected water seems to have a negligible effect on oil recovery in both secondary and tertiary recovery stages. The study indicated that waterflooding with optimum water composition can improve oil recovery up to 30% of the original oil in place. In addition, the investigation showed that changing water salinity from LSWF to high salinity waterflooding can lead to an incremental oil recovery of up to 18% in the tertiary recovery stage. It was evident that applying the optimum composition in the secondary recovery stage is more effective than applying it in the tertiary recovery stage. Furthermore, the key parameters of the injected water and rock properties in secondary and tertiary recovery stages were studied using Fractional Factorial Design. The results revealed that the concentrations of Mg2+, Na+, K+, and Cl− in the injected water are the greatest influence parameters in the secondary recovery stage. However, the most dominant parameters in the tertiary recovery stage are the rock minerals and the concentration of K+, HCO3−, and SO42− in the injected water. In addition, it appears that the anhydrite percentage in the carbonate reservoirs may be an effective parameter in the tertiary WF. Also, there are no clear relations between the incremental oil recovery and the oil properties (total acid number or total base number) in both secondary and tertiary recovery stages. In addition, the results of the analysis showed an incremental oil recovery in all ranges of the studied flooding temperatures. The findings of this study can help to establish guidelines for screening and designing optimum salinity and composition for WF projects in carbonate reservoirs.

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Section: Salinity Range For the Lswf Applicationssupporting

confidence: 63%

Comprehensive investigation of low salinity waterflooding in carbonate reservoirs

Snosy

Ela

El-Banbi

et al. 2021

J Petrol Explor Prod Technol

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“…Factorial design is an important statistical method for optimizing and simplifying laboratory experiments by investigating the effects of multiple controllable factors on interest responses 34 , 57 . These statistical evaluations of gas viscosity were conducted using Minitab software 55 .…”

Section: Resultsmentioning

confidence: 99%

Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions

Gomaa,

Abdalla,

Salem

et al. 2024

Sci Rep

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The accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy of natural gas operations. Due to their time-consuming and costly nature, experimental measurements of gas viscosity are challenging. Data-based machine learning (ML) techniques afford a resourceful and less exhausting substitution, aiding research and industry at gas modeling that is incredible to reach in the laboratory. Statistical approaches were used to analyze the experimental data before applying machine learning. Seven machine learning techniques specifically Linear Regression, random forest (RF), decision trees, gradient boosting, K-nearest neighbors, Nu support vector regression (NuSVR), and artificial neural network (ANN) were applied for the prediction of methane (CH4), nitrogen (N2), and natural gas mixture viscosities. More than 4304 datasets from real experimental data utilizing pressure, temperature, and gas density were employed for developing ML models. Furthermore, three novel correlations have developed for the viscosity of CH4, N2, and composite gas using ANN. Results revealed that models and anticipated correlations predicted methane, nitrogen, and natural gas mixture viscosities with high precision. Results designated that the ANN, RF, and gradient Boosting models have performed better with a coefficient of determination (R2) of 0.99 for testing data sets of methane, nitrogen, and natural gas mixture viscosities. However, linear regression and NuSVR have performed poorly with a coefficient of determination (R2) of 0.07 and − 0.01 respectively for testing data sets of nitrogen viscosity. Such machine learning models offer the industry and research a cost-effective and fast tool for accurately approximating the viscosities of methane, nitrogen, and gas mixture under normal and harsh conditions.

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“…Each group uses different techniques based on the injection of fluids or other agents to modify certain physical and/or chemical properties, either in the fluids or in the reservoir, improving the oil recovery [6]. According to the EOR aforementioned classification, Low Salinity Waterflooding (LSW) is usually envisioned as a chemical EOR method consisting of injecting low salinity brine that is depleted of divalent cations compared to in-situ brine into the reservoirs, as demonstrated in numerous core-flooding experiments in sandstone rocks [7]. However, due to the complexity of crude oil-brine-rock interactions, the underlying mechanism of LSW has been debated in the literature for the past two decades [8].…”

Section: Introduction 41mentioning

confidence: 99%

Modeling Wettability Alteration During Low Salinity Waterflooding in Sandstone Reservoirs: A Brief Review

Campos¹,

Pessoa²,

Malgaresi³

2021

Blucher Engineering Proceedings

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Low salinity waterflooding (LSW) is an emerging enhanced oil recovery (EOR) technique in which the salinity of the injected water is substantially reduced to improve oil production from sandstone reservoirs. While a significant number of laboratory tests have been carried out to investigate the impact of LSW in the literature, publications on modeling of this process are not so frequent due to the struggling in describe this phenomenon. Different approaches were proposed to model the wettability alteration during LSW, including salting-in, multiple ion exchange (MIE) and double layer expansion (DLE). In this paper a brief literature review is presented, describing the main mechanisms that have been proposed to explain the effects of wettability alteration during LSW, also gathering different mathematical models available in the literature.

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Comprehensive investigation of low-salinity waterflooding in sandstone reservoirs

Cited by 30 publications

References 82 publications

Comprehensive investigation of low salinity waterflooding in carbonate reservoirs

Comprehensive investigation of low salinity waterflooding in carbonate reservoirs

Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions

Modeling Wettability Alteration During Low Salinity Waterflooding in Sandstone Reservoirs: A Brief Review

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