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This paper presents a theoretical method to simulate gas-water relative permeability for nanoscale porous media utilizing fractal theory. The comparison between the calculation results and experimental data was performed to validate the present model. The result shows that the gas-water relative permeability would be underestimated significantly without interfacial effects. The thinner the liquid film thickness, the greater the liquid-phase relative permeability. In addition, both liquid surface diffusion and gas diffusion coefficient can promote gas-liquid two-phase flow. Increase of liquid surface diffusion prefer to increase liquid-phase permeability obviously as similar as increase of gas diffusion coefficient to increase gas-phase permeability. Moreover, the pore structure will become complicated with the increase of fractal dimension, which would reduce the gas-water relative permeability. This study has provided new insights for development of gas reservoirs with nanoscale pores such as shale.

It is very important to utilize hydraulic fracturing for unconventional reservoir development. Accurate prediction of effective stimulated reservoir volume (SRV) after fracturing facilitates production evaluation. However, the traditional methods to predict SRV are time consuming and the precision cannot fully meet the requirements. To overcome the shortcomings, a new approach was presented using deep learning which includes four procedures. Firstly, the datasets were collected by numerical simulation considering non-Darcy flow characteristics. Additionally, the Branched Deep Neural Network model (B-DNN) was established after data fusion through adding a branch neural network. Then the optimal hyperparameters were obtained after adjusting to satisfy model accuracy and reliability. Finally, the prediction results of B-DNN and convolutional neural network (CNN) and recurrent neural network (RNN) were compared. The results show that the model with Softplus activation function, four hidden layers, and 250 neurons in each layer would have the best calculation results. The proposed model has good agreement with actual field data which can reach 97%. Furthermore, compared with the CNN and RNN models, it is shown that the B-DNN model has considerable prediction accuracy and is less time-consuming. This deep learning model provides new insight for production evaluation in the fractured tight oil reservoir. [ABSTRACT FROM AUTHOR]

Camptothecin (CPT) is an important natural product targeting to Topoisomerase I (Topo I). Despite the high antitumor activity, this compound cannot enter clinical trial owing to its high toxicity and low bio‐availability. In this project, the ring‐closing metathesis (RCM) synthesis strategy was employed to introduce spirocycle functional group in the 5‐position of CPT. Thus, in the catalysis of Grubbs II catalyst, 5,5‐diallylcamptothecin underwent RCM reaction to give 5‐spirocycle CPT compound in 73% yield. [ABSTRACT FROM AUTHOR]

Lithium–sulfur batteries (LSBs) are widely regarded as reliable novel secondary batteries due to their low price and high capacity. Nevertheless, the notorious "shuttle effect" limits the commercialization of LSBs. In order to solve this problem, we fabricated a Ni3S2–Ni/C composite through carbonization, vulcanization and hydrothermal reactions by using a Ni-MOF precursor and applied it as a separator modification layer to enhance the electrochemical properties of LSBs. To further increase the conductivity of the material, a small amount of GO was added during the experiment. The prepared material was also used as separator modified coating material to optimize the electrochemical performance of LSBs. The as prepared Ni3S2–Ni/C(GO) composite shows good conductivity and has a superior porous structure and abundant active sites. Lithium polysulfides (LPs) can be physically confined and chemically adsorbed, what is more, the Ni and Ni3S2 active sites enable fast conversion of LPs which further optimizes the rate performance. From the cycle performance measurement, the initial discharge specific capacity of the Ni3S2–Ni/C(GO) modified separator battery is found to be 1263.4, 1181.5, 1090.6, and 840.3 mA h g−1 at 0.05, 0.1, 0.3 and 0.5C, respectively. After 400 charge/discharge cycles at 0.5C, the capacity remains at 483.6mA h g−1 with a capacity retention ratio of 57.56%. [ABSTRACT FROM AUTHOR]