Search

A great number of high-quality fractured-vuggy reservoirs (FVRs) are buried in the ultradeep carbonate strata in the Tarim Basin, Western China. Drilling confirms that some FVRs with strong beaded reflections are partially or completely filled with solids, resulting in oil and gas exploration failures. In this paper, we propose a well data (the drilling information and the well-logging data) constrained seismic waveform component decomposition and reconstruction method for ultradeep FVR characteristic identification (WSFI). WSFI contains the well data (the drilling information and the well-logging data) constrained seismic waveform component linear discriminant analysis and reconstruction and seismic attribute (i.e., root-mean-square amplitude)-based FVR characteristic identification. A forward-modeling example indicates that WSFI can effectively identify the FVR characteristics. Field data example in the ZG block in the Tarim Basin demonstrates that WSFI can be widely used for FVR characteristic identification.

The Tazhong paleo-uplift is one of the most important hydrocarbon enrichment areas in Tarim Basin. After years of exploration, many reservoirs have been discovered in the deep Paleozoic carbonate karsted rocks. Current research suggests that tectonic evolution and faulting movements have an important impact on reservoir development and hydrocarbon accumulation. But the reservoir prediction reliability is greatly compromised due to the lack of appropriate technical means to ascertain the geometric and kinematic characteristics of fault system more accurately. We solve this difficulty to a great extent by integrating a series of new applicable technologies. Firstly, the existing seismic amplitude cube was further processed with the technology of "vector high fidelity signal enhance" which enhanced the natural bandwidth of seismic imaged volumes by simultaneously optimizing the signal and frequency content. Then more details of large-scale faults in the target interval were displayed. Secondly, a variety of fault detection methods were used to trace all possible large-scale faults including conventional edge-detection methods such as Variance cube and new method of "end-to-end convolutional neural network (CNN)". Thirdly, "auto fault patch extraction" was performed and the extracted fault patches were combined with some manual work to make sure they followed certain structural patterns according to the regional geological knowledge. Fourthly, the kinematics of the mapped fault system including its grouping and staging were carefully explored based on "discontinuity stability analysis" and traditional geological analysis. The former was based on Mohr-Coulomb criterion considering friction angle and cohesion of the faults, their attitudes and the 3D paleo-stress fields corresponding to different tectonic events. And the latter mainly focused on vertical layering of seismic structures considering vertical variations of fault patterns and strata attitudes, offset formations, stratigraphic unconformities, growth formations and fault patterns in plan view, etc. It was suggested that the karsted carbonate reservoirs in some regions were mainly related to 3 types of fault damage zones, i.e. strike-slip fault damage zones, reverse fault damage zones and the hybrid ones. Among them, the parts with larger deformation, pull-apart basin, En echelon structure, faulted anticline, and superimposed elements were the most favorable belts to target high performance producers. It is the first time to integrate so many applicable new technologies to ascertain more clearly the geometry and kinematics and the fault system in the deep karsted carbonate reservoirs which are otherwise blurred on seismic images. This can be applied directly for optimized well placement and can also be referred to for similar industrial projects.

A one step facile method is proposed to synthesize Co3O4/BCNO composites as efficient and stable catalysts for the oxygen reduction reaction in Zn–air batteries.

Natural product (NP) has a long history in promoting modern drug discovery, which has derived or inspired a large number of currently prescribed drugs. Recently, the NPs have emerged as the ideal candidates to combine with other therapeutic strategies to deal with the persistent challenge of conventional therapy, and the molecular regulation mechanism underlying these combinations is crucial for the related communities. Thus, it is urgently demanded to comprehensively provide the disease-specific molecular regulation data for various NP-based drug combinations. However, no database has been developed yet to describe such valuable information. In this study, a newly developed database entitled ‘Natural Product-based Drug Combination and Its Disease-specific Molecular Regulation (NPCDR)’ was thus introduced. This database was unique in (a) providing the comprehensive information of NP-based drug combinations & describing their clinically or experimentally validated therapeutic effect, (b) giving the disease-specific molecular regulation data for a number of NP-based drug combinations, (c) fully referencing all NPs, drugs, regulated molecules/pathways by cross-linking them to the available databases describing their biological or pharmaceutical characteristics. Therefore, NPCDR is expected to have great implications for the future practice of network pharmacology, medical biochemistry, drug design, and medicinal chemistry. This database is now freely accessible without any login requirement at both official (https://idrblab.org/npcdr/) and mirror (http://npcdr.idrblab.net/) sites., Graphical Abstract Graphical AbstractNPCDR: natural product-based drug combination and its disease-specific molecular regulation.

Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named ‘DRESIS’ was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/

The traditional acid leaching method for recycling valuable metals from the cathode material of spent lithium-ion batteries (LIBs) has encountered the problems of high consumption of H₂SO₄ and reducing agents. In this study, a novel process has been developed to strengthen valuable metal recovery from spent lithium-ion batteries by reductive thermal treatment and electrochemical leaching. With the help of reductive thermal treatment under the conditions of R = 1600 °C and 120 min, the layered structure of the cathode material LiNi₁/₃Co₁/₃Mn₁/₃O₂ is reduced to a mixture of Li₂CO₃, NiO, Co₃O₄, Mn₂O₃, and MnO₂ due to the oxygen releasing from the framework. The destruction of the layered crystal structure reduces the difficulty of leaching valuable metals. 90.59% Ni, 90.53% Co, 66.40% Mn, and 100% Li were leached under the optimal conditions of 20 mL/g liquid–solid ratio, 1.5 M H₂SO₄, 0.8 A, and 150 min at room temperature. Comparing the processes of reductive thermal treatment with acid leaching and direct electrochemical leaching of spent cathode material under the same leaching conditions, the proposed approach can achieve the highest metal recovery and economic benefits. It can be implemented at room temperature, decreases the application of reducing agent and sulfuric acid, and makes full use of spent graphite, resulting in a greener recycling process for recovering valuable metals from spent LIBs.

What is already known about this topic? Dengue fever (DF) outbreaks affect hundreds of millions of people worldwide and have increased significantly in Guangdong Province in 2019. What is added by this report? This paper described briefly DF outbreaks were attributed to several types of dengue virus (DENV) including DENV-1, DENV-2, and DENV-3 in 2019 in Guangdong, tracked the sources of viruses through phylogenetic analysis and epidemiological investigation, and primarily revealed the epidemiological links among the outbreaks. What are the implications for public health practice? The introduction of DENV from DF endemic areas increased pressure on the prevention and control of DF in Guangdong. Early detection of suspected cases and typing and genotyping of circulating viruses should be prioritized and enhanced to promptly assess the likelihood of local transmission, of introduction, and of subsequent sustained local transmission of the virus to implement optimal prevention and control strategies and measures.

NiTi alloy has a wide range of applications as a biomaterial due to its high ductility, low corrosion rate, and favorable biocompatibility. Although Young’s modulus of NiTi is relatively low, it still needs to be reduced; one of the promising ways is by introducing porous structure. Traditional manufacturing processes, such as casting, can hardly produce complex porous structures. Additive manufacturing (AM) is one of the most advanced manufacturing technologies that can solve impurity issues, and selective laser melting (SLM) is one of the well-known methods. This paper reviews the developments of AMNiTi with a particular focus on SLM-NiTi utilization in biomedical applications. Correspondingly, this paper aims to describe the three key factors, including powder preparation, processing parameters, and gas atmosphere during the overall process of porous NiTi. The porous structure design is of vital importance, so the unit cell and pore parameters are discussed. The mechanical properties of SLM-NiTi, such as hardness, compressive strength, tensile strength, fatigue behavior, and damping properties and their relationship with design parameters are summarized. In the end, it points out the current challenges. Considering the increasing application of NiTi implants, this review paper may open new frontiers for advanced and modern designs.

As the demand for ore resources increases, the target for mineral exploration gradually shifts from shallow to deep parts of the Earth (>1 km). However, for the ore-hosting strata, it is difficult to obtain high-resolution images by using the electromagnetic method. Seismic full waveform inversion (FWI) is an optimization algorithm which aims at minimizing the prestack seismic data residual between synthetic and observed data. In this case, FWI provides an effective way to achieve high-resolution imaging of subsurface structures. However, acquired seismic data usually lack low frequencies, resulting in severe cycle skipping of FWI, when the initial velocity model is far away from the true one. Phase information in the seismic data provides the kinematic characteristics of waves and has a quasi-linearly relationship with subsurface structures. In this article, we propose to use a phase-amplitude-based full waveform inversion with total-variation regularization (TV-PAFWI) to invert the deep-seated ores. The ore-hosting velocity model test results demonstrate that the TV-PAFWI is suitable for high-resolution velocity model building, especially for deep-seated ores.

