Particles migrating and plugging mechanism in loosen sandstone heavy oil reservoir and the strategy of production with moderate sanding

Fine rock particles is easy to be suspended and carried in loosen sandstone heavy oil reservoir due to the higher density and viscosity of heavy oil. The sand particles settle down, bridge and clog in pore and throat, as the result, the filtration resistance in reservoir will be redistributed. It significantly impacts on the well productivity. In this paper, the process of sand particles transporting and clogging in tunnels of rock is observed utilizing a microscopic visualization model with the unconsolidated sandpack. Furthermore, the mechanism of fine particles migration and clogging and the effects to percolation capacity of porous medium is investigated through the dynamic permeability changes in the weak-consolidated sandpack tube is monitored under different conditions of particles suspended fluid injection. It is shown that the performance of permeability decline with particles migration is affected by the size and sorting of mobile particles and throats, concentration of suspended particles, total amount of particles and the pressure drawdown or fluid flowing velocity, the maximum permeability reduction and the clogging transition time is determined by the minimum size of bridging particles. As a field application example, the strategy of production with moderate sanding in loosen sandstone heavy oil reservoir is discussed at the end of this pape

Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank

Unbiased learning to rank (ULTR) aims to mitigate various biases existing in user clicks, such as position bias, trust bias, presentation bias, and learn an effective ranker. In this paper, we introduce our winning approach for the "Unbiased Learning to Rank" task in WSDM Cup 2023. We find that the provided data is severely biased so neural models trained directly with the top 10 results with click information are unsatisfactory. So we extract multiple heuristic-based features for multi-fields of the results, adjust the click labels, add true negatives, and re-weight the samples during model training. Since the propensities learned by existing ULTR methods are not decreasing w.r.t. positions, we also calibrate the propensities according to the click ratios and ensemble the models trained in two different ways. Our method won the 3rd prize with a DCG@10 score of 9.80, which is 1.1% worse than the 2nd and 25.3% higher than the 4th.Comment: 5 pages, 1 figure, WSDM Cup 202

Latent TGF-beta binding protein-1 plays an important role in craniofacial development

Objective: This study aims to replicate the phenotype of Ltbp1 knockout mice in zebrafish, and to address the function of LTBP1 in craniofacial development. Methods: Whole mount in situ hybridization (WISH) of ltbp1 was performed at critical periods of zebrafish craniofacial development to explore the spatial-temporal expression pattern. Furthermore, we generated morpholino based knockdown model of ltbp1 to study the craniofacial phenotype. Results: WISH of ltbp1 was mainly detected in the mandibular jaw region, brain trunk, and internal organs such as pancreas and gallbladder. And ltbp1 colocalized with both sox9a and ckma in mandibular region. Morpholino based knockdown of ltbp1 results in severe jaw malformation. Alcian blue staining revealed severe deformity of Meckel's cartilage along with the absence of ceratobranchial. Three-dimension measurements of ltbp1 morphants jaws showed decrease in both mandible length and width and increase in open mouth distance. Expression of cartilage marker sox9a and muscle marker ckma was decreased in ltbp1 morphants. Conclusions: Our experiments found that ltbp1 was expressed in zebrafish mandibular jaw cartilages and the surrounding muscles. The ltbp1 knockdown zebrafish exhibited phenotypes consistent with Ltbp1 knockout mice. And loss of ltbp1 function lead to significant mandibular jaw defects and affect both jaw cartilages and surrounding muscles

Investigation and protection of fishery resources in the middle of Bohai Sea

In May and October 2017, 12 stations were set up in the Central Bohai Sea for fishery resources investigation. The results show that there are many dominant species in this area, and the inshore fishery resources are higher than those in the open sea because of the abundant nutrients from land, the high density of zooplankton and the food of swimming animals. In order to effectively protect the fishery resources in the Central Bohai Sea, this paper puts forward some suggestions, such as strengthening the protection propaganda, scientific and reasonable fishing, and strengthening the management of marine environment

Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes

Accurate identification of the correct, biologically relevant RNA structures is critical to understanding various aspects of RNA biology since proper folding represents the key to the functionality of all types of RNA molecules and plays pivotal roles in many essential biological processes. Thus, a plethora of approaches have been developed to predict, identify, or solve RNA structures based on various computational, molecular, genetic, chemical, or physicochemical strategies. Purely computational approaches hold distinct advantages over all other strategies in terms of the ease of implementation, time, speed, cost, and throughput, but they strongly underperform in terms of accuracy that significantly limits their broader application. Nonetheless, the advantages of these methods led to a steady development of multiple in silico RNA secondary structure prediction approaches including recent deep learning-based programs. Here, we compared the accuracy of predictions of biologically relevant secondary structures of dozens of self-cleaving ribozyme sequences using seven in silico RNA folding prediction tools with tasks of varying complexity. We found that while many programs performed well in relatively simple tasks, their performance varied significantly in more complex RNA folding problems. However, in general, a modern deep learning method outperformed the other programs in the complex tasks in predicting the RNA secondary structures, at least based on the specific class of sequences tested, suggesting that it may represent the future of RNA structure prediction algorithms

Research on adjustable intelligent speed retarder

The existing speed bumps can reduce the number of traffi c accidents, but it will reduce the comfort of drivers and reduce the service life of passing vehicles. In order to reduce the number of traffi c accidents at the same ti me, to ensure the driver’s comfort according to the provisions of the driving, to prevent the service life of the vehicle to reduce, put forward a lifting speed belt, the speed belt by measuring subsystem, lifting power subsystem and deceleration plate device composed. The experimental results show that the device can maximize the driver’s comfort while reducing the speed of passing vehicles, reduce the number of traffi c accidents, improve traffi c safety, and protect people’s life and property safety