Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30469
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Salo, Erik
McMillan, David
Connor, Richard
Title: Work orders - Value from structureless text in the era of digitisation
Citation: Salo E, McMillan D & Connor R (2019) Work orders - Value from structureless text in the era of digitisation. In: SPE Offshore Europe Conference and Exhibition 2019, OE 2019. SPE Offshore Europe Conference and Exhibition 2019, Aberdeen, UK, 03.09.2019-06.09.2019. Richardson, TX, USA: Society of Petroleum Engineers. https://doi.org/10.2118/195788-MS
Issue Date: 2019
Date Deposited: 18-Nov-2019
Conference Name: SPE Offshore Europe Conference and Exhibition 2019
Conference Dates: 2019-09-03 - 2019-09-06
Conference Location: Aberdeen, UK
Abstract: Free text and hand-written reports are losing ground to digitization fast, however many hours of effort are still lost across the industry to the manual creation and analysis of these data types. Work orders in particular contain valuable information from failure rates to asset health, but at the same time present operators with such analytical difficulties and lack of structure that many are missing out on the value completely. This research challenges the current mainstream practice of manual work order analysis by presenting a methodology fit for today’s context of efficiency and digitization. A prototype text mining software for work order analysis was developed and tested in a user-oriented approach in cooperation with industrial partners. The final prototype combines classical machine learning methods, such as hierarchical clustering, with the operator’s expert knowledge obtained via an active learning approach. A novel distance metric in this context was adapted from information-theoretical research to improve clustering performance. Using the prototype tool in a case study with real work order data, analytical effort for certain datasets was reduced by 90% - from two working weeks to a day. In addition, the active learning framework resulted in an approach that end users described as "practical" and "intuitive" during testing. An in-depth review was also conducted regarding the uncertainty of the results – a key factor for implementation in a decision-making context. The outcomes of this work showcase the potential of machine learning to drive the digitization of not only new installations, but also older assets, where as a result the large amount of unstructured historical data becomes an advantage rather than a hindrance. User testing results encourage a wider uptake of machine learning solutions in the industry, and particularly a shift towards more accessible in-house analytical capabilities.
Status: AM - Accepted Manuscript
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Licence URL(s): http://www.rioxx.net/licenses/under-embargo-all-rights-reserved

Files in This Item:
File Description SizeFormat 
Saloet al-SPEOE-2019.pdfFulltext - Accepted Version1.37 MBAdobe PDFUnder Permanent Embargo    Request a copy

Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.



This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.