The width of reservoir-scale fault damage zone governs fluid flow of the tight reservoirs in subsurface petroliferous basins. Together with cores, thin sections, geophysical well logs, and production data from more than 60 wells, we describe fracture attributes and fault damage zones associated with a conjugate strike-slip fault system in tight Ordovician carbonate rock at depths of between 6500 m and 7500 m in the Tarim intracratonic basin in NW China. Fracture frequency, aperture, and oil production in wells reveals a power-law relationship with the distance from fault cores, with a variation in two orders of magnitude. The cumulative data of fracture frequency, aperture and oil production show multiple gradients in slope with the distance. Except for sampling bias, the varied gradients are possibly related to the overlaying multiple fault zones or multiple sections along a large fault zone, which are useful to constrain damage zone width. Together with fracture frequency, cumulative fracture frequency, aperture and oil production, four major damage zone boundaries are identified at ∼200, ∼420 m, ∼780 m and ∼1100 m. Therefore, a wide fault damage zone with a width of more than 2 km in the carbonate rocks could be inferred, which mostly depends on the interaction and superposition of secondary faults. In addition, there is a relatively high fracture frequency decay exponent (>1) with distance, which is possibly related to fault size, fault splays, background fracture frequency and the lithology. In turn, the growth of wide damage zone also resulted in a large scattered distribution of the fracture attributes with the distance from fault cores. Attributes of fractures of various scales from the deep subsurface provide powerful tools for assessing damage zone width and growth.

This paper is concerned with the fault-tolerant cooperative control (FTCC) problem of multiple unmanned aerial vehicles (multi-UAVs) in the communication network. By exploiting neural network (NN) to approximate the nonlinear terms existing in the highly nonlinear multi-UAVs system, a distributed neural adaptive control scheme is proposed when only a subset of follower UAVs has access to the leader UAV’s states. To solve the problem of “explosion of complexity” in traditional backstepping architecture and reduce the number of online updating parameters of NN, dynamic surface control (DSC) and minimal learning parameter techniques are employed to reduce the computational complexity. Furthermore, by combining graph theory and Lyapunov approach, it is proved that velocities and altitudes of all follower UAVs can track the velocity and altitude of the leader UAV. Finally, simulation results are presented to verify the effectiveness of the proposed control scheme.

Forest fires usually appear in complicated terrain, as a result, ground vehicles are obviously inaccessible to these fire areas. In recent years, Unmanned Aerial Vehicles (UAVs) have been paid increasing attention and become a very promising solution to forest fires searching and monitoring. This paper explores the application of UAVs in the wild forest fires searching and fire frontier monitoring mission. First, the FARSITE fire model is used to simulate the realistic wild fire behavior. After that, the fire search problem is solved with Genetic Algorithm (GA). Then, a cooperative fire monitoring algorithm is investigated. Finally, simulation results show the effectiveness of the proposed searching and monitoring methods.

Unmanned aerial vehicles (UAVs) have been extensively used in civil and industrial applications due to the rapid development of the guidance, navigation and control (GNC) technologies. Especially, using deep reinforcement learning methods for motion control acquires a major progress recently, since deep [Formula: see text]-learning algorithm has been successfully applied to the continuous action domain problem. This paper proposes an improved deep deterministic policy gradient (DDPG) algorithm for path following control problem of UAV. A specific reward function is designed for minimizing the cross-track error of the path following problem. In the training phase, a double experience replay buffer (DERB) is used to increase the learning efficiency and accelerate the convergence speed. First, the model of UAV path following problem has been established. After that, the framework of DDPG algorithm is constructed. Then the state space, action space and reward function of the UAV path following algorithm are designed. DERB is proposed to accelerate the training phase. Finally, simulation results are carried out to show the effectiveness of the proposed DERB–DDPG method.

