Summary Although researchers have applied many methods to history matching, such as Monte Carlo methods, ensemble-based methods, and optimization algorithms, history matching fractured reservoirs is still challenging. The key challenges are effectively representing the fracture network and coping with large amounts of reservoir-model parameters. With increasing numbers of fractures, the dimension becomes larger, resulting in heavy computational work in the inversion of fractures. This paper proposes a new characterization method for the multiscale fracture network, and a powerful dimensionality-reduction method by means of an autoencoder for model parameters. The characterization method of the fracture network is dependent on the length, orientation, and position of fractures, including large-scale and small-scale fractures. To significantly reduce the dimension of parameters, the deep sparse autoencoder (DSAE) transforms the input to the low-dimensional latent variables through encoding and decoding. Integrated with the greedy layer-wise algorithm, we set up a DSAE and then take the latent variables as optimization variables. The performance of the DSAE with fewer activating nodes is excellent because it reduces the redundant information of the input and avoids overfitting. Then, we adopt the ensemble smoother (ES) with multiple data assimilation (ES-MDA) to solve this minimization problem. We test our proposed method in three synthetic reservoir history-matching problems, compared with the no-dimensionality-reduction method and the principal-component analysis (PCA). The numerical results show that the characterization method integrated with the DSAE could simplify the fracture network, preserve the distribution of fractures during the update, and improve the quality of history matching naturally fractured reservoirs.

To study the effects of water stress on the fluorescence parameters and photosynthetic characteristics of rice under drip irrigation and mulching, so as to determine the response mechanisms to water stress during the tillering stage. A two-year trial was carried out at Shihezi University, China. Three water gradients were investigated. The results showed that the chlorophyll content (a + b), photosynthetic rate (Pn), and leaf area index (LAI) decreased with decreasing soil moisture content at the tillering stage. The chlorophyll content (a + b) and Pn in the flooding irrigation (CK) treatment were significantly higher than those in the stress treatments, and the chlorophyll content (a + b) and Pn in the W1 and W2 treatments were significantly lower than those in the other treatments. The maximum LAI of the CK, W1, and W2 treatments were similar, while the W3 produced lower values; stress treatment improved the ability of tillering in the early and middle stages, while the decrease in soil water content in the tillering stage resulted in a decrease in the final tillering rate; drought stress in the tillering stage resulted in decreased rice yields. The yield of the W1 and W2 treatments were similar, while that of the W3 treatment was seriously reduced. The main reasons for the reduction in yield was the significant decrease in the number of effective panicles, the seed setting rate, and a decrease in the 1000-grains weight. Water consumption in the stress treatments decreased by 51.69%–58.78% compared to the CK treatment; water-use efficiency in the CK treatment was only 0.25 kg·m−3, and the water-use efficiency of the stress treatments increased by 40%–72%. We should make full use of the compensation effect of drought stress in the water regulation of drip irrigation in covered rice and adopt the water control measure of the W2 treatment in the tillering stage. These measures are conducive to improving water-use efficiency and achieving the goal of high quality, high yield, and high efficiency.

Summary History matching is a typical inverse problem that adjusts the uncertainty parameters of the reservoir numerical model with limited dynamic response data. In most situations, various parameter combinations can result in the same data fit, termed as nonuniqueness of inversion. It is desirable to find as many global or local optima as possible in a single optimization run, which may help to reveal the distribution of the uncertainty parameters in the posterior space, which is particularly important for robust optimization, risk analysis, and decision making in reservoir management. However, many factors, such as the nonlinearity of inversion problems and the time-consuming numerical simulation, limit the performance of most existing inverse algorithms. In this paper, we propose a novel data-driven niching differential evolution algorithm with adaptive parameter control for nonuniqueness of inversion, called DNDE-APC. On the basis of a differential evolution (DE) framework, the proposed algorithm integrates a clustering approach, niching technique, and local surrogate assistant method, which is designed to balance exploration and convergence in solving the multimodal inverse problems. Empirical studies on three benchmark problems demonstrate that the proposed algorithm is able to locate multiple solutions for complex multimodal problems on a limited computational budget. Integrated with convolutional variational autoencoder (CVAE) for parameterization of the high-dimensional uncertainty parameters, a history matching workflow is developed. The effectiveness of the proposed workflow is validated with heterogeneous waterflooding reservoir case studies. By analyzing the fitting and prediction of production data, history-matched realizations, the distribution of inversion parameters, and uncertainty quantization of forecasts, the results indicate that the new method can effectively tackle the nonuniqueness of inversion, and the prediction result is more robust.

Plants experience a wide array of environmental stimuli, some of which are frequent occurrences of cold weather, which have priming effects on agricultural production and agronomic traits. DNA methylation may act as an epigenetic regulator for the cold response of Tartary buckwheat (Fagopyrum tataricum). Combined with long-term field observation and laboratory experiments, comparative phenome, methylome, and transcriptome analyses were performed to investigate the potential epigenetic contributions for the cold priming of Tartary buckwheat variety Dingku1. Tartary buckwheat cv. Dingku1 exhibited low-temperature resistance. Single-base resolution maps of the DNA methylome were generated, and a global loss of DNA methylation was observed during cold responding in Dingku1. These sites with differential methylation levels were predominant in the intergenic regions. Several hundred genes had different DNA methylation patterns and expressions in different cold treatments (cold memory and cold shock), such as CuAO, RPB1, and DHE1. The application of a DNA methylation inhibitor caused a change of the free lysine content, suggesting that DNA methylation can affect metabolite accumulation for Tartary buckwheat cold responses. The results of the present study suggest important roles of DNA methylation in regulating cold response and forming agronomic traits in Tartary buckwheat.

Drought stress occurs frequently and severely as a result of global climate change, and it exerts serious effects on plants. 5-Aminolevulinic acid (5-ALA) plays a crucial role in conferring abiotic stress tolerance in plants. To enhance the drought tolerance of turfgrass and investigate the effects of 5-ALA on antioxidant metabolism and gene expression under drought stress conditions, exogenous 5-ALA was applied by foliar spraying before Kentucky bluegrass (Poa pratensis L.) seedlings were exposed to drought [induced by 10% polyethylene glycol (PEG)] stress for 20 days. 5-ALA pretreatment increased turf quality (TQ) and leaf relative water content (RWC) while reducing reactive oxygen species (ROS) production including HO content and O generation rate, lipoxygenase (LOX) activity, and malondialdehyde (MDA) content under drought stress. 5-ALA pretreatment maintained ascorbate (AsA) and glutathione (GSH) contents and the ASA/DHA and GSH/GSSG ratios at high levels, and it enhanced the activities of superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), glutathione peroxidase (GPX), dehydroascorbate reductase (DHAR), and glutathione reductase (GR), which are crucial for scavenging drought-induced ROS. In addition, 5-ALA upregulated the relative expression levels of Cu/ZnSOD, APX, GPX, and DHAR but downregulated those of CAT and GR under drought stress. These results indicated that the application of 5-ALA might improve turfgrass quality and promote drought tolerance in Kentucky bluegrass through reducing oxidative damage and increasing non-enzyme antioxidant levels and antioxidant enzyme activity at transcriptional and posttranscriptional levels.