Expandable proppants to moderate production drop in hydraulically fractured wells

Santos, Livio; Taleghani, Arash Dahi; Li, Guoqiang

doi:10.1016/j.jngse.2018.04.026

Cited by 43 publications

(11 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shape memory polymers (SMPs) can store a prescribed shape for a long time and recover them by specific external trigger, for example, heat. SMPs have the potential to be deployed in various engineering structures and devices, including, but are not limited to, automotive (Lendlein and Kelch, 2002;Hu et al, 2012;Meng and Li, 2013;Zhao et al, 2015), aerospace (Leng et al, 2008;Liu et al, 2014), marine time (Ma et al, 2021), oil and gas (Taleghani et al, 2017;Santos et al, 2018;Mansour et al, 2019;Santos et al, 2021;Tabatabaei et al, 2021), geothermal (Elhag et al, 2021;Magzoub et al, 2021;Mohamed et al, 2022), civil engineering (Li and Xu, 2011a;Li et al, 2013b;Li et al, 2015;Ouyang and Li, 2009), and biomedical industries (Lendlein and Langer, 2002;Small et al, 2010). The same as any engineering materials, constitutive laws are essential to design and manufacture SMPs into engineering structures and devices because constitutive laws are a key component in forming the governing differential equations.…”

Section: Introductionmentioning

confidence: 99%

Insight in thermomechanical constitutive modeling of shape memory polymers

Shojaei

Xu²,

Yan

et al. 2022

Front. Mech. Eng.

Self Cite

View full text Add to dashboard Cite

Shape memory polymers (SMPs) are a new member of the smart materials family. SMPs have found wide applications or potential applications in almost all manmade structures and devices. In order to better design smart structures and devices using SMPs, thermomechanical constitutive modeling is essential. In this insight paper, we will focus on presenting several multi-length-scale and multi-physics modeling frameworks, including the thermodynamics consistent model, elasto-viscoplastic model, statistical mechanics model, and phase evaluation law model. The SMPs modeled will include amorphous one-way shape memory polymers, semicrystalline one-way shape memory polymers, semicrystalline two-way shape memory polymers, and functional and mechanical damage effects on SMPs. Finally, we will give some in-depth perspectives on future development in this area of study.

show abstract

Section: Introductionmentioning

confidence: 99%

Insight in thermomechanical constitutive modeling of shape memory polymers

Shojaei

Xu²,

Yan

et al. 2022

Front. Mech. Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Meanwhile, in the past decades, due to the 3D printability, good mechanical properties, and an excellent shape memory effect (SME), ultraviolet (UV)-curable thermoset shape memory polymers (TSMPs) have found wide applications in actuators, proppants, flame retardancy, sealants, self-healing, , and many more . By using UV-curable TSMPs and 3D printing via digital light processing or direct ink writing, structures with complex shapes and functionalities can be manufactured such as deployable structures and soft robotics .…”

Section: Introductionmentioning

confidence: 99%

“…By utilizing these databases, investigators have already achieved a lot of success. For example, through Pubchem, Butkiewicz et al assembled multiple data sets to establish a new benchmark platform for drug target proteins; 24 26 Meanwhile, in the past decades, due to the 3D printability, good mechanical properties, and an excellent shape memory effect (SME), ultraviolet (UV)-curable thermoset shape memory polymers (TSMPs) have found wide applications in actuators, 27 proppants, 28 flame retardancy, 29 sealants, 30 selfhealing, 31,32 and many more. 33 By using UV-curable TSMPs and 3D printing 34 via digital light processing or direct ink writing, structures with complex shapes and functionalities can be manufactured such as deployable structures 35 and soft robotics.…”

Section: Introductionmentioning

confidence: 99%

From Drug Molecules to Thermoset Shape Memory Polymers: A Machine Learning Approach

Yan

Feng

2021

ACS Appl. Mater. Interfaces

Self Cite

View full text Add to dashboard Cite

Ultraviolet (UV)-curable thermoset shape memory polymers (TSMPs) with high recovery stress but mild glass transition temperature (T g ) are highly desired for 3D/4D printing lightweight load-bearing structures and devices. However, a bottleneck is that high recovery stress usually means high T g . For a few TSMPs with high recovery stress, their T g values are close to the decomposition temperature, and thus, the shape memory effect cannot be triggered safely and effectively. While machine learning (ML) has served as a useful tool to discover new materials and drugs, the grand challenge of using ML to discover new TSMPs persists in the very limited data available. Here, we report an enhanced ML approach by combining the transfer learning− variational autoencoder with a weighted-vector combination method. By learning a large data set with drug molecules in a pretraining process, we were able to effectively map the TSMPs to a hidden space that is much closer to a Gaussian distribution. Through this approach, we created a large compositional space and were able to discover five new types of UV-curable TSMPs with desired properties, one of which was validated by the experiments. Our contribution includes (1) representing the features of TSMPs by drug molecules to overcome the barrier of a limited training data set and (2) developing a ML framework that is able to overcome the barrier of mapping the molar ratio information. It is shown that this approach can effectively learn TSMP features by utilizing the relatedness between the data-scarce (and biased) TSMP target and data-abundant drug source, and the result is much more accurate and more robust than the benchmark set by the support vector machine method using direct label encoding and Morgan encoding. Therefore, it is believed that this framework is a state-of-the-art study in the TSMP field. This study opens new opportunities for discovering not only new TSMPs but also other thermoset polymers.

show abstract

“…11 Conductivity studies performed under in situ conditions by Penny have shown polymer loading, type of crosslinker, and breaker system dramatically affect the conductivity of a proppant pack. 12 Other proppants with different functions or performances have been developed for different oil and gas reservoirs, such as the low crushing rate proppant, 13 oil-wet proppant, 14 expandable proppant, 15 and non-spherical proppant. 16 Existing proppants have addressed some problems, but the density and suspension ability do not meet the requirement of network fracturing.…”

Section: Introductionmentioning

confidence: 99%