A Case Study of Real-Time Drilling Optimization to Improve Well Delivery through Enhancing Drilling Rates and Identifying Invisible Lost Time to Improve Performance

2015 ◽  
Author(s):  
Rafik Zakariya ◽  
Alaa Zein ◽  
Emad Diab ◽  
Amr Lotfy ◽  
Chris Marland ◽  
...  
Keyword(s):  
Real Time ◽  
Improve Performance ◽  
Drilling Optimization ◽  
Lost Time

Integrated Geomechanical Operations for Successful Field Development: A Case Study from Western Offshore, India

2021 ◽  
Author(s):  
Pawandeep Bagga ◽  
Tapan Kidambi ◽  
Ashish Sharma ◽  
Anjana Panchakarla ◽  
Aditee Kulkarni
Keyword(s):  
Real Time ◽  
Oil And Gas ◽  
Integrated Approach ◽  
Field Development ◽  
Drilling Optimization ◽  
Fit For Purpose ◽  
High Level ◽  
Offshore Field ◽  
Jack Up

Abstract This paper deals with the field development study for an offshore field in the western part of India. The main points of focus are holistic execution of integrated workflows for the delivery of subsea oil and gas wells from a jack up platform in this region. Given that the encountered formations encountered in wells posed significant challenges during the drilling phase, a field level geomechanics study was commissioned to understand and mitigate any challenges and effect smooth drilling and logging operations. Understanding the geomechanical effects by analysing the offset wells drilled in the region provided significant insights into the potential challenges faced while exploring target formations. The proposed well locations were drilled in a structurally complex geological setting. From the analysis of previously drilled wells in the region, it was evident that the variation in insitu properties of the lithologies and the extreme heterogeneity and vugular nature of the encountered carbonates caused significant drillability issues with subsequent losses, excessive cuttings, and several back reaming cycles impacting rig time and leading to generally poor borehole conditions. On the other hand, the shales encountered at shallower depths presented a different challenge, especially with a high swelling tendency, adding to progressively worsening hole conditions and significant fluid invasion. Finally, the basal clastics and the depleted zones with variable rock strengths added to the borehole instability issues, with particular zones projecting losses while others showed influxes. In light of such a plethora of issues, an integrated approach including dynamic real time monitoring of operations, structured LWD and wireline logging programmes, a high level petrophysics, formation evaluation and borehole acoustics for shear radial profiling was carried out. A fit for purpose geomechanical model was built encompassing the results of these analyses and was continually updated in real time during the operations phase. Given the variability in the pressures, temperatures and operational mud weights in each section, execution for successful delivery of the wells was further aided by identification of the optimal mud systems, critical casing setting depths and real time drilling optimization, ensuring good borehole quality throughout for further logging and testing programmes.

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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