Trajectory Control Technology of the Shallow Layer Cluster Horizontal Well in Western Margin of the Junggar Basin

The Pai 601 block of Shengli Oilfield in the western margin of the Junggar basin is the key block of capacity building in the Junggar basin, due to shallow kick-off points, strong anisotropy of oblique section of strata and the development of reservoir calcareous intercalation leads to very outstanding problem of Well trajectory control. Through using method of optimizing the KOP anddrilling well path design method which correct the dogleg that based on the steerable BHA and formation anisotropy, combiningbuildup section and horizontal landing phase of the BHA and drilling parameters optimization, which has solved the problem of shallow layer cluster horizontal well trajectory control in western margin of the Junggar basin. Through the technology applications of the 200 shallow horizontal wells, a set of shallow horizontal well trajectory control technology which was suitable to the west margin of in Junggar basin was formed.Key words: Shallow layers; Junggar basin; Cluster horizontal wells; The drilling well trajectory design; Well trajectory contro

Dynamic analysis of the longitudinal vibration on bottom drilling tools

With extreme complexity, the drilling process is a dynamic process which is severely influenced by longitudinal vibration. Longitudinal vibration, as one of the most important reason, is directly generated by the fatigue failure of the bottom hole assembly. In this paper, the natural frequencies of longitudinal vibration along the drillstring are analyzed by the finite element method. The deformed plot, stress nephogram, and displacement contour map under 1 to 4 ordered the natural frequency of the longitudinal vibration are obtained. The analysis results show that the maximum deformation always appears in the central part of the string so that some technological process on these positions is required to reduce the collision between the string and wellbore wall. Additionally, a time series of longitudinal vibration of a bottom rotating drillstring is extracted from real-time field data, which is measured while drilling near the drill bit. Then the time-frequency and energy spectrum analysis of the longitudinal vibration is carried out. The results of the statistical analysis show that, when the drillstring uniformly rotates, the longitudinal vibration can be considered as a kind of random vibration. However, if the stick-slip phenomenon occurs during the drilling process, the energy concentration will appear in the time series spectrum of the longitudinal vibration, by which means the vibration could be regarded as random no longer

Deep learning based real-time facial mask detection and crowd monitoring

During the Covid pandemic, the importance of wearing mask has been noted globally. Additionally, crowded human clusters facilitated the transmission of the virus, which brings up the need for new systems for monitoring such situations. To address such issues, this research proposes an object recognition visual system based on deep learning to monitor the wearing of masks in a certain space and the control of the number of people indoors as an important tool during an epidemic. This research mainly investigates two types of identification. The first is to monitor whether people entering the site wear a mask at the entrance and exit of the field, and the second is to count the number of people entering a specific area. Experimental results show that by utilising the visual sensor, it is possible to detect and identify the people who frequently enter and exit in real-time. An advanced transfer learning approach has been employed to achieve the best discrimination performance. The actual training results prove that the migration learning Mask R-CNN algorithm produced by this method and the original Mask R-CNN algorithm have increased the mAP by 3%, reaching a mAP of 96%. In addition, the accuracy of the random sampling and identification in actual scenes has reached 92.1%. The developed deep learning vision system has an enhanced identification ability for the verification and analysis of actual scenes and has great application potential

Novel joint-drift-free scheme at acceleration level for robotic redundancy resolution with tracking error theoretically eliminated

In this article, three acceleration-level joint-drift-free (ALJDF) schemes for kinematic control of redundant manipulators are proposed and analyzed from perspectives of dynamics and kinematics with the corresponding tracking error analyses. First, the existing ALJDF schemes for kinematic control of redundant manipulators are systematized into a generalized acceleration-level joint-drift-free scheme with a paradox pointing out the theoretical existence of the velocity error related to joint drift. Second, to remedy the deficiency of the existing solutions, a novel acceleration-level joint-drift-free (NALJDF) scheme is proposed to decouple Cartesian space error from joint space with the tracking error theoretically eliminated. Third, in consideration of the uncertainty at the dynamics level, a multi-index optimization acceleration-level joint-drift-free scheme is presented to reveal the influence of dynamics factors on the redundant manipulator control. Afterwards, theoretical analyses are provided to prove the stability and feasibility of the corresponding dynamic neural network with the tracking error deduced. Then, computer simulations, performance comparisons, and physical experiments on different redundant manipulators synthesized by the proposed schemes are conducted to demonstrate the high performance and superiority of the NALJDF scheme and the influence of dynamics parameters on robot control. This work is of great significance to enhance the product quality and production efficiency in industrial production

The symbiotic bacteria Alcaligenes faecalis of the entomopathogenic nematodes Oscheius spp. exhibit potential biocontrol of plant- and entomopathogenic fungi

Soil-dwelling entomopathogenic nematodes (EPNs) kill arthropod hosts by injecting their symbiotic bacteria into the host hemolymph and feed on the bacteria and the tissue of the dying host for several generations cycles until the arthropod cadaver is completely depleted. The EPN-bacteria-arthropod cadaver complex represents a rich energy source for the surrounding opportunistic soil fungal biota and other competitors. We hypothesized that EPNs need to protect their food source until depletion and that the EPN symbiotic bacteria produce volatile and non-volatile exudations that deter different soil fungal groups in the soil. We isolated the symbiotic bacteria species (Alcaligenes faecalis) from the EPN Oscheius spp. and ran infectivity bioassays against entomopathogenic fungi (EPF) as well as against plant pathogenic fungi (PPF). We found that both volatile and non-volatile symbiotic bacterial exudations had negative effects on both EPF and PPF. Such deterrent function on functionally different fungal strains suggests a common mode of action of A.faecalis bacterial exudates, which has the potential to influence the structure of soil microbial communities, and could be integrated into pest management programs for increasing crop protection against fungal pathogens

