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In view of reservoirs with low pressure, medium porosity, and low permeability in Changqing Oilfield, along with the “double high stage” in the middle and late development featuring a sharp rise in water cut and a low comprehensive water flooding recovery rate, a study of a composite surfactant flooding system was conducted. It is found that there is a synergistic effect between dodecyl hydroxypropyl sulfobetaine (HPSB) and naphthenic petroleum sulfonate (NPS). When the ratio of HPSB to NPS is 8:2, the best interfacial activity is exhibited in the formation water of the target block, which can reduce the interfacial tension of oil–water equilibrium to 1 × 10 −3  mN/m and has strong salt tolerance. The synergistic effect of the two makes the composite system have good anti-adsorption performance, with little adsorption loss on the sand surface, and no significant chromatographic separation in the formation. The composite system also has good stability under reservoir conditions. The 0.2% composite surfactant system can reduce the water injection pressure from 1.52 to 1.16 MPa, a decrease of 23.7%; and increase the oil recovery ratio from 45.71 to 63.33%, an increase of 17.62%. These results demonstrate that the composite system (HPSB:NPS = 8:2) has a positive impact on pressure reduction and production increase in the low permeability reservoirs of Changqing Oilfield.

Background Trigonella foenum-graecum L. is a Leguminosae plant, and the stems, leaves, and seeds of this plant are rich in chemical components that are of high research value. The chloroplast (cp) genome of T. foenum-graecum has been reported, but the mitochondrial (mt) genome remains unexplored. Results In this study, we used second- and third-generation sequencing methods, which have the dual advantage of combining high accuracy and longer read length. The results showed that the mt genome of T. foenum-graecum was 345,604 bp in length and 45.28% in GC content. There were 59 genes, including: 33 protein-coding genes (PCGs), 21 tRNA genes, 4 rRNA genes and 1 pseudo gene. Among them, 11 genes contained introns. The mt genome codons of T. foenum-graecum had a significant A/T preference. A total of 202 dispersed repetitive sequences, 96 simple repetitive sequences (SSRs) and 19 tandem repetitive sequences were detected. Nucleotide diversity (Pi) analysis counted the variation in each gene, with atp6 being the most notable. Both synteny and phylogenetic analyses showed close genetic relationship among Trifolium pratense , Trifolium meduseum , Trifolium grandiflorum , Trifolium aureum , Medicago truncatula and T. foenum-graecum . Notably, in the phylogenetic tree, Medicago truncatula demonstrated the highest level of genetic relatedness to T. foenum-graecum , with a strong support value of 100%. The interspecies non-synonymous substitutions (Ka)/synonymous substitutions (Ks) results showed that 23 PCGs had Ka/Ks < 1, indicating that these genes would continue to evolve under purifying selection pressure. In addition, setting the similarity at 70%, 23 homologous sequences were found in the mt genome of T. foenum-graecum. Conclusions This study explores the mt genome sequence information of T. foenum-graecum and complements our knowledge of the phylogenetic diversity of Leguminosae plants.

The evaluation of the basic properties and hydrocarbon generation potential of medium-low maturity shale oil serves as a critical link between geological resources and engineering development. This study focuses on JY oil shale, utilizing analytical techniques including vitrinite reflectance (R 0 ), total organic carbon (TOC), thermogravimetric analysis (TGA), and Rock-Eval pyrolysis to systematically characterize its geochemical properties. Based on sample characteristics, an in-situ upgrading simulation system was developed and optimized. By comparing nuclear magnetic resonance (NMR) T 1 -T 2 spectrum and pyrolysis-gas chromatography/mass spectrometry (PY-GC-MS) results before and after thermal cracking, the research further elucidates the mechanism by which thermal cracking influences the occurrence state of organic matter. Results indicate that the JY shale samples have a TOC content ranging from 1.94% to 2.57%, a total hydrocarbon generation potential (S 1  + S 2 ) of approximately 21 mg/g, R 0 between 0.66% and 1.18%, and a sapropelinite content in kerogen as high as 90.33%. These parameters classify the kerogen as Type0 I, belonging to medium-low maturity source rocks with high organic matter abundance, favorable pyrolysis characteristics, and significant hydrocarbon generation potential, thus qualifying for in-situ upgrading development. Simulation experiments conducted within the temperature range of 400 °C to 600 °C show that under isothermal conditions at 450 °C for 12 h, the organic carbon degradation rate can reach about 40%, and fracturing measures can enhance the in-situ upgrading effect. After thermal cracking, the proportion of kerogen decreases by approximately 10%, the proportion of adsorbed hydrocarbons increases by about 5%, and the proportion of free hydrocarbons increases by roughly 3%. PY-GC-MS analysis further reveals that the proportion of light hydrocarbons increases significantly by about 20%, while the proportion of heavy hydrocarbons decreases by over 20%, validating the conversion sequence of “heavy hydrocarbons → medium hydrocarbons → light hydrocarbons” during thermal cracking. This study methodologically integrates multi-scale analysis with a self-developed simulation system and mechanistically clarifies the evolution pathway of the three-phase state of organic matter and the trend toward lighter hydrocarbon compositions. It provides theoretical and experimental support for assessing the feasibility of in-situ upgrading development and optimizing techniques for medium-low maturity shale oil, offering valuable insights for mitigating development risks.