With the gradual disappearance of traditional structural reservoirs, widely distributed deep thin lithologic reservoirs developed in continental and transitional sedimentary environments may offer significant petroleum reserves. Reservoir characterization through conventional impedance inversion methods without appropriate selection of regularization parameters and other empirical constraints cannot easily detect and assess these reserves, however, due to their size, variation, and features that conceal them. We have evaluated a nonlinear chaos inversion method that uses well data and seismic facies to characterize the oil and gas reservoirs of the Tazhong oil field in the Tarim Basin, China. The inverted results exhibited high agreement with the well data in highlighting the interfaces and lithologic bodies. This integrated method also provided enhanced resolution of depositional contacts and variable lithologic bodies. Specifically, the approach was able to describe a highly variable, approximately 5 m thick Carboniferous formation at depths greater than 3400 m. Borehole and core data provided a complementary hydrocarbon accumulation model. We used background information on hydrocarbon-bearing units within the main structure (an anticline) to detect and access (drill) ultra-thin lithologic reservoirs located in deeper areas of the structural high. Two wells drilled according to predictions derived from this new approach have each reached production rates of more than 30 tons of oil per day. Thus, we have determined the effectiveness of combining chaos inversion methods with empirical constraints in exploration of deep thin hydrocarbon reservoirs.

Architecture and permeability of fault damage zones are important for understanding faulting mechanisms and fluid flow in fractured rocks. Nevertheless, this understanding is often hindered by limited availability of data, especially in the subsurface. A comprehensive suite of cores, logs and production data is here presented to unravel petrophysical properties and fracture characteristics of deep (>6000 m) Ordovician limestones within strike-slip fault zones of the Tarim intracratonic basin (NW China). The results show that (1) the carbonate porosity is mainly secondary dissolution porosity and comprises low porosity ( 5 mD) and high porosity (>8%); (2) fault damage zones are generally tight with fracture aperture

This paper addresses the difficult wake vortex attenuation control problem for receiver unmanned aerial vehicle (UAV) in autonomous aerial refueling (AAR). When receiver UAV is approaching the tanker aircraft/UAV in AAR, the wake vortex induced by the tanker aircraft/UAV will have adverse effects on the safe flight of the receiver UAV. By introducing the wake vortex into the receiver UAV’s equations through wind-effect terms, a receiver UAV model including wake vortex is first given to facilitate the controller design. Then, to investigate the wake vortex attenuation control, disturbance observer technique is incorporated into the dynamic surface control architecture to estimate the lumped uncertainties caused by the wake vortex. By using Lyapunov method, it is shown that the receiver UAV can approach to the tanker aircraft/UAV with high precision in the presence of wake vortex. Finally, simulation results are presented to illustrate the effectiveness of the proposed control strategy.

This paper presents a new online reconfigurable line-of-sight (LOS) path following control approach for an unmanned aerial vehicle (UAV). First, a time-varying lookahead distance mechanism is developed for guaranteeing agile and abrupt actions of the UAV by moving it towards the desired path from which the UAV is far away, while generating more smooth operations of the UAV to reduce the fluctuations when it is close to the demanded path. Then, a self-adjustable integral LOS guidance strategy is devised to effectively compensate the steady-state errors and sideslip angles which are caused by the negative impacts from wind. The neural network technique is employed for learning and regulating control parameters of the proposed guidance law online in order to precisely counteract the adverse effects of time-varying wind-induced sideslips. Finally, extensive simulation studies are carried out to demonstrate the effectiveness of the proposed path following methodology.

In this paper, a distributed adaptive fault-tolerant cooperative control (FTCC) scheme is developed for flying multiple unmanned aerial vehicles (UAVs) with consideration of actuator and sensor faults. The communication network is an undirected, fixed topology and only a subset of UAVs has access to the common reference. By using a sliding-mode observer, the common reference is estimated by each UAV. The lumped uncertainties including external disturbances, actuator and sensor faults are estimated by an adaptive mechanism. On the basis of the estimated reference and lumped uncertainties, dynamic surface control technique is utilized to eliminate the computational burden inherent in the traditional backstepping control architecture. The highlight is that external disturbances, actuator and sensor faults are considered in the distributed control scheme for multi-UAVs simultaneously. By using graph theory and Lyapunov-based method, it is proved that all signals in the closed-loop system are bounded. Furthermore, simulation results are exhibited to demonstrate the effectiveness of the proposed control scheme.

To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis (PCA) are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.

In recent years, the regional surveillance has an increasingly range of applications. This paper proposes a solution for using multiple UAVs to regional surveillance based on pheromones and artificial potential field theory. First, the flight environment of target area is divided into a number of units. Then, the pheromones model is applied in each unit. Due to the diffusibility of pheromones, a gradient of pheromones would be formed in the target area. After that, the UAV would fly along the gradient of pheromones towards the high intensity areas. Finally, the solution is improved with artificial potential field theory for successfully resolving the issues of obstacle avoidance, collision avoidance and optimal search. Simulations in three cases have been carried out to verify the effectiveness of the proposed solution.