Nanoparticles assisted polymer flooding: Comprehensive assessment and empirical correlation

Salem, Khalaf G.; Tantawy, Mahmoud A.; Gawish, Ahmed A.; Gomaa, Sayed; El-hoshoudy, A.N.

doi:10.1016/j.geoen.2023.211753

Cited by 21 publications

(9 citation statements)

References 94 publications

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“…The culture medium was collected at different time points (1,2,4,6, and 8 days) under sterile conditions. Subsequently, the collected medium was centrifuged at 1000 rpm and 4 • C for 10 min.…”

Section: Bacteria Growth Curvementioning

confidence: 99%

“…The globally increasing demand for crude oil has prompted the oil industry to enhance oil production from primary, secondary, and tertiary recovery methods to fulfill the global requirement. Among the most mature, effective, and successful chemical-enhanced oil recovery (CEOR) methods is polymer flooding [1][2][3], which involves the injection of a polymer solution into the reservoir with a suitable concentration to diminish the mobility ratio of the water-oil system (M) as much as possible, and hence increase the sweep efficiency [4][5][6][7]. However, frequent injections of polymers create more extensive pore paths, resulting in the quick movement of injection fluid out of the oil-bearing strata.…”

Section: Introductionmentioning

confidence: 99%

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Research on the Functional Microbe Activation System in a Post-Polymer Flooded Reservoir

Liu,

Wang,

Wei

et al. 2024

Processes

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Further exploitation of the residual oil underground in post-polymer flooded reservoirs is attractive and challenging. Microbial-enhanced oil recovery (MEOR) is a promising strategy to enhance the recovery of residual oil in post-polymer flooded reservoirs. Identifying and selectively activating indigenous microorganisms with oil displacement capabilities is an urgent requirement in the current design of efficient microbial-enhanced oil recovery technologies. This study combines high-throughput sequencing with functional network analysis to identify the core functional microbes within the reservoirs. Concurrently, it devises targeted activation strategies tailored to oligotrophic conditions through an analysis of environmental factor influences. The feasibility of these strategies is then validated through physical simulation experiments. With nutrient stimulation, the overall diversity of microorganisms decreases while the abundance of functional microorganisms increases. The core displacement results showed that the oil recovery factor increased by 3.82% on the basis of polymer flooding. In summary, this research has established a system for the efficient activation of functional microorganisms under oligotrophic conditions by utilizing bioinformatics, network analysis, and indoor simulation systems. This achievement will undoubtedly lay a solid foundation for the practical implementation of microbial enhancement techniques in the field.

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Section: Bacteria Growth Curvementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on the Functional Microbe Activation System in a Post-Polymer Flooded Reservoir

Liu,

Wang,

Wei

et al. 2024

Processes

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“…In recent years, PNIPAm has been extensively employed in the development of next-generation bioelectronic interfaces. 25 The thermoresponsive deformation of PNIPAm hydrogels enable control of their bending and unbending, enabling them to grasp objects and function as soft robots. Soft robots have special advantages compared to traditional hard robots, which are made of stiff materials such as metals and ceramics and are powered by electrical, hydraulic, or pneumatic sources.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, PNIPAm has been extensively employed in the development of next-generation bioelectronic interfaces. 25…”

Section: Introductionmentioning

confidence: 99%

Effect of functionalized multiwalled carbon nanotubes on the mechanical, swelling and viscoelastic properties of gum ghatti-cl-poly(NIPAm) hydrogels

Dave,

Macwan

2024

New J. Chem.

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“…In this regard, soft computations and machine learning gained incremental spread in petroleum and chemical engineering issues 12 , 30 – 33 . These approaches include various predictive models such as ANN, GP, ANFIS, SVM, and their optimized hybrids 1 , 34 – 36 . ANNs used to outperform multivariate nonlinear regression processes 37 .…”

Section: Introductionmentioning

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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Nanoparticles assisted polymer flooding: Comprehensive assessment and empirical correlation

Cited by 21 publications

References 94 publications

Research on the Functional Microbe Activation System in a Post-Polymer Flooded Reservoir

Research on the Functional Microbe Activation System in a Post-Polymer Flooded Reservoir

Effect of functionalized multiwalled carbon nanotubes on the mechanical, swelling and viscoelastic properties of gum ghatti-cl-poly(NIPAm) hydrogels

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

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