Although big data analytics is a hot topic across all sectors, many companies from banking to pharmaceuticals are only recently beginning to realise the value locked in their data.
It is safe to say that the oil and gas industry are not exempt from this and are 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.
It is a fact that contained within large historical datasets is valuable information and knowledge that, when coupled with domain expertise, can be used to achieve a variety of benefits including: more efficient maintenance scheduling, improved performance, reduced downtime and maximised margins.
However, the quantity and noise from unrepresentative operations, such as process upsets and malfunctioning instrumentation, creates a challenge to unlocking valuable information that can be used to improve performance.
Our lnProcess application can reduce the complexity of large datasets, offering the user an interface of easily accessible plots and summary tables with trends and correlations.
Features such as missing data, constant values, faulty sensors and questionable values can be analysed through the system.
Our proprietary technology lnProcess is designed to assist our clients in cleansing and ordering historical data to gain valuable information that may positively impact processes and profit.
When analysing data and making decisions based on variables, operations usually focus on first order, i.e. the most obvious, effects.
For example, flow rate into a vessel on a platform is known to have a direct effect on its fluid level, whereas the pressure sensors on a compressor would typically not be considered. However, pressure drop across a compressor is indicative of compressor efficiency which, in turn, has an effect on separator levels.
In refineries, the flow rate of medium gas oil (MGO) is a function of many parameters such as reflux ratios, reboiler heat load and temperature of the feed - all first-order effects.
However, there are other factors affecting this which would not be directly linked to MGO production such as the temperature of the desalter or the temperature of a slops stream being added to the crude blend. Both have an effect on CDU feed temperature.
Functions and Benefits - InProcess offers many functions and benefits including:
lnProcess can import large datasets from data historians. It then gives the user an interface, consisting of plots and summary tables, to view this data. To maximise value from this, it is key to understand outliers and 'clean' datasets before additional analysis such as process modelling and optimisation.
lnProcess allows the user to examine the data and visualise any points that may be missing, constant values, faulty sensors and questionable values. Plots can then be used to identify process outliers by looking at combinations of process variables.
lnProcess allows the user to visualise operating regions drawn from snapshots of the combinations of operating variables at a particular time horizon.
Following identification, they can then be correlated to key process variables. Operating regions can highlight process nuances such as decreasing process unit efficiency, shift changes and changing crude blend composition.
lnProcess takes large datasets, displays their underlying trends and shows the variables which have maximum effect. It can pick out relationships caused by combinations of variables and other underlying effects as well as first-order relationships.
However, process data is complex, and there are many interactions. Statistical analysis uncovers the underlying combinatorial effects by selecting a target /objective function based on process requirements, for example, maximising flowrate, minimising oily water overboard or maximising the production of diesel.
Read the latest issue of the OGV Energy magazine HERE.