[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119066-en":3,"doc-seo-119066-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119066,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Application of machine learning modeling for the upstream oil and gas industry injury rate prediction - Research","This study addresses workplace injury prediction in the upstream oil and gas sector, where incident rates can be elevated due to demanding work environments and operational conditions. It evaluates how tailored safety procedures and well-designed HSE mitigation can reduce risk by enabling earlier identification of likely incidents. Three machine-learning models are implemented on Petroleum Safety Authority of Norway datasets for drilling operations, including preprocessing, feature selection, model training, and performance accuracy assessment.","ISSN: 2091-0878 (Online) ISSN: 2738-9707 (Print)  \nOriginal Article  \nApplication of machine learning modeling for the upstream oil and gas industry injury rate prediction  \nDesalegn Y1, Daniel K1, Mesfin B2  \n1 School of Mechanical and Industrial Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia.  \n2 University of Stavanger, Department of Energy and Petroleum Engineering, Stavanger, Norway.  \nCorresponding authors:  \nDesalegn Yeshitila, & Professor Daniel Kitaw (PhD) School of Mechanical and Industrial Engineering,  \nAddis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia  \nTel.: +251932768383  \n[E-mail: ](E-mail: desuselam@yahoo.co.uk)[desuselam@yahoo.co.uk](E-mail: desuselam@yahoo.co.uk)  \n[E-mail: ](E-mail: danielkitaw@yahoo.com)[danielkitaw@yahoo.com](E-mail: danielkitaw@yahoo.com)  \nORCID ID: [https://orcid.org/0000-](https://orcid.org/0000-)[ ](https://orcid.org/0000-)[0002-2696-3548](0002-2696-3548)  \nDate of submission: 21.02.2023  \nDate of acceptance: 11.09.2023  \nDate of publication: 01.04.2024  \nConflicts of interest: None  \nSupporting agencies: None  \nDOI:[https://doi.org/10.3126/ijosh](https://doi.org/10.3126/ijosh)  \n[.v14i2.52668](.v14i2.52668)  \nCopyright: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License  \nABSTRACT  \nIntroduction: Yearly, the International Labor Organization report indicates many workplace accident occurrences. The degree of the happenings depends on the workplace environment setting and the incident regulatory measures implemented. By the nature of its work environment, the oil and gas upstream sector is susceptible to high incident rates. In the current fierce business competition and practices, improving productivity, quality, and other processes, such as Safety, is vital. Implementing well-designed safety procedures is the key to managing and reducing the risk level of workplace incidents.  \nMethods: Recently, the application of Machine learning (ML) modeling for accident/injury prediction has been reported in the construction, mining, transport, and health sectors. Likewise, the objective of this paper was to implement three machine-learning-based models to predict injury rates in a drilling operation. The Petroleum Safety Authority of Norway provided the datasets. First, the dataset was pre-processed, and then the selected features and target dataset were used for the modeling. Finally, the model prediction and performance accuracy analysis were performed.  \nResults: Results showed that multivariable regression (MVR), Random Forest (RF), and Artificial Neural Network (ANN) machine learning algorithms-based models predict the test data with R2 values of 0.9576, 0.793, and 0.97036, respectively.  \nConclusion: As the common saying goes, 'prevention is better than cure.' For this, implementing methods such as improved work processes and Health, Safety, and Environment (HSE) mitigation procedures, workplace injuries, and accidents allow for reducing the risk level of workplace injuries. The application of integrated machine learning tools, along with carefully built-in workplace accident database implementation, will provide early detection and possible remedial precautions that can be taken to prevent workplace injuries/accidents/fatalities. However, extensive research and development are required to deploy the method in real life. Combining Machine Learning modeling and carefully designed safety measures is vital for successful and robust predictive tools.  \nKeywords: ANN, HSE, Multivariate Regression, Occupational injury, Random Forest, Safety Management  \nIntroduction  \nOccupational accidents and occupational injuries can happen anytime and in any field. Occupational injury includes personal injury or fatality from work accidents. The consequences can affect the employees' performance and personal life outside of work. A report from the International Labor Organizatio","cbCaij5aLoCtQvck","https://ap.wps.com/l/cbCaij5aLoCtQvck","pdf",652928,1,14,"English","en",105,"# Abstract\n# Introduction\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What was the main objective of this paper?\",\"answer\":\"To implement three machine-learning-based models to predict injury rates in a drilling operation for the upstream oil and gas industry.\"},{\"question\":\"What data source was used for modeling?\",\"answer\":\"Datasets provided by the Petroleum Safety Authority of Norway were used, followed by preprocessing and feature/target preparation.\"},{\"question\":\"Which models performed best and how were they evaluated?\",\"answer\":\"Multivariable regression, Random Forest, and Artificial Neural Network predicted test data with R2 values of 0.9576, 0.793, and 0.97036 respectively, assessed through prediction performance accuracy.\"}]","Application of machine learning modeling for the upstream oil and gas industry injury rate prediction - 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