Implementation of a Fully Automated Real-Time Torque and Drag Model for Improving Drilling Performance: Case Study

2018 ◽  
Author(s):  
Mojtaba Shahri ◽  
Timothy Wilson ◽  
Taylor Thetford ◽  
Brian Nelson ◽  
Michael Behounek ◽  
...  
Keyword(s):  
Real Time ◽  
Drag Model ◽  
Drilling Performance

An Innovative Approach Towards Real-Time Torque and Drag Model and its Analysis - A Case Study

2017 ◽  
Author(s):  
Mohammed Alsharif ◽  
Musab Al Khudiri ◽  
Bassam Albassam ◽  
Kamran Ahmad Khan
Keyword(s):  
Real Time ◽  
Innovative Approach ◽  
Drag Model ◽  
Its Analysis

Bottom Hole Assembly Design Enhancement Successfully Eliminate Hole Problems and Reduce Significant Drilling Time : South S Field Case Study

2020 ◽  
Author(s):  
Y. D. Mulia
Keyword(s):  
Contact Area ◽  
Cost Savings ◽  
Assembly Design ◽  
Drilling Performance ◽  
Bottom Hole ◽  
Unconsolidated Sand ◽  
Bottom Hole Assembly ◽  
Drilling Parameter ◽  
Drilling Time

For S-15 and S-14 wells at South S Field, drilling of the 12-1/4” hole section became the longest tangent hole section interval of both wells. There were several challenges identified where hole problems can occur. The hole problems often occur in the unconsolidated sand layers and porous limestone formation sections of the hole during tripping in/out operations. Most of the hole problems are closely related to the design of the Bottom Hole Assembly (BHA). In many instances, hole problems resulted in significant additional drilling time. As an effort to resolve this issue, a new BHA setup was then designed to enhance the BHA drilling performance and eventually eliminate hole problems while drilling. The basic idea of the enhanced BHA is to provide more annulus clearance and limber BHA. The purpose is to reduce the Equivalent Circulating Density (ECD,) less contact area with formation, and reduce packoff risk while drilling through an unconsolidated section of the rocks. Engineering simulations were conducted to ensure that the enhanced BHA were able to deliver a good drilling performance. As a results, improved drilling performance can be seen on S-14 well which applied the enhanced BHA design. The enhanced BHA was able to drill the 12-1/4” tangent hole section to total depth (TD) with certain drilling parameter. Hole problems were no longer an issue during tripping out/in operation. This improvement led to significant rig time and cost savings of intermediate hole section drilling compared to S-15 well. The new enhanced BHA design has become one of the company’s benchmarks for drilling directional wells in South S Field.

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.

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