Real-Time Water Injection Monitoring with Distributed Fiber Optics Using Physics-Informed Machine Learning

2021 ◽  
Author(s):  
Teymur Sadigov ◽  
Cagri Cerrahoglu ◽  
James Ramsay ◽  
Laurence Burchell ◽  
Sean Cavalero ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Real Time ◽  
Fiber Optics ◽  
Water Injection ◽  
The Real ◽  
Start Up ◽  
Naturally Fractured ◽  
Monitoring Application ◽  
Time Injection

Abstract This paper introduces a novel technique that allows real-time injection monitoring with distributed fiber optics using physics-informed machine learning methods and presents results from Clair Ridge asset where a cloud-based, real-time application is deployed. Clair Ridge is a structural high comprising of naturally fractured Devonian to Carboniferous continental sandstones, with a significantly naturally fractured ridge area. The fractured nature of the reservoir lends itself to permanent deployment of Distributed Fiber Optic Sensing (DFOS) to enable real-time injection monitoring to maximise recovery from the field. In addition to their default limitations, such as providing a snapshot measurement and disturbing the natural well flow with up and down flowing passes, wireline-conveyed production logs (PL) are also unable to provide a high-resolution profile of the water injection along the reservoir due to the completion type. DFOS offers unique surveillance capability when permanently installed along the reservoir interface and continuously providing injection profiles with full visibility along the reservoir section without the need for an intervention. The real-time injection monitoring application uses both distributed acoustic and temperature sensing (DAS & DTS) and is based on physics-informed machine learning models. It is now running and available to all asset users on the cloud. So far, the application has generated high-resolution injection profiles over a dozen multi-rate injection periods automatically and the results are cross-checked against the profiles from the warmback analyses that were also generated automatically as part of the same application. The real-time monitoring insights have been effectively applied to provide significant business value using the capability for start-up optimization to manage and improve injection conformance, monitor fractured formations and caprock monitoring.

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

Navigating Organisation in Times of VUCA: Implications for the HR and OD Profession

2019 ◽  
Vol 12 (2) ◽  
pp. 145-150
Author(s):  
Vasudevan Alasingachar
Keyword(s):  
Experiential Learning ◽  
Real Time ◽  
New Age ◽  
The Real ◽  
The Past ◽  
Formative Stage ◽  
Start Up ◽  
The Future ◽  
Specific Learning

This article addresses two vectors of VUCA interwoven in the narratives, a summary of personal theories about VUCA. Such theories are anchored and arise from experiential learning in my practice as HR/L&D and OD consultants over the past four decades. The implication for HR and OD profession is to consider their relevance when organisations navigate VUCA. Next is the culling out of the specific learning about HR and OD interphases that has worked in my experience, supported by examples and metaphors. The premise I put forward as conclusion are: In order to be at the centre stage of partnering with business, HR and OD have to complement and innovate new-age VUCA strategies. VUCA competencies with appropriate metrics are in the formative stage. The competencies are emerging from the real-time stories of consultants, companies and academia (TATA 26/11 and DuPont safety mandate). Only when HR and OD integrate and work together can the future of leadership or start-up entrepreneurs learn from their insights to ‘thrive in VUCA’.

scholarly journals Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3930 ◽  
Author(s):  
Ayaz Hussain ◽  
Umar Draz ◽  
Tariq Ali ◽  
Saman Tariq ◽  
Muhammad Irfan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Air Pollutants ◽  
Garbage Collection ◽  
Air Pollutant ◽  
Real Time Monitoring ◽  
Collection System ◽  
The Real ◽  
The Status

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

scholarly journals Rapid Diagnosis of Tuberculosis by Real-Time High-Resolution Imaging of Mycobacterium tuberculosis Colonies

2015 ◽  
Vol 53 (8) ◽  
pp. 2693-2696 ◽  
Author(s):  
Ramzi Ghodbane ◽  
Shady Asmar ◽  
Marlena Betzner ◽  
Marie Linet ◽  
Joseph Pierquin ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
High Resolution ◽  
Real Time ◽  
Solid Medium ◽  
Contamination Rate ◽  
High Resolution Imaging ◽  
Standard Protocol ◽  
Content Type ◽  
The Real ◽  
Resolution Imaging

Culture remains the cornerstone of diagnosis for pulmonary tuberculosis, but the fastidiousness ofMycobacterium tuberculosismay delay culture-based diagnosis for weeks. We evaluated the performance of real-time high-resolution imaging for the rapid detection ofM. tuberculosiscolonies growing on a solid medium. A total of 50 clinical specimens, including 42 sputum specimens, 4 stool specimens, 2 bronchoalveolar lavage fluid specimens, and 2 bronchial aspirate fluid specimens were prospectively inoculated into (i) a commercially available Middlebrook broth and evaluated for mycobacterial growth indirectly detected by measuring oxygen consumption (standard protocol) and (ii) a home-made solid medium incubated in an incubator featuring real-time high-resolution imaging of colonies (real-time protocol). Isolates were identified by Ziehl-Neelsen staining and matrix-assisted laser desorption ionization–time of flight mass spectrometry. Use of the standard protocol yielded 14/50 (28%)M. tuberculosisisolates, which is not significantly different from the 13/50 (26%)M. tuberculosisisolates found using the real-time protocol (P= 1.00 by Fisher's exact test), and the contamination rate of 1/50 (2%) was not significantly different from the contamination rate of 2/50 (4%) using the real-time protocol (P= 1.00). The real-time imaging protocol showed a 4.4-fold reduction in time to detection, 82 ± 54 h versus 360 ± 142 h (P< 0.05). These preliminary data give the proof of concept that real-time high-resolution imaging ofM. tuberculosiscolonies is a new technology that shortens the time to growth detection and the laboratory diagnosis of pulmonary tuberculosis.

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