Real-Time Well Integrity Diagnostics Using the Latest Developments in Visual Logging Technology; a Case Study.

2016 ◽  
Author(s):  
Nasser M. Al-Hajri ◽  
Mohammed D. Al-Ajmi ◽  
Fehead M. Al-Subaie ◽  
Andre Hognestad ◽  
Erik Johannessen
Keyword(s):  
Real Time ◽  
Well Integrity

Case Study of a Novel Autonomous Real-Time Monitoring, Control and Analysis System, to Maximize Production Uptime on Sustained Annulus Pressure Wells, While Improving HSE and Compliance with Double Barrier Well Integrity Policies

2021 ◽  
Author(s):  
Rylan Paul Dsouza ◽  
Rachelle Christine Cornwall ◽  
Alan David Brodie ◽  
Pedro Patela ◽  
Hamdi Bouali Daghmouni ◽  
...  
Keyword(s):  
Real Time ◽  
Temperature Data ◽  
Effective Management ◽  
Double Barrier ◽  
Barrier Integrity ◽  
Set Point ◽  
Well Integrity ◽  
Innovative Solution ◽  
Analysis System

Abstract This paper describes an innovative solution for the safe and effective management of wells with unplanned sustained annulus pressure (SAP). The solution restores double barrier integrity in the well and provides reliable real time annulus pressure and temperature data. It also has the functionality to autonomously bleed-off the annulus pressure at a pre-determined set point. As a result, the nature and severity of the SAP can be better understood, and in many cases wells that would otherwise have been closed in awaiting workover can remain in production.

Real Time Cementing Hydraulics Simulations Bring Risk Down

2019 ◽  
Author(s):  
Sviatoslav Pelipenko ◽  
Nicolas C. Flamant ◽  
Simon C. Impey
Keyword(s):  
Real Time ◽  
Salient Feature ◽  
Fluid Viscosity ◽  
Cement Slurry ◽  
Well Integrity ◽  
Additional Input ◽  
Measurement While Drilling ◽  
Drilling Operations ◽  
Real Time Simulations

Abstract Proper control of downhole pressure during cementing operations is critical to maintaining well integrity, i.e. avoiding getting a well kick or fracturing the formation. Contrary to drilling operations where pressure can be monitored in real time thanks to measurement while drilling by downhole tools, no such measurements are available while cementing. Cementing operations must therefore rely on the use of simulations to estimate pressures downhole and ensure that the well integrity is not compromised. These simulations are typically performed ahead of the operations, but for critical wells it is paramount to also perform the calculations in real time to account for any deviation from the plan. We will first provide a description of the hydraulics simulator used for real time simulations. A key feature is the ability to account for fluctuations in injected fluid density, as a result of the cement slurry mixing process. This effectively results in tracking a high number of fluids with different density properties. The simulator also takes into account fluid compressibility and pressure and temperature dependent fluid viscosity, the magnitude of the effects of which we examine in application to generic field cases. Another salient feature of the simulator is its ability to determine whether fluid is lost to the formation by using flow returning from the well as an additional input. We highlight the work accomplished to achieve the performance required for real time computations and then illustrate how the simulator gets used during operation through a case study.

Pollution based real time control strategies for combined sewer systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 331-336 ◽  
Author(s):  
Gabriela Weinreich ◽  
Wolfgang Schilling ◽  
Ane Birkely ◽  
Tallak Moland
Keyword(s):  
Real Time ◽  
Control Strategies ◽  
Ammonia Nitrogen ◽  
Simple Extension ◽  
Real Time Control ◽  
Time Control ◽  
Sewer Systems ◽  
Receiving Waters ◽  
Tunnel System

This paper presents results from an application of a newly developed simulation tool for pollution based real time control (PBRTC) of urban drainage systems. The Oslo interceptor tunnel is used as a case study. The paper focuses on the reduction of total phosphorus Ptot and ammonia-nitrogen NH4-N overflow loads into the receiving waters by means of optimized operation of the tunnel system. With PBRTC the total reduction of the Ptot load is 48% and of the NH4-N load 51%. Compared to the volume based RTC scenario the reductions are 11% and 15%, respectively. These further reductions could be achieved with a relatively simple extension of the operation strategy.

scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

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