Insights to surfactant huff-puff design in carbonate reservoirs based on machine learning modeling

Yao, Ya; Qiu, Yue; Cui, Yue; Wei, Mingzhen; Bai, Baojun

doi:10.1016/j.cej.2022.138022

Cited by 21 publications

(9 citation statements)

References 80 publications

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“…The oil droplets collide with each other as they move forward, and the oil droplets converge and form oil bands. As the oil bands move, more oil beads converge and form larger oil bands, promoting the further movement of residual oil to the producing well [3].…”

Section: Surfactant Enhanced Huff and Puff Mechanism (1) Reduce The O...mentioning

confidence: 99%

Numerical simulation of enhanced throughput of surfactant in Lamadian oilfield

Han

2024

E3S Web Conf.

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Based on the actual data of Lamadian oilfield in terms of geological structure, reservoir fluid physical properties and production dynamics, a numerical simulation geological model of CMG STARS chemical flooding module is established, which is consistent with the static and dynamic characteristics of the oil reservoir. In order to study the effect of enhanced throughput of surfactants, based on the mechanism of enhanced throughput of surfactants, the numerical model was established by defining parameters such as oil-water interfacial tension at different concentrations, residual oil saturation, oil-water relative permeability curve, capillary pressure, isotonic point change and surfactant diffusion coefficient in the formation, etc. The numerical simulation of surfactant enhanced throughput was carried out to optimize the surfactant enhanced throughput parameters in Lamadian oilfield, and the sensitivity sequence of each injection and production parameter to the surfactant enhanced throughput and oil was determined. The final recommended injection and production parameters are surfactant injection amount (10000m3), injection concentration (0.5%) and brazing time (2d). It is predicted that the cumulative oil increase can reach 1310m3, the initial daily fluid production is controlled at 80m3/d, the daily oil production can be increased by 10m3/d, and the water cut can be reduced by 10%.

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Section: Surfactant Enhanced Huff and Puff Mechanism (1) Reduce The O...mentioning

confidence: 99%

Numerical simulation of enhanced throughput of surfactant in Lamadian oilfield

Han

2024

E3S Web Conf.

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“…In this context, machine learning (ML) models are increasingly attracting researchers' attention with the rapid development of computational capacity and increase of available data. Various ML models such as artificial neural network (ANN), support vector machine, and random forest have been widely used in the modeling and predicting of many fields including physiochemical property, 41 biomass gasification, 42 molecular drug discovery, 43 catalysis, 44 surfactant, 45 etc. As ML models especially ANN are more complex than traditional models such as linear regression, they are difficult to be interpreted fundamentally and may be considered "black box" models.…”

Section: Introductionmentioning

confidence: 99%

Micromixing Performance and Residence Time Distribution in a Miniaturized Magnetic Reactor: Experimental Investigation and Machine Learning Modeling

Chen

Deng

Luo

2023

Ind. Eng. Chem. Res.

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Miniaturization of mixers is a hot topic in process intensification, but efficient mixing at operational conditions with relatively large viscosity μ and large flow rate ratio R between two mixed solutions is still a great challenge for micromixers. Herein, a miniaturized magnetic reactor (MMR) is developed, and its micro- and macromixing performances are characterized by the Villermaux/Dushman reaction and residence time distribution experiments, respectively. The results show that the miniaturization of the premixing unit and the stirred unit is vital. By arranging the stirred units in series, the MMR is effective in reducing axial dispersion, intensifying mixing at operational conditions with large μ and R and achieving efficient energy utilization without introducing extra energy input. The micromixing time t m in the optimized MMR ranges from 0.24 to 1.42 ms when 4.72 mPa·s < μ < 73.30 mPa·s and 8 < R < 20, indicating superior micromixing performance. Moreover, machine learning models are established and compared for predicting t m with excellent accuracy and guiding mixer design, and the SHAP method is adopted for model interpretation.

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“…Under cold recovery conditions, the occurrence of oily foam represents a significant production mechanism in heavy oil reservoirs . The principles of oily foam have been successfully applied in the development of heavy oil reservoirs in Venezuela and Canada. − Oily foam refers to the dispersed gas–liquid two-phase flow phenomenon that occurs during the production process in heavy oil reservoirs. When the reservoir pressure falls below the bubble point pressure, numerous tiny gas bubbles form in the crude oil.…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of Anionic-CO₂-Soluble Surfactant Di-(2-ethylhexyl) Sodium Sulfosuccinate-Assisted Oily Foam Based on Statistical Analysis of Bubble Dynamic Characteristics

Xu,

Wang,

et al. 2023

Langmuir

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Currently, oily foam stability in CO 2 injection for heavy oil recovery exhibits inadequacies that considerably constrain its extensive application. Some scholars have conducted research demonstrating that CO 2 -soluble surfactants can assist in inducing heavy oil to form oil-based foams (oily foam). In this study, stability tests for the oily foam were conducted at different surfactant concentrations using a visualized PVT cell. Oily foam stability was assessed by calculating the comprehensive foam index (S) and analyzing the bubble images. The research indicates that AOT can effectively reduce the interfacial tension between oil and gas. At a concentration of 0.1 wt % AOT, the interfacial tension can be effectively reduced from 1.75 to 1.14 mN/m. The concentration of 0.3 wt % AOT represents a turning point, with an S of 16 101.7 mL•min. Beyond this concentration, the increase in S becomes less pronounced. As the concentration of CO 2soluble surfactant is increased from 0.1 to 0.5 wt %, the average bubble radius decreases from 2.74 to 0.43 mm, while the number of bubbles per unit area increases from 5.56 to 81.1 per cm 2 . With an increasing concentration of the CO 2 -soluble surfactant, the system generates more and smaller gas bubbles within the oily foam, resulting in a slower bubble coalescence. The findings of this study are poised to play a pivotal role in enhancing heavy oil recovery efficiency.

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Insights to surfactant huff-puff design in carbonate reservoirs based on machine learning modeling

Cited by 21 publications

References 80 publications

Numerical simulation of enhanced throughput of surfactant in Lamadian oilfield

Numerical simulation of enhanced throughput of surfactant in Lamadian oilfield

Micromixing Performance and Residence Time Distribution in a Miniaturized Magnetic Reactor: Experimental Investigation and Machine Learning Modeling

Stability Analysis of Anionic-CO₂-Soluble Surfactant Di-(2-ethylhexyl) Sodium Sulfosuccinate-Assisted Oily Foam Based on Statistical Analysis of Bubble Dynamic Characteristics

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Insights to surfactant huff-puff design in carbonate reservoirs based on machine learning modeling

Cited by 21 publications

References 80 publications

Numerical simulation of enhanced throughput of surfactant in Lamadian oilfield

Numerical simulation of enhanced throughput of surfactant in Lamadian oilfield

Micromixing Performance and Residence Time Distribution in a Miniaturized Magnetic Reactor: Experimental Investigation and Machine Learning Modeling

Stability Analysis of Anionic-CO2-Soluble Surfactant Di-(2-ethylhexyl) Sodium Sulfosuccinate-Assisted Oily Foam Based on Statistical Analysis of Bubble Dynamic Characteristics

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Product

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Stability Analysis of Anionic-CO₂-Soluble Surfactant Di-(2-ethylhexyl) Sodium Sulfosuccinate-Assisted Oily Foam Based on Statistical Analysis of Bubble Dynamic Characteristics