Research on automatic monitoring and controlling technology downhole for zonal injection in digital oilfield

2016 ◽  
Author(s):  
Yang Lingzhi ◽  
Yu Jiuzheng ◽  
Ju Yafeng ◽  
Yao Bin ◽  
Luo Bilin ◽  
...  
Keyword(s):  
Automatic Monitoring ◽  
Digital Oilfield

Discuss on down-Hole Intelligent Intermittent Pumping Technology in Low Liquid Production Well

2012 ◽  
Vol 594-597 ◽  
pp. 2598-2601 ◽  
Author(s):  
Bao Ming Shen ◽  
Di Qi ◽  
Hong Tao Chi ◽  
Li Qiang Ma ◽  
Xian Ku Liu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Remote Control ◽  
Power Consumption ◽  
Automatic Monitoring ◽  
Electrical Heating ◽  
Production Well ◽  
Production Wells ◽  
Intermittent Pumping ◽  
Digital Oilfield ◽  
Oilfield Development

Because of its matured lifting technology and perfect supporting process, beam unit-rod pumping is applied widely in oilfield development. But as applied in low liquid production wells, the proportion of idle power consumption is large. Therefore, the lifting technology of electric submersible piston pump was studied to decrease the energy consumption. Based on the supporting technologies of electrical heating, liquid level automatic monitoring and remote control, the technology of down-hole intelligent intermittent pumping was developed, which can decrease the energy consumption, and provide technical support for the construction of digital oilfield.

AUTOMATED SYSTEM FOR MONITORING THE VEGETATIVE STATE OF PLANTS

2020 ◽  
Author(s):  
S.B. Kudryashev ◽  
◽  
V.A. Naumenko ◽  
K.V. Lamanova ◽  
R.D. Belashov ◽  
...  
Keyword(s):  
Infrared Spectrum ◽  
Vegetative State ◽  
Automatic Monitoring ◽  
Automated System

The article presents a system for automatic monitoring of plant conditions, which is based on a specially developed method for processing photographs in the infrared spectrum. The paper substantiates the choice of a quadrocopter as a transport platform, which provides mobility to the system.

In-situ automatic observations of ice thickness of seas

2012 ◽  
Vol 19 (3) ◽  
pp. 583-592 ◽  
Author(s):  
Yinke Dou ◽  
Xiaomin Chang
Keyword(s):  
Sea Ice ◽  
New Technologies ◽  
Capacitive Sensor ◽  
Automatic Monitoring ◽  
Ice Thickness ◽  
In Situ Observation ◽  
Glacier Surface ◽  
Sea Ice Thickness ◽  
Surface Ice

Abstract Ice thickness is one of the most critical physical indicators in the ice science and engineering. It is therefore very necessary to develop in-situ automatic observation technologies of ice thickness. This paper proposes the principle of three new technologies of in-situ automatic observations of sea ice thickness and provides the findings of laboratory applications. The results show that the in-situ observation accuracy of the monitor apparatus based on the Magnetostrictive Delay Line (MDL) principle can reach ±2 mm, which has solved the “bottleneck” problem of restricting the fine development of a sea ice thermodynamic model, and the resistance accuracy of monitor apparatus with temperature gradient can reach the centimeter level and research the ice and snow substance balance by automatically measuring the glacier surface ice and snow change. The measurement accuracy of the capacitive sensor for ice thickness can also reach ±4 mm and the capacitive sensor is of the potential for automatic monitoring the water level under the ice and the ice formation and development process in water. Such three new technologies can meet different needs of fixed-point ice thickness observation and realize the simultaneous measurement in order to accurately judge the ice thickness.

INTELLIGENT PRODUCTION FIELD AND DIGITAL OILFIELD OF THE FUTURE

2019 ◽  
pp. 83-91
Author(s):  
T.A. Pospelova ◽  
◽  
A.N. Kharitonov ◽  
A.Yu. Yushkov ◽  
A.V. Strekalov ◽  
...  
Keyword(s):  
The Future ◽  
Digital Oilfield

Automatic monitoring of denitrification rates and capacities in activated sludge processes using fluorescence or redox potential

1998 ◽  
Vol 37 (12) ◽  
pp. 121-129 ◽  
Author(s):  
S. Isaacs ◽  
Terry Mah ◽  
S. K. Maneshin
Keyword(s):  
Biological Activity ◽  
Activated Sludge ◽  
Redox Potential ◽  
Treatment Plant ◽  
Automatic Monitoring ◽  
Organic Substrate ◽  
Anoxic Zone ◽  
Optimal Dosing ◽  
Denitrifying Microorganisms ◽  
Activated Sludge Processes

A novel method is described to automatically estimate several key parameters affecting denitrification in activated sludge processes: the nitrate concentration, the denitrification capacity, and the maximum (substrate unlimited) and actual denitrification rates. From these, the concentration of active denitrifying microorganisms and the quality of available organic substrate pool can be estimated. Additionally, a modification of the method allows the determination of the efficacy of various carbon substrates to enhance denitrification, and this can be used to determine optimal dosing rates of an external carbon source. The method is based on measurements of either fluorescence or redox potential (ORP) in an isolated mini-reactor, the Biological Activity Meter (BAM), situated in the anoxic zone of the wastewater treatment plant. Advantages of the method are that it is in situ, operating at the same temperature as in the measured anoxic zone, requires no pumps or pipes for mixed liquor sampling, consumes little or no reagents, and uses measurement signals which are instantaneous and low maintenance, one of which provides a direct measure of biological activity.

