scholarly journals Addressing Diverse Petroleum Industry Problems Using Machine Learning Techniques: Literary Methodology─Spotlight on Predicting Well Integrity Failures

ACS Omega ◽  
2022 ◽  
Author(s):  
Adel M. Salem ◽  
Mostafa S. Yakoot ◽  
Omar Mahmoud
Keyword(s):  
Machine Learning ◽  
Petroleum Industry ◽  
Machine Learning Techniques ◽  
Well Integrity ◽  
Learning Techniques

IR 4.0 Technologies Enable Breakthroughs in Upstream Production Operations

2021 ◽  
Author(s):  
Nasser M. Al-Hajri ◽  
Sulaiman T. Ureiga ◽  
Akram R. Barghouti ◽  
Syed K. Gilani ◽  
Muhammad Imran Javed
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Industrial Revolution ◽  
Petroleum Industry ◽  
Big Data Analytics ◽  
Scale Formation ◽  
Machine Learning Techniques ◽  
Optimum Frequency ◽  
Well Integrity ◽  
Scale Inhibition

Abstract The fourth industrial revolution (IR 4.0) has brought about many exciting and game changing technological advancements in recent years that span across different industries. Our petroleum industry was no exception. In this paper, we will present realizations of IR 4.0's fruitful impact on multiple upstream production engineering and operation problems. The first IR 4.0 technology uses machine learning techniques to predict scale inhibition and design inhibition programs that arrest scale formation. Scale formation is a common oilfield problem that consumes a lot of expense from operators. The machine learning method has shown its ability to curtail such expenses and manage risks associated with scale formation. The second technology is modeling the reliability of downhole Inflow Control Valves (ICVs) and predicting their failure. The technology is based on advanced big data analytics and uses automated statistical techniques to achieve the method objectives. This technology provides production engineers with an analytical decision-making model to predict ICVs failures and suggest the optimum frequency for stroking or cycling of the downhole valves as a preventive maintenance practice. The third IR 4.0 technology is the automated well integrity risk ranking. This particular technology uses smart interfaces and advanced computation algorithms applied on big data to assign (or weigh) risks of a well in terms of well integrity. This intelligent integrity ranking or classification shifts focus to wells prone to integrity failures more than the healthy ones. In addition, the method helps optimize integrity surveillance resources and prevents the obvious setbacks from a well integrity issue. The paper will explain detailed methodologies of all three IR 4.0 technologies and outline expected results from field implementation of those technologies.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

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