Mechanical properties are some of the most important parameters for understanding well drilling and hydraulic fracturing designs in unconventional reservoir development. As an effective tool, nanoindentation has been used to determine the mechanical properties of rocks at the nanoscale. In this study, the Longmaxi Formation shale samples from the Yibin area of China were collected and analyzed to obtain the multiphase mechanical properties. The mineral compositions and organic geochemistry of the shale samples were studied using X-ray diffraction, energy-dispersive X-ray spectrometry, and a carbon/sulfur analyzer. The pore structures of the shale samples at the micro- and nanoscales were characterized by field-emission scanning electron microscopy. The mechanical parameters of the shale samples, such as the hardness and elastic modulus, were investigated using the nanoindentation method to identify three mineral phases: brittle minerals, soft matters, and complex minerals at the interfaces between brittle minerals and soft matters. The uncertainty characteristics of the mechanical parameters of the three mineral phases were evaluated using the Weibull model, and the factors interfering with the mechanical parameters were analyzed for the different shale samples. The results showed that the brittle minerals had the largest recovered elastic deformations and the smallest residual deformations, while the soft matters had the largest residual deformations and the smallest recovered elastic deformations. The analysis results of the coefficients of variation and the Weibull modulus both confirmed that the scatter of the hardness was higher than that of the elastic modulus because of the uncertain contact area, and the hardness and elastic modulus of the soft matters had the highest uncertainty among the three mineral phases. The elastic modulus increased nonlinearly with increasing hardness according to a power function for the whole shale sample. The elastic modulus and hardness both had a favorable linear relationship with the total organic carbon (TOC) content, illustrating that the TOC content was one of the significant factors that affected the mechanical parameters of the shale samples.

Developing high-efficiency and stable visible-light-driven (VLD) photocatalysts for removal of toxic antibiotics is still a huge challenge at present. Herein, a novel CdS/Bi₂MoO₆ heterojunction with CdS nanoparticles decorated Bi₂MoO₆ microspheres has been obtained by a simple solvothermal-precipitation-calcination method. 1.0CdS/Bi₂MoO₆ has stronger light absorption ability and highest photocatalytic activity with levofloxacin (LEV) degradation efficiency improving 6.2 or 12.6 times compared to pristine CdS or Bi₂MoO₆. CdS/Bi₂MoO₆ is very stable during cycling tests, and no appreciable activity decline and microstructural changes are observed. Results signify that the introduction of CdS could enhance the light absorption ability and dramatically boost the separation of charge carriers, leading to the excellent photocatalytic performance of the heterojunction. This work demonstrates that flower-like CdS/ Bi₂MoO₆ is an excellent photocatalyst for the efficient removal of the LEV antibiotic.

Nanotechnology provides potential benefits for enhanced oil recovery (EOR) in low permeability reservoirs. In this paper, SiO2/P(MBAAm-co-AM) composite nanoparticles were prepared using the distillation precipitation polymerization method. Scanning electron microscopy (SEM), transmission electron microscope (TEM), fourier transform infrared spectroscopy (FT-IR), and thermogravimetric analysis (TGA) were employed to characterize the morphology and microstructure of nanoparticles. The swelling behavior of polymeric nanoparticles in brine was investigated to evaluate the effect of salinity and temperature. Kinetic and thermodynamic analyses were employed to reveal the swelling mechanism. Displacement experiments were performed to investigate their performance in EOR in low permeability reservoirs. The results show that the swelling ratio of SiO2/P(MBAAm-co-AM) composite nanoparticles is higher at low salinity and high temperature, which can be explained by the Flory theory. The swelling process is spontaneous and endothermic, being controlled by physical adsorption involving the diffusion of water molecules, which complies with a first-order kinetics model. The suspension of SiO2/P(MBAAm-co-AM) composite nanoparticles can improve incremental oil recovery from 10.28% to 21.97% with an increase of the swelled particle size from 580 to 1160 nm. It is feasible that core-shell polymeric nanoparticles can be used for EOR in low permeability reservoirs.

Polymeric nanoparticle suspension is a newly developed oil-displacing agent for enhanced oil recovery (EOR) in low-permeability reservoirs. In this work, SiO₂/P(MBAAm-co-AM) polymeric nanoparticles were successfully synthesized by a simple distillation–precipitation polymerization method. Due to the introduction of polymer, the SiO₂/P(MBAAm-co-AM) nanoparticles show a favorable swelling performance in aqueous solution, and their particle sizes increase from 631 to 1258 nm as the swelling times increase from 24 to 120 h. The apparent viscosity of SiO₂/P(MBAAm-co-AM) suspension increases with an increase of mass concentration and swelling time, whereas it decreases as the salinity and temperature increase. The SiO₂/P(MBAAm-co-AM) suspension behaves like a non-Newtonian fluid at lower shear rates, yet like a Newtonian fluid at shear rates greater than 300 s⁻¹. The EOR tests of the SiO₂/P(MBAAm-co-AM) suspension in heterogeneous, low-permeability cores show that SiO₂/P(MBAAm-co-AM) nanoparticles can effectively improve the sweep efficiency and recover more residual oils. A high permeability ratio can result in a high incremental oil recovery in parallel cores. With an increase of the permeability ratio of parallel cores from 1.40 to 15.49, the ratios of incremental oil recoveries (low permeability/high permeability) change from 7.69/4.61 to 23.61/8.46. This work demonstrates that this SiO₂/P(MBAAm-co-AM) suspension is an excellent conformance control agent for EOR in heterogeneous, low-permeability reservoirs. The findings of this study can help to further the understanding of the mechanisms of EOR using SiO₂/P(MBAAm-co-AM) suspension in heterogeneous, low-permeability reservoirs.

The physical-informed neural network (PINN) model can greatly improve the ability to fit nonlinear data with the incorporation of prior knowledge, which endows traditional neural networks with interpretability. Considering the seepage law in the tight reservoir after hydraulic fracturing, a model based on PINN and two-dimensional seepage physical equations was proposed, which can effectively predict the flow field distribution of the tight reservoir after fracturing. Firstly, the dataset was obtained based on physical and numerical models of the tight reservoirs developed by volume fracturing. Furthermore, coupling the neural networks and the two-dimensional unsteady seepage equation, a PINN model was developed to predict the flow field distribution of the tight reservoir. Finally, a systematic study was performed concerning the noise corruption levels, training iterations, and training sample size that affect the prediction results of PINN models. Besides, a comparison between PINN and traditional deep neural networks (DNN) was presented. The results show that the DNN model was not only sensitive to noisy data but also more vulnerable to overfitting as the training iterations increase. In addition, the prediction accuracy cannot be guaranteed when the samples are inadequate (<500). In contrast, the PINN model was less affected by noise and training iterations and thus indicates greater stability. Moreover, the PINN model outperforms the DNN model in the case of inadequate samples attributing to prior knowledge. This study confirms that the adopted PINN model can provide algorithmic support for the accurate prediction of flow field distribution of the tight reservoirs.