An offshore equipment data forecasting system

Ahmad Syazwan Sahdom, Alan Cheah Kah Hoe, Jaspaljeet Singh Ranjit Singh

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
PublisherSpringer
Pages115-126
Number of pages12
DOIs
Publication statusPublished - 01 Jan 2019

Publication series

NameLecture Notes in Networks and Systems
Volume67
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Fingerprint

Alarm systems
Industry
Gas industry
Statistical Models
Gases
Seawater
Linear regression
Flow rate
Pumps
Monitoring
Testing
Oils

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

Cite this

Sahdom, A. S., Hoe, A. C. K., & Ranjit Singh, J. S. (2019). An offshore equipment data forecasting system. In Lecture Notes in Networks and Systems (pp. 115-126). (Lecture Notes in Networks and Systems; Vol. 67). Springer. https://doi.org/10.1007/978-981-13-6031-2_25
Sahdom, Ahmad Syazwan ; Hoe, Alan Cheah Kah ; Ranjit Singh, Jaspaljeet Singh. / An offshore equipment data forecasting system. Lecture Notes in Networks and Systems. Springer, 2019. pp. 115-126 (Lecture Notes in Networks and Systems).
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Sahdom, AS, Hoe, ACK & Ranjit Singh, JS 2019, An offshore equipment data forecasting system. in Lecture Notes in Networks and Systems. Lecture Notes in Networks and Systems, vol. 67, Springer, pp. 115-126. https://doi.org/10.1007/978-981-13-6031-2_25

An offshore equipment data forecasting system. / Sahdom, Ahmad Syazwan; Hoe, Alan Cheah Kah; Ranjit Singh, Jaspaljeet Singh.

Lecture Notes in Networks and Systems. Springer, 2019. p. 115-126 (Lecture Notes in Networks and Systems; Vol. 67).

Research output: Chapter in Book/Report/Conference proceedingChapter

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Sahdom AS, Hoe ACK, Ranjit Singh JS. An offshore equipment data forecasting system. In Lecture Notes in Networks and Systems. Springer. 2019. p. 115-126. (Lecture Notes in Networks and Systems). https://doi.org/10.1007/978-981-13-6031-2_25