Real Time Cloud-Based Automation for Formation Evaluation Optimization, Risk Mitigation and Decarbonization

Mapping Intimacies ◽

10.2118/205402-ms ◽

2021 ◽

Author(s):

Rebecca Nye ◽

Camilo Mejia ◽

Evgeniya Dontsova

Keyword(s):

Machine Learning ◽

Real Time ◽

Risk Mitigation ◽

High Rate ◽

Learning Models ◽

Sample Collection ◽

Formation Evaluation ◽

The Real ◽

Prediction Techniques ◽

Machine Learning Models

Abstract Recent developments in artificial intelligence (AI) have enabled upstream exploration and production companies to make better, faster and accurate decisions at any stage of well construction, while reducing operational expenditure and risk, increasing logistic efficiencies. The achieved optimization through digitization at the wellsite will significantly reduce the carbon emissions per well drilled when fully embraced by the industry. In addition, an industry pushed to drill in more challenging environments, they must embrace safer and more practical methods. An increase in prediction techniques, to generate synthetic formation evaluation wellbore logs, has unlocked the ability to implement a combination of predictive and prescriptive analytics with petrophysical and geochemical workflows in real time. The foundation of the real time automation is based on advanced machine learning (ML) techniques that are deployed via cloud connectivity. Three levels of logging precision are defined in the automated workflow based on the data inputs and machine learning models. The first level is the forecasting ahead of the bit that implements advanced machine learning using historical data, aiding proactive operational decisions. The second level has improved precision by incorporating real time drilling measurements and providing a credible contingency to for wellbore logging program. The last level incorporates petrophysical workflows and geochemical measurements to achieve the highest precision for logging prediction in the industry. Supervised and unsupervised machine learning models are presented to demonstrate the path for automation. Precision above 95% in the real time automated workflows was achieved with a combination of physics and advanced machine learning models. The automation of the workflow has assisted with optimization of logging programs utilizing technology with costly lost in hole charges and high rate of tool failures in offshore operations. The optimization has reduced the requirement for logistics associated with logging and eliminated the need for radioactive sources and lithium batteries. Highest precision in logging prediction has been achieved through an automated workflow for real time operations. In addition, the workflow can also be deployed with robotics technology to automate sample collection, leading to increased efficiencies.

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Comparative Study of Real Time Machine Learning Models for Stock Prediction through Streaming Data

JUCS - Journal of Universal Computer Science ◽

10.3897/jucs.2020.059 ◽

2020 ◽

Vol 26 (9) ◽

pp. 1128-1147

Author(s):

Ranjan Behera ◽

Sushree Das ◽

Santanu Rath ◽

Sanjay Misra ◽

Robertas Damasevicius

Keyword(s):

Machine Learning ◽

Real Time ◽

Historical Data ◽

Streaming Data ◽

Support Vector ◽

Learning Models ◽

Stock Prediction ◽

The Real ◽

Lambda Architecture ◽

Machine Learning Models

Stock prediction is one of the emerging applications in the field of data science which help the companies to make better decision strategy. Machine learning models play a vital role in the field of prediction. In this paper, we have proposed various machine learning models which predicts the stock price from the real-time streaming data. Streaming data has been a potential source for real-time prediction which deals with continuous ow of data having information from various sources like social networking websites, server logs, mobile phone applications, trading oors etc. We have adopted the distributed platform, Spark to analyze the streaming data collected from two different sources as represented in two case studies in this paper. The first case study is based on stock prediction from the historical data collected from Google finance websites through NodeJs and the second one is based on the sentiment analysis of Twitter collected through Twitter API available in Stanford NLP package. Several researches have been made in developing models for stock prediction based on static data. In this work, an effort has been made to develop scalable, fault tolerant models for stock prediction from the real-time streaming data. The Proposed model is based on a distributed architecture known as Lambda architecture. The extensive comparison is made between actual and predicted output for different machine learning models. Support vector regression is found to have better accuracy as compared to other models. The historical data is considered as a ground truth data for validation.

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Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

Applied Clinical Informatics ◽

10.1055/s-0041-1726422 ◽

2021 ◽

Vol 12 (02) ◽

pp. 372-382

Author(s):

Christine Xia Wu ◽

Ernest Suresh ◽

Francis Wei Loong Phng ◽

Kai Pik Tai ◽

Janthorn Pakdeethai ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Real Time ◽

Risk Score ◽

Patient Specific ◽

Learning Models ◽

Medicine Department ◽

High Risk Patients ◽

Risk Patients ◽

Machine Learning Models

Abstract Objective To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions. Methods Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. Results Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. Conclusion Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.

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Machine Learning Models for Stock Prediction Using Real-Time Streaming Data

Learning and Analytics in Intelligent Systems - Biologically Inspired Techniques in Many-Criteria Decision Making ◽

10.1007/978-3-030-39033-4_10 ◽

2020 ◽

pp. 101-108

Author(s):

Monalisa Jena ◽

Ranjan Kumar Behera ◽

Santanu Kumar Rath

Keyword(s):

Machine Learning ◽

Real Time ◽

Streaming Data ◽

Learning Models ◽

Stock Prediction ◽

Machine Learning Models

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Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives

Computational Intelligence and Neuroscience ◽

10.1155/2021/3941978 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Khalid Twarish Alhamazani ◽

Jalawi Alshudukhi ◽

Saud Aljaloud ◽

Solomon Abebaw

Keyword(s):

Machine Learning ◽

Kidney Diseases ◽

Recognition Performance ◽

High Rate ◽

Learning Models ◽

Industry Standard ◽

Global Health Issue ◽

To Receive ◽

Decision Forest ◽

Machine Learning Models

Chronic kidney disease (CKD) is a global health issue with a high rate of morbidity and mortality and a high rate of disease progression. Because there are no visible symptoms in the early stages of CKD, patients frequently go unnoticed. The early detection of CKD allows patients to receive timely treatment, slowing the disease’s progression. Due to its rapid recognition performance and accuracy, machine learning models can effectively assist physicians in achieving this goal. We propose a machine learning methodology for the CKD diagnosis in this paper. This information was completely anonymized. As a reference, the CRISP-DM® model (Cross industry standard process for data mining) was used. The data were processed in its entirety in the cloud on the Azure platform, where the sample data was unbalanced. Then the processes for exploration and analysis were carried out. According to what we have learned, the data were balanced using the SMOTE technique. Four matching algorithms were used after the data balancing was completed successfully. Artificial intelligence (AI) (logistic regression, decision forest, neural network, and jungle of decisions). The decision forest outperformed the other machine learning models with a score of 92%, indicating that the approach used in this study provides a good baseline for solutions in the production.

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