An offshore equipment data forecasting system
In the oil and gas industry, various machineries and equipment are used to perform oil and gas extractions. The problem arises when there is unplanned maintenance on any equipment. Unplanned maintenance will result in unplanned deferments that disrupt business operations. Companies may have develope...
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2023
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| author | Sahdom A.S. Hoe A.C.K. Dhillon J.S. |
| author2 | 57208926305 |
| author_facet | 57208926305 Sahdom A.S. Hoe A.C.K. Dhillon J.S. |
| author_sort | Sahdom A.S. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | In the oil and gas industry, various machineries and equipment are used to perform oil and gas extractions. The problem arises when there is unplanned maintenance on any equipment. Unplanned maintenance will result in unplanned deferments that disrupt business operations. Companies may have developed monitoring systems based on current and historical equipment statuses, but ideally there should be mechanisms to conduct or produce real-time forecasts on equipment conditions. In this paper, linear regression models were tested and deployed in a system developed to forecast flow rate of seawater lift pumps of an offshore platform. Apart from identifying and evaluating a suitable statistical model to derive the forecasts, this paper presents a tool that was developed using the selected model to automate real-time data extraction and execute the prediction process. The models were developed based on raw data that were accumulated from an oil and gas company over a period of 3 months. Of the 3 months� data, the first 2 months of data were used as the training data, and the last one month was used for testing the models. Data cleansing was performed on the dataset whereby unwanted values that could affect accuracy of the model or any other data with values not processable by the models were eliminated. Results indicated that Autoregressive (AR) model is suitable for a real-time prediction of an offshore equipment. � Springer Nature Singapore Pte Ltd. 2019. |
| format | Book Chapter |
| id | my.uniten.dspace-25013 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Springer |
| record_format | dspace |
| spelling | my.uniten.dspace-250132023-05-29T15:30:22Z An offshore equipment data forecasting system Sahdom A.S. Hoe A.C.K. Dhillon J.S. 57208926305 56105282800 7003949854 In the oil and gas industry, various machineries and equipment are used to perform oil and gas extractions. The problem arises when there is unplanned maintenance on any equipment. Unplanned maintenance will result in unplanned deferments that disrupt business operations. Companies may have developed monitoring systems based on current and historical equipment statuses, but ideally there should be mechanisms to conduct or produce real-time forecasts on equipment conditions. In this paper, linear regression models were tested and deployed in a system developed to forecast flow rate of seawater lift pumps of an offshore platform. Apart from identifying and evaluating a suitable statistical model to derive the forecasts, this paper presents a tool that was developed using the selected model to automate real-time data extraction and execute the prediction process. The models were developed based on raw data that were accumulated from an oil and gas company over a period of 3 months. Of the 3 months� data, the first 2 months of data were used as the training data, and the last one month was used for testing the models. Data cleansing was performed on the dataset whereby unwanted values that could affect accuracy of the model or any other data with values not processable by the models were eliminated. Results indicated that Autoregressive (AR) model is suitable for a real-time prediction of an offshore equipment. � Springer Nature Singapore Pte Ltd. 2019. Final 2023-05-29T07:30:22Z 2023-05-29T07:30:22Z 2019 Book Chapter 10.1007/978-981-13-6031-2_25 2-s2.0-85066142194 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066142194&doi=10.1007%2f978-981-13-6031-2_25&partnerID=40&md5=fbf4a7a55dae31dfb122cf04dd4f72d7 https://irepository.uniten.edu.my/handle/123456789/25013 67 115 126 Springer Scopus |
| spellingShingle | Sahdom A.S. Hoe A.C.K. Dhillon J.S. An offshore equipment data forecasting system |
| title | An offshore equipment data forecasting system |
| title_full | An offshore equipment data forecasting system |
| title_fullStr | An offshore equipment data forecasting system |
| title_full_unstemmed | An offshore equipment data forecasting system |
| title_short | An offshore equipment data forecasting system |
| title_sort | offshore equipment data forecasting system |
| url_provider | http://dspace.uniten.edu.my/ |