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INNOVATION & TECHNOLOGY ZONE
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BIG DATA ANALYTICS Intertek is an industry leader with more than 46,000 employees in 1,000 locations. We deliver Total Quality Assurance expertise 24 hours a day, 7 days a week with our industry-winning processes and customer-centric culture. Interpret is part of Intertek’s portfolio of technology innovations, which allows Intertek to work with clients to extract the value from their data and deliver solutions.
Company Details Website: www.intertek.com/ep Email: production.support@intertek.com Tel: +44 (0) 1224 708 500 Address: Exploration Drive, Aberdeen Science & Energy Park, Bridge of Don, Aberdeen, AB23 8HZ, Scotland, UK
Technology Development stage: Commercial Launch date: 2018
INNOVATION &
TECHNOLOGY
IN ENERGY ANNUAL
202 2 www.ogv.energy I August 2022
Although big data analytics is a hot topic in many sectors, companies from banking to pharmaceuticals have only recently started to realise the value locked in their data. The Oil and Gas industry is not exempt from this, and it’s safe to say that it is no stranger to large amounts of process data. For many years data historians have worked away collecting measurements from instrumentation around oil and gas processes globally. The data is large and complex with many interactions and correlations between variables which are not easily interpreted.
What is Big Data? Put simply big data is a collection of information gathered through various means which is so large and complex that significant benefits can only be extracted through the application of computational algorithms. Indeed the exponential development of computational processing power and storage is the key driving force behind the wider application of big data analytics.
Upstream production involves many complex processes and numerous properties are constantly monitored including temperature, pressure, flow rates, GOR. As there are numerous processes occurring simultaneously, it can sometimes be difficult to pinpoint exactly where an error is originating from and this is where data analytics can step in. To give an example, a client approached Intertek as they were having intermittent problems with their upstream process. The issue was resulting in damage to certain seals, and was often a cause for shutdown. To identify where the problem was occurring, Intertek received data from around the installation. The data contained information from periods of normal operation as well as during process disruptions. Intertek undertook data analysis with two primary objectives:
1.
Identify root cause for process disruption
2. Establish a method for more efficient process monitoring
To promote efficiency, it was necessary to automate the process to monitor the performance of every process unit through the combinatorial analysis of all key instrumentation measurements. In this example the dataset contained measurements from over 150 process sensors. Taking the data every 2 minutes over 2 months gives The Status Quo 43,000 time periods and therefore 6.5 million data points! The need to reduce the It is a fact that contained within large complexity of the dataset into historical datasets is valuable easy to visualise trends and information and knowledge that, correlations which focus on the when coupled with domain Intertek uses key parameters and add value expertise, can be used to to the process will now be achieve a variety of benefits proprietary software demonstrated for including: more efficient (Interpret) in order to this example. maintenance, scheduling, improved performance, undertake big data Data Purification reduced downtime and analytics activities maximised margins. To maximise value from data it is key to understand outliers and However, this valuable ‘clean’ the data set before moving information is hidden in the forward to any additional analysis such quantity of data and further as process modelling, optimisation etc. Data compounded by dataset issues such as purification can be a lengthy process and, it is the noise from unrepresentative operation i.e. our experience that, a large portion of all big process upsets, malfunctioning instrumentation data analytics activities is around screening etc. Hence, to simplify analytics, operators and pre-processing the data to ensure usually focus on time series trending and first conclusions are valid. order effects.