In recent years, fracturing-flooding technology has achieved a series of successful practices in the development of low-permeability oil reservoirs. However, research on the dynamic behavior of fracturing-flooding remains limited. In this paper, a dual medium model considering anisotropic characteristics is established for the target blocks. Multiple sets of conventional water injection transitions and multi-cycle fracturing-flooding operations are designed for simulation to explore the subsequent optimal operational schemes. Simulations are conducted on the optimal transitions between conventional water injection and multi-cycle fracturing-flooding schemes for different reservoir models with varying physical properties to study the dynamic behavior of fracturing-flooding in oil reservoirs with different properties. The results indicate that, for conventional water injection schemes, the optimal transition time for both the target well group and other reservoirs with different properties corresponds to a formation pressure coefficient between 1.2 and 1.3, with the optimal injection–production ratio being 1:1. From the perspective of water cut, the accumulated oil production of multi-cycle fracturing-flooding is higher than that of conventional water injection. The optimal multi-cycle fracturing-flooding schemes for both the target well group and other reservoirs with different properties are to start fracturing-flooding when the formation pressure coefficient is around 0.8 and to begin production when it reaches 1.4.

Interleukin (IL)-33 is an important member of the IL-1 family that has pleiotropic activities in innate and adaptive immune responses in host defense and disease. It signals through its ligand-binding primary receptor ST2 and IL-1 receptor accessory protein (IL-1RAcP), both of which are members of the IL-1 receptor family. To clarify the interaction of IL-33 with its receptors, we determined the crystal structure of IL-33 in complex with the ectodomain of ST2 at a resolution of 3.27 Å. Coupled with structure-based mutagenesis and binding assay, the structural results define the molecular mechanism by which ST2 specifically recognizes IL-33. Structural comparison with other ligand–receptor complexes in the IL-1 family indicates that surface-charge complementarity is critical in determining ligand-binding specificity of IL-1 primary receptors. Combined crystallography and small-angle X-ray–scattering studies reveal that ST2 possesses hinge flexibility between the D3 domain and D1D2 module, whereas IL-1RAcP exhibits a rigid conformation in the unbound state in solution. The molecular flexibility of ST2 provides structural insights into domain-level conformational change of IL-1 primary receptors upon ligand binding, and the rigidity of IL-1RAcP explains its inability to bind ligands directly. The solution architecture of IL-33–ST2–IL-1RAcP complex from small-angle X-ray–scattering analysis resembles IL-1β–IL-1RII–IL-1RAcP and IL-1β–IL-1RI–IL-1RAcP crystal structures. The collective results confer IL-33 structure–function relationships, supporting and extending a general model for ligand–receptor assembly and activation in the IL-1 family.

Accurately obtaining the working status of the sucker rod pumping wells is a challenging problem for oil production. Sensors at the polished rod collect working data to form surface dynamometer cards for fault diagnosis. A prevalent method for recognizing these cards is the convolutional neural network (CNN). However, this approach has two problems: an unbalanced dataset due to varying fault frequencies and similar dynamometer card shapes that complicate recognition. This leads to a low accuracy of fault diagnosis in practice, which is unsatisfactory. Therefore, this paper proposes a hybrid approach of the deep learning method and rule-based method for fault diagnosis of sucker rod pumping wells. Specifically, when the CNN model alone fails to achieve satisfactory accuracy in the working status, historical monitoring data of the relevant wells can be collected, and expert rules can assist CNN to improve diagnostic accuracy. By analyzing time series data of factors such as the maximum and minimum loads, the area of the dynamometer card, and the load difference, a knowledgebase of expert rules can be created. When performing fault diagnosis, both the dynamometer cards and related time series data are used as inputs. The dynamometer cards are used for the CNN model to diagnose, and the related time series data are used for expert rules to diagnose. The diagnostic results and the confidence levels of the two methods are obtained and compared. When the two diagnostic results conflict, the one with higher confidence is preserved. Out of the 2066 wells and 7 fault statuses analyzed in field applications, the hybrid approach demonstrated a 21.25% increase in fault diagnosis accuracy compared with using only the CNN model. Additionally, the overall accuracy rate of the hybrid approach exceeded 95%, indicating its high effectiveness in diagnosing faults in sucker rod pumping wells.