scholarly journals Assessment of Automatically Monitored Water Levels and Water Quality Indicators in Rivers with Different Hydromorphological Conditions and Pollution Levels in Greece

Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 86
Author(s):  
Angeliki Mentzafou ◽  
George Varlas ◽  
Anastasios Papadopoulos ◽  
Georgios Poulis ◽  
Elias Dimitriou
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Quality Indicators ◽  
High Frequency ◽  
Automatic Monitoring ◽  
Water Levels ◽  
High Temporal Resolution ◽  
Water Quality Indicators ◽  
Pollution Levels ◽  
Riverine Ecosystems

Water resources, especially riverine ecosystems, are globally under qualitative and quantitative degradation due to human-imposed pressures. High-temporal-resolution data obtained from automatic stations can provide insights into the processes that link catchment hydrology and streamwater chemistry. The scope of this paper was to investigate the statistical behavior of high-frequency measurements at sites with known hydromorphological and pollution pressures. For this purpose, hourly time series of water levels and key water quality indicators (temperature, electric conductivity, and dissolved oxygen concentrations) collected from four automatic monitoring stations under different hydromorphological conditions and pollution pressures were statistically elaborated. Based on the results, the hydromorphological conditions and pollution pressures of each station were confirmed to be reflected in the results of the statistical analysis performed. It was proven that the comparative use of the statistics and patterns of the water level and quality high-frequency time series could be used in the interpretation of the current site status as well as allowing the detection of possible changes. This approach can be used as a tool for the definition of thresholds, and will contribute to the design of management and restoration measures for the most impacted areas.

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

scholarly journals A Knowledge-Based Algorithm for Automatic Monitoring of Orthodontic Treatment: The Dental Monitoring System. Two Cases

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1856
Author(s):  
Silvia Caruso ◽  
Sara Caruso ◽  
Marianna Pellegrino ◽  
Rayan Skafi ◽  
Alessandro Nota ◽  
...  
Keyword(s):  
Adult Patient ◽  
Remote Monitoring ◽  
Digital Technology ◽  
Orthodontic Treatment ◽  
Dental Treatment ◽  
Automatic Monitoring ◽  
Software As A Service ◽  
Knowledge Based ◽  
First Case ◽  
Service Application

Background: In the dental field, digital technology has created new opportunities for orthodontists to integrate their clinical practice, and for patients to collect information about orthodontics and their treatment, which is called “teledentistry.” Dental monitoring (DM) is a recently introduced orthodontic application that combines safe teledentistry with artificial intelligence (AI) using a knowledge-based algorithm, allowing an accurate semi-automatic monitoring of the treatment. Dental Monitoring is the world’s first SaaS (Software as a Service) application designed for remote monitoring of dental treatment, developed in Paris, France, with Philippe Salah as the Co-founder and CEO. Cases presentation: This report describes two cases in which DM system was essential to achieve the control of certain movements: it was possible to follow the movement, even if complex, such as the anterior cross of an adult patient and a lack of space in the canine of the growing patient. The software analyzed the fit and retention of the aligner, thus ensuring correct biomechanics. They were treated during the COVID-19 pandemic lockdown with aligners. The first case is a growing patient who was monitored during an interceptive orthodontic treatment to manage a retained upper canine. The second case is an adult patient forced to finalize his treatment of upper lateral incisor crossbite. The software analyzed the fit and retention of the aligner, thus ensuring correct biomechanics. Conclusions: DM system appears to be a promising method, useful for improving the interaction between doctor and patient, generally acceptable and useful to patients, even in critical clinical situations, at least in cases with optimal compliance and ability to use the tool properly.

scholarly journals Zebrafish automatic monitoring system for conditioning and behavioral analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marta de Oliveira Barreiros ◽  
Felipe Gomes Barbosa ◽  
Diego de Oliveira Dantas ◽  
Daniel de Matos Luna dos Santos ◽  
Sidarta Ribeiro ◽  
...  
Keyword(s):  
Video Processing ◽  
Red Light ◽  
Automatic Monitoring ◽  
Automated System ◽  
Control Group ◽  
Automatic Monitoring System ◽  
Fish Schools ◽  
Detection And Tracking ◽  
Evaluation Strategies ◽  
Light Stimuli

AbstractStudies using zebrafish (Danio rerio) in neuro-behavioural research are growing. Measuring fish behavior by computational methods is one of the most efficient ways to avoid human bias in experimental analyses, extending them to various approaches. Sometimes, thorough analyses are difficult to do, as fish can behave unpredictably during an experimental strategy. However, the analyses can be implemented in an automated way, using an online strategy and video processing for a complete assessment of the zebrafish behavior, based on the detection and tracking of fish during an activity. Here, a fully automatic conditioning and detailed analysis of zebrafish behavior is presented. Microcontrolled components were used to control the delivery of visual and sound stimuli, in addition to the concise amounts of food after conditioned stimuli for adult zebrafish groups in a conventional tank. The images were captured and processed for automatic detection of the fish, and the training of the fish was done in two evaluation strategies: simple and complex. In simple conditioning, the zebrafish showed significant responses from the second attempt, learning that the conditioned stimulus was a predictor of food presentation in a specific space of the tank, where the food was dumped. When the fish were subjected to two stimuli for decision-making in the food reward, the zebrafish obtained better responses to red light stimuli in relation to vibration. The behavior change was clear in stimulated fish in relation to the control group, thus, the distances traveled and the speed were greater, while the polarization was lower in stimulated fish. This automated system allows for the conditioning and assessment of zebrafish behavior online, with greater stability in experiments, and in the analysis of the behavior of individual fish or fish schools, including learning and memory studies.

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