Effect of annulus drilling fluid on lateral vibration of drillstring

Annular drilling fluid between the drillstring and borehole wall has a great influence on lateral vibration of drillstring and the influence involves the added mass. Assuming the drilling fluid is incompressible, we derive the added mass coefficient that annular drilling fluid influences on lateral vibration of drillstring in the case of axial flow of drilling fluid. When the axial flow of drilling fluid is considered, the added mass coefficient is difficult to solve. We apply CFD method and dynamic mesh technique to establish the calculation model for the flow in the annulus caused by the vibration of drillstring in the annulus. The pressure distribution and velocity distribution of annular drilling fluid are obtained. The added mass force of the drilling fluid acting on the drillstring along the direction of the drillstring is obtained from the pressure distribution, and the added mass coefficient of the lateral vibration of the drillstring is obtained. This paper provides the basis to solve the added mass coefficient of the lateral vibration of drillstring considering axial flow of drilling fluid

The interaction between different types of activated RAW 264.7 cells and macrophage inflammatory protein-1 alpha

<p>Abstract</p> <p>Background</p> <p>Two major ways of macrophage (MΦ) activation can occur in radiation-induced pulmonary injury (RPI): classical and alternative MΦ activation, which play important roles in the pathogenesis of RPI. MΦ can produce chemokine MΦ inflammatory protein-1α (MIP-1α), while MIP-1α can recruit MΦ. The difference in the chemotactic ability of MIP-1α toward distinct activated MΦ is unclear. We speculated that there has been important interaction of MIP-1α with different activated MΦ, which might contribute to the pathogenesis of RPI.</p> <p>Methods</p> <p>Classically and alternatively activated MΦ were produced by stimulating murine MΦ cell line RAW 264.7 cells with three different stimuli (LPS, IL-4 and IL-13); Then we used recombinant MIP-1α to attract two types of activated MΦ. In addition, we measured the ability of two types of activated MΦ to produce MIP-1α at the protein or mRNA level.</p> <p>Results</p> <p>Chemotactic ability of recombinant MIP-1α toward IL-13-treated MΦ was the strongest, was moderate for IL-4-treated MΦ, and was weakest for LPS-stimulated MΦ (p < 0.01). The ability of LPS-stimulated MΦ to secrete MIP-1α was significantly stronger than that of IL-4-treated or IL-13-treated MΦ (p < 0.01). The ability of LPS-stimulated MΦ to express MIP-1α mRNA also was stronger than that of IL-4- or IL-13-stimulated MΦ (p < 0.01).</p> <p>Conclusions</p> <p>The chemotactic ability of MIP-1α toward alternatively activated MΦ (M2) was significantly greater than that for classically activated MΦ (M1). Meanwhile, both at the mRNA and protein level, the capacity of M1 to produce MIP-1α is better than that of M2. Thus, chemokine MIP-1α may play an important role in modulating the transition from radiation pneumonitis to pulmonary fibrosis <it>in vivo</it>, through the different chemotactic affinity for M1 and M2.</p

Augmenting Pathologists with NaviPath: Design and Evaluation of a Human-AI Collaborative Navigation System

Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-resolution tumor images to search for pathology patterns of interest. However, existing AI-assisted tools have not realized this promised potential due to a lack of insight into pathology and HCI considerations for pathologists' navigation workflows in practice. We first conducted a formative study with six medical professionals in pathology to capture their navigation strategies. By incorporating our observations along with the pathologists' domain knowledge, we designed NaviPath -- a human-AI collaborative navigation system. An evaluation study with 15 medical professionals in pathology indicated that: (i) compared to the manual navigation, participants saw more than twice the number of pathological patterns in unit time with NaviPath, and (ii) participants achieved higher precision and recall against the AI and the manual navigation on average. Further qualitative analysis revealed that navigation was more consistent with NaviPath, which can improve the overall examination quality.Comment: Accepted ACM CHI Conference on Human Factors in Computing Systems (CHI '23

xPath: Human-AI Diagnosis in Pathology with Multi-Criteria Analyses and Explanation by Hierarchically Traceable Evidence

Data-driven AI promises support for pathologists to discover sparse tumor patterns in high-resolution histological images. However, from a pathologist's point of view, existing AI suffers from three limitations: (i) a lack of comprehensiveness where most AI algorithms only rely on a single criterion; (ii) a lack of explainability where AI models tend to work as 'black boxes' with little transparency; and (iii) a lack of integrability where it is unclear how AI can become part of pathologists' existing workflow. Based on a formative study with pathologists, we propose two designs for a human-AI collaborative tool: (i) presenting joint analyses of multiple criteria at the top level while (ii) revealing hierarchically traceable evidence on-demand to explain each criterion. We instantiate such designs in xPath -- a brain tumor grading tool where a pathologist can follow a top-down workflow to oversee AI's findings. We conducted a technical evaluation and work sessions with twelve medical professionals in pathology across three medical centers. We report quantitative and qualitative feedback, discuss recurring themes on how our participants interacted with xPath, and provide initial insights for future physician-AI collaborative tools.Comment: 31 pages, 11 figure