What is Soft-sensor?
Soft-sensor is a fancy term for the parameters which are derived as a function of the sensor- captured parameters. These could also be termed as “Latent Variables”, which are not readily available, but is required as a key metric to estimate the process/asset performance.
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How to derive a Soft-sensor?
This question becomes important in-order to devise a right metric to derive the soft-sensor. These parameters could either be estimated through data-driven techniques or through first principle models.
Typically, we leap through the following steps to generate a soft-sensor:
- Select/Evaluate the right soft-sensor which is required to estimate the performance
- Gather all the required constants or the sensor signals which will be required to develop it.
- Look for the possible ways (Data-driven/Mechanistic) to evaluate it
- Test it in the production environment, and estimate it’s direct inference in the real-time operations
- If satisfactory, then use various forecasting rules/techniques to estimate the performance in the successive events
Soft-Sensor in Oil and Gas Industry
Soft-sensors has recently observed a lot of attraction in Oil and Gas industry. It’s been used heavily for assessing the performance of the industry enablers/assets such as Heat Exchangers, pumps, compressor.etc. Deriving such parameters becomes hard when we try to implement it in a process which is heavily governed by the first-principle/mechanistic models. This also brings in the requirement of integrating those models into the analytics workspace for a convenient and reliable analysis.
Let us look into the two different scenarios(Use cases) where we will be using soft-sensors derived through 1) Advanced Machine Learning Techniques, and 2)Mechanistic Techniques:
Use case I:
Debutanizer column performance prediction:
For debutanizer column performance is dictated by the extent of separation that happens to obtain a high purity C4 fraction from the tops as LPG (gasoline is obtained as the bottoms).
The challenge with this process comes when the gas chromatograph which is used for real time monitoring of the C4 fraction in the stream is off-due to maintenance. Such situation handicaps the operator/engineers to qualify the process performance. The lab analysis for estimating the quality of the product takes a while(few hours) to verify the process performance that happened in the past. This