Coccidiosis is a global disease caused by protozoans, typically including Eimeria spp., which pose a significant threat to the normal growth and development of young animals. Coccidiosis affects mainly the gut, where parasite proliferation occurs. The intestinal barrier, which consists of chemical, mechanical, biological, and immune defences, plays a crucial role in protecting the host against pathogens, xenobiotics, and toxins present in the gastrointestinal tract. When animals ingest sporulated Eimeria spp. oocysts, these parasites primarily reproduce in the intestinal tract, causing damage to the structure and function of the intestine. This disruption of intestinal homeostasis adversely affects animal health. Numerous studies have also revealed that Eimeria-infected animals experience slower bone growth rates, inferior meat quality, reduced egg production and quality, as well as impaired growth and development. Therefore, the purpose of this review is to examine the underlying mechanisms through which Eimeria spp. regulate intestinal damage and disturb the balance of the internal environment. Specifically, this review will focus on their effects on the structural basis of the host intestine's chemical, mechanical, biological and immune barriers. This understanding is crucial for the development of effective drugs to prevent the invasion of Eimeria spp. into the intestine, which is of paramount importance for maintaining host health.

The regulation of upstream cascade reservoirs has significantly altered the downstream hydrologic regime and should be taken into account in design flood estimation. The current flood regional composition (FRC) methods do not consider the unfavorable situations for reservoir flood control operation. In this paper, a novel framework, the most unfavorable flood regional composition (MUFRC) method, was proposed based on flood risk analysis to estimate design flood in the cascade reservoir operation period. The cascade reservoirs in the Yalong River basin were selected as a case study. The results indicated that (1) the proposed MUFRC method would allocate more flood volume to the downstream uncontrolled sub-basin, and the precise definition of flood disaster loss could have a significant impact on the MUFRC method for the rational estimation of design flood. (2) The 1000-year design flood peak, and 3-day and 7-day flood volumes at the outlet section estimated by the MUFRC method are 15,400 m3/s, 3.91, and 8.42 billion m3, respectively, which are higher than the values estimated by other FRC methods. (3) The flood control water level in the downstream reservoir can be adjusted for the reduction in design floods in the operation period, which can additionally generate 460 million kW·h (+1.82%) of hydropower during the flood season. A comparison study and sensitivity analysis further proved that the MUFRC method can rationally allocate flood volume while balancing the flood risk and comprehensive utilization benefits, which is worth further study and practical application.

Most traditional injection-production optimization methods that treat the entire oil reservoir as a whole require re-optimization when facing new reservoirs, which is not only time-consuming but also does not make full use of historical experience information. This study decomposes the reservoir into independent basic production units to increase sample size and diversity and utilizes image enhancement techniques to augment the number of samples. Two frameworks based on convolutional neural networks (CNNs) are employed to recommend optimal control strategies for inputted well groups. Framework 1 uses bottom hole pressure (BHP) as a control variable and trains a CNN with optimal BHP obtained by reinforcement learning algorithms as labels. Framework 2 saves BHP and corresponding oil well revenue (NPV) during reinforcement learning optimization and trains a CNN with well groups and BHP as features and NPV as labels. The CNN in this framework is capable of directly outputting the NPV according to control strategies. The particle swarm algorithm (PSO) is used to generate control strategies and call CNN to predict development effects until PSO converges to the optimal production strategy. The experimental results demonstrate that the CNN-based frameworks outperform the traditional PSO-based methods in terms of accuracy and computational efficiency. Framework 1 achieves an output accuracy of 87% for predicting the optimal BHP for new well groups, while Framework 2 achieves an accuracy of 78%. Both frameworks exhibit fast running times, with each iteration taking less than 1 s. This study provides a more effective and accurate method for optimizing oil well production in oil reservoirs by decomposing oil reservoirs into independent units and using CNN to construct an algorithm framework, which is of great significance for the real-time optimization and control of oil wells in oil fields.

The effective displacement of the shale oil from organic nanopores plays a significant role in development of the shale oil reservoirs. In order to deeply understand the microscopic displacement mechanism of alkane of shale oil by CO2 in organic nanopores, microscopic pore model of organic matter and molecular model of CO2 and n-dodecane were established to investigate the influences of key parameters on the displacement process by using the Monte Carlo and molecular dynamics simulation method. The instantaneous adsorption of molecules demonstrates that the displacement of n-dodecane and the adsorption of CO2 are proportional to the increase of the injection pressure of CO2 as well as the pore size. In addition, the results also show that the adsorption capacity of CO2 first increases and then decreases with the increase of the temperature, which indicates that the optimum temperature exists for the adsorption of CO2. This work can provide critical insights into understanding the microscopic displacement mechanism of shale oil by CO2 in organic nanopores in shale oil reservoirs and lay a solid foundation for the CO2 flooding in the shale oil reservoir and the CO2 storage.