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.
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Sfetsos A (2002) A novel approach for the forecasting of mean hourly wind speed time series. Renew Energy 27:163–174. https://doi.org/10.1016/S09601481(01)00193-8
Chang W-Y (2014) A literature review of wind forecasting methods. J Power Energy Eng 2:161–168. https://doi.org/10.4236/jpee.2014.24023
Subbaiah NKCHV (2016) SARIMA modelling and forecasting of seasonal rainfall patterns in India. Retrieved from https://tinyurl.com/y884qyco
Breiman L (2001) Statistical modeling: the two cultures. Retrieved from https://projecteuclid.org/download/pdf_1/euclid.ss/1009213726
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Sahdom, A.S., Hoe, A.C.K., Dhillon, J.S. (2019). An Offshore Equipment Data Forecasting System. In: Piuri, V., Balas, V., Borah, S., Syed Ahmad, S. (eds) Intelligent and Interactive Computing. Lecture Notes in Networks and Systems, vol 67. Springer, Singapore. https://doi.org/10.1007/978-981-13-6031-2_25
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DOI: https://doi.org/10.1007/978-981-13-6031-2_25
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Online ISBN: 978-981-13-6031-2
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