Poromechanics of Fractured/Faulted Reservoirs During Fluid Injection Based on Continuum Damage Modeling and Machine Learning

Abbassi, Fethi; Karrech, Ali; Islam, Md. Saiful; Seibi, Abdennour

doi:10.1007/s11053-022-10134-8

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…The "Agency for Toxic Substances and Disease Registry" has ranked Pb as the second-most hazardous substance [1], and the US Environmental Sustainability 2023, 15, 11049. https://doi.org/10.3390/su151411049 https://www.mdpi.com/journal/sustainability Protection Agency also placed Pb on the top priority list. Owing to its carcinogenic nature, non-biodegradability, low mobility, and persistence in soil for years, it becomes a continuous threat to human life [2,3]. Although it is naturally present in soil, ranging from 50 to 500 mg kg −1 [4], an increased concentration is directly accumulated owing to anthropogenic activities, including the recycling of lead-acid batteries, fossil fuel burning, leaded paints, and automobile emissions.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Sustainable Phytoextraction of Lead from Contaminated Soil Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

et al. 2023

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Lead (Pb) is well known for the containment of soil surfaces. In the last few decades, phytoremediation has been the most ideal technology to extract Pb from soil, involving numerous chemical reactions and cost analysis. The aim of this study is to model and to optimize Pb extraction from the contaminated soil via Pelargonium hortorum by comparing two modeling approaches: response surface methodology (RSM) and artificial neural networks (ANNs) with the genetic algorithm (GA). To determine the significance of the proposed solution, in vitro essays were performed to check the Pb tolerance of bacterial strains (NCCP 1844, 1848, 1857, and 1862), followed by the co-application of bacteria and citric acid on a Pb hyperaccumulator (Pelargonium hortorum L.) on Murashige and Skoog (MS) agar medium. Afterwards, a pot culture experiment was performed to optimize Pb extraction competency from Pb-spiked (0 mg kg−1, 500 mg kg−1, 1000 mg kg−1, and 1500 mg kg−1) soil by Pelargonium hortorum L., to which citric acid (5 and 10 mmol L−1) and Microbacterium paraoxydance (1 and 1.5 OD) were applied. Plants were harvested at 30, 60, and 90 day intervals, and they were analyzed for dry biomass and Pb uptake characteristics. The maximum Pb extraction efficiency of 86.0% was achieved with 500 mg kg−1 soil Pb for 60 days. Furthermore, RSM, based on the Box–Behnken design (BBD) and the ANN-based Levenberg–Marquardt Algorithm (LMA), were applied to model Pb extraction from the soil. The significance of the predicted values from RSM and LMA were close to 36.0% and 86.05%, respectively, compared to the laboratory values. The comprehensive evaluation of these findings encouraged the accuracy, reliability, and efficiency of the ANN for the optimization process. Therefore, experimental results showed that ANN is an accurate technique to optimize an integrated phytoremediation system for sustainable Pb removal, besides being environmentally friendly and potentially cost-effective.

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Section: Introductionmentioning

confidence: 99%

Optimizing Sustainable Phytoextraction of Lead from Contaminated Soil Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

et al. 2023

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“…Its oil has low density, low viscosity, low sulfur content, high wax content, and a high solidification point (Wei and Liu, 2019;Zhong et al, 2020;Xu et al, 2021). Besides, this reservoir is a structural layered reservoir with a complex oil-water relationship influenced by fault cutting, along with poor inter-well connectivity (Abbassi et al, 2022). During the initial stage of oil production, crude oil has good properties, a strong self-jet ability, a high production ability, and a long period of low water-cut (Dontsov, 2022;Ma et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Research on imbibition effect of surfactant fracturing fluid in offshore reservoirs with low permeability and high temperature

Liu,

Zou,

Lan

et al. 2023

Front. Energy Res.

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Surfactant imbibition can effectively improve the development effect of fracturing technique. In order to enhance oil recovery, a temperature resistant surfactant was selected using interfacial tension and contact angle testers based on the offshore reservoir conditions of high temperature and low permeability. Experiments on spontaneous and forced imbibition with artificial columnar cores were performed to optimize parameters of imbibition processes during fracturing in high temperature and low permeability reservoirs. Results show that among the three surfactants, amphoteric Gemini surfactant had a stable molecular structure with non-compressible hydrophobic groups which significantly reduced oil-water interfacial tension, enhanced rock wettability, and demonstrated good resistance to high temperatures The maximum recovery increase during amphoteric Gemini surfactant imbibition is 16.22%. The higher the core permeability, the higher the core inside pressure, and the longer the well shut-in time, the greater the forced imbibition effect. A proper well shut-in time and injection volume should be considered when using surfactant solution as fracturing liquid.

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“…Researchers have made much progress in the application of intelligent technologies on many industrial fields. For instance, artificial neural network (ANN) has been used to predict the enhanced oil recovery using fluid injection (Abbassi et al, 2023), to identify parameters of a mechanical ductile damage in sheet metal forming (Abbassi et al, 2013), to monitor and assess environmental quality (Franceschi et al, 2018), etc. However, an overall review upon the intellectualization advance of design technology for FRP composites is still absent.…”

Section: Introductionmentioning

confidence: 99%

Intelligent methods for optimization design of lightweight fiber-reinforced composite structures: A review and the-state-of-the-art

Chen

Zhang

et al. 2023

Front. Mater.

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As the application of lightweight fiber-reinforced composite structures reaches an unprecedented scale in industry, design technology for composite structures becomes crucial for enhancing performance, improving productivity, and reducing cost. In recent years, the rapid development of intelligent technology, such as big data, deep learning, and machine learning, has promoted the development of design technology. However, the current situation and intellectualization of the design technology is not well summarized. This paper reviews the advance in design technologies for fiber-reinforced composite structures, including prediction and optimization methods for composite properties. Then, their intellectualization development is overviewed. Finally, the development trend of intelligent design technologies and intelligent composite structures are discussed. This work can provide a reference for researchers in the related field.

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Poromechanics of Fractured/Faulted Reservoirs During Fluid Injection Based on Continuum Damage Modeling and Machine Learning

Cited by 6 publications

References 42 publications

Optimizing Sustainable Phytoextraction of Lead from Contaminated Soil Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Optimizing Sustainable Phytoextraction of Lead from Contaminated Soil Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Research on imbibition effect of surfactant fracturing fluid in offshore reservoirs with low permeability and high temperature

Intelligent methods for optimization design of lightweight fiber-reinforced composite structures: A review and the-state-of-the-art

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