Fundamental investigation of an environmentally-friendly surfactant agent for chemical enhanced oil recovery

Madani, Mohammad; Zargar, Ghasem; Takassi, Mohammad Ali; Daryasafar, Amin; Wood, David A.; Zhang, Zhien

doi:10.1016/j.fuel.2018.10.105

Cited by 104 publications

(56 citation statements)

References 32 publications

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“…The peak at 1445 cm −1 could be attributed to vibration of the amino group which is in resonance with the attached amine group. The C-O stretching band was observed at 1057 cm −1 and a peak appeared at 729 cm −1 due to the N-H out of plane bending (Takassi et al 2017;Madani et al 2019). Additionally, Fig.…”

Section: Spectral Characterization Of Synthesized Surfactantsmentioning

confidence: 87%

“…The resonance of these protons was observed at around 1.81-1.39 ppm. The methyl group of the hydrocarbon tail was also observed as a triplet peak at 0.90 ppm (Takassi et al 2017;Madani et al 2019). In the H-NMR spectrum of the developed l-Cys, the carboxyl proton was Fig.…”

Section: Spectral Characterization Of Synthesized Surfactantsmentioning

confidence: 94%

“…12a) between kerosene and l-Arg solutions reduced from 144° to 70°. It is also clear that a rapid drop in the value of contact angle occurred with increasing the l-Arg concentration up to 2000 ppm (from 144° to 78°), and this decline continued (Takassi et al 2017;Madani et al 2019). b L-Cys (Ramshini 2017) very smoothly to 70° at 10,000 ppm concentration.…”

Section: Contact Anglementioning

confidence: 97%

“…In order to synthesize l-Arg with accordance with Takassi et al 2017and Madani et al (2019), 5 mL of lauroyl chloride and 3.0 mg of arginine were mixed with 100 mL of methanol in a 250-mL beaker. After mixing thoroughly for 2 h, the solution was transferred into a dry round-bottom flask and was mixed for 24 h by magnetic stirring at the reflux temperature when a white precipitate was observed in the bottom of the flask.…”

Section: L-arg Synthesismentioning

confidence: 99%

“…After the purification of the produced material by crystallization in a distilled water-ethanol mixture, sufficient crystallized powder was extracted for melting point, nuclear magnetic resonance ( 1 H-NMR) and Fourier transform infrared (FT-IR) spectra analyses (Takassi et al 2017;Ramshini 2017;Madani et al 2019). The melting point of the purified synthesized l-Arg surfactant was measured at 185-186 °C, which is a good indicator for the synthesis process, since the formation of the target surfactant has effectively decreased the melting point of arginine.…”

Section: Characterization Of Amino-acid Surfactantsmentioning

confidence: 99%

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Experimental investigation into l-Arg and l-Cys eco-friendly surfactants in enhanced oil recovery by considering IFT reduction and wettability alteration

et al. 2019

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Surfactant flooding is an important technique used to improve oil recovery from mature oil reservoirs due to minimizing the interfacial tension (IFT) between oil and water and/or altering the rock wettability toward water-wet using various surfactant agents including cationic, anionic, non-ionic, and amphoteric varieties. In this study, two amino-acid based surfactants, named lauroyl arginine (l-Arg) and lauroyl cysteine (l-Cys), were synthesized and used to reduce the IFT of oil-water systems and alter the wettability of carbonate rocks, thus improving oil recovery from oil-wet carbonate reservoirs. The synthesized surfactants were characterized using Fourier transform infrared spectroscopy and nuclear magnetic resonance analyses, and the critical micelle concentration (CMC) of surfactant solutions was determined using conductivity, pH, and turbidity techniques. Experimental results showed that the CMCs of l-Arg and l-Cys solutions were 2000 and 4500 ppm, respectively. It was found that using l-Arg and l-Cys solutions at their CMCs, the IFT and contact angle were reduced from 34.5 to 18.0 and 15.4 mN/m, and from 144° to 78° and 75°, respectively. Thus, the l-Arg and l-Cys solutions enabled approximately 11.9% and 8.9% additional recovery of OOIP (original oil in place). It was identified that both amino-acid surfactants can be used to improve oil recovery due to their desirable effects on the EOR mechanisms at their CMC ranges.

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Section: Spectral Characterization Of Synthesized Surfactantsmentioning

confidence: 87%

Section: Spectral Characterization Of Synthesized Surfactantsmentioning

confidence: 94%

Section: Contact Anglementioning

confidence: 97%

Section: L-arg Synthesismentioning

confidence: 99%

Section: Characterization Of Amino-acid Surfactantsmentioning

confidence: 99%

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Experimental investigation into l-Arg and l-Cys eco-friendly surfactants in enhanced oil recovery by considering IFT reduction and wettability alteration

et al. 2019

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Bio‐enhanced oil recovery (BEOR) methods: All‐important review of the occasions and challenges

Maleki,

Kazemzadeh,

Dehghan Monfared

et al. 2024

Can J Chem Eng

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Bio‐enhanced oil recovery (BEOR) is an advanced and innovative approach in the oil industry that could be a potential solution to increase oil production from existing reservoirs using technologies based on biological methods. Nevertheless, there has been a lack of comprehensive reviews that elucidate the various aspects and potential of different BEOR methods and processes. This review summarizes the recent progress of various methods employed in BEOR, exploring their applications and highlighting their distinct advantages. BEOR employs different techniques, including microorganisms and biomicrobes microbial enhanced oil recovery (MEOR), enzymes, biopolymers, bionanomaterials, alkaline, and biosurfactants to increase oil recovery. Microorganisms contribute significantly to BEOR through metabolic processes that result in the production of gases and acids. The role of enzymes is to enhance the fluid flow and thereby facilitate oil production. Biosurfactants reduce the interfacial tension (IFT) between oil and water and mobilize the trapped oil. Biopolymers are obtained from biological sources such as plants, microorganisms, and algae. Biopolymers can interact with oil, which is well‐used in the process of EOR. Bionano processes represent a fusion of biology and nanotechnology, integrate the advantages of both microorganisms and nanoparticles, and provide a synergic effect for EOR. The BEOR revealed an attractive potential to be an effective approach to maximizing the oil recovery considering environmental, economic productivity, and sustainability issues. Also, this review encourages further studies and development in this field for fully exploiting BEOR's capacity and meeting the ever‐increasing needs of energy resources with a sustainable viewpoint.

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Advanced EOR screening methodology based on LightGBM and random forest: A classification problem with imbalanced data

Seyyedattar,

Afshar,

Zendehboudi

et al. 2024

Can J Chem Eng

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In an unstable oil market with volatile prices due to various natural and geopolitical factors, it is crucial for oil‐producing companies to enhance the value of their assets by improving the recovery factors of petroleum reservoirs. Primary recovery through natural depletion or artificial lift and secondary recovery using waterflooding and immiscible gas injection typically recover no more than 10%–40% of the available reserves. A significant portion of the hydrocarbons remain unproduced if enhanced oil recovery (EOR) methods are not implemented. EOR projects are extremely costly, complex, and usually have long lead times from the decision‐making and design phases to pilot and full‐field implementations. Therefore, oil and gas operator companies need reliable insights into the best possible EOR options from the early stages of any field development planning. Since screening potential EOR choices is the first step in deciding future production scenarios, a smart EOR screening tool can add significant value by streamlining the EOR decision‐making process. In this study, we developed an EOR screening tool based on two advanced machine learning classification algorithms, random forest and light gradient boosting machine (LightGBM). These tree‐based ensemble learning classifiers were trained on an extensive dataset of 1384 worldwide EOR implementations, encompassing various reservoir conditions and reservoir rock and fluid properties as the feature space, to predict the EOR type as the class label. Considering EOR screening as a classification problem, an essential aspect of model development would be addressing the data imbalance of EOR datasets. To tackle this issue, the adaptive synthetic (ADASYN) sampling method was used to reduce classification bias by oversampling the training sets to achieve uniform class distributions. We designed an iterative model development procedure in which the classifiers were trained and tested on various training and test subsets split by stratified random sampling. For each classifier, the classification results at each iteration were used to build the confusion matrix and calculate model evaluation metrics (accuracy, precision, recall, and F1–score), which were then averaged over all independent runs to provide a fair assessment of classification performance. Moreover, binary receiver operating characteristic (ROC) curves were used to evaluate the classifier predictions and improvements obtained by oversampling. The results showed that both random forest and LightGBM classifiers made accurate class predictions, with LightGBM achieving slightly better classification performance in each modelling scenario (with or without oversampling). In both cases, the oversampling of the training dataset resulted in significant improvement of the classifiers, as evidenced by higher values of the evaluation metrics, leading to considerably more accurate EOR type predictions; specifically, oversampling boosted the prediction accuracy of the random forest model from 78.3% to 89.5% and the LightGBM model from 77.5% to 90.2%. Additionally, feature importance rankings provided valuable insights into which input variables had the greatest impact on model development.

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Fundamental investigation of an environmentally-friendly surfactant agent for chemical enhanced oil recovery

Cited by 104 publications

References 32 publications

Experimental investigation into l-Arg and l-Cys eco-friendly surfactants in enhanced oil recovery by considering IFT reduction and wettability alteration

Experimental investigation into l-Arg and l-Cys eco-friendly surfactants in enhanced oil recovery by considering IFT reduction and wettability alteration

Bio‐enhanced oil recovery (BEOR) methods: All‐important review of the occasions and challenges

Advanced EOR screening methodology based on LightGBM and random forest: A classification problem with imbalanced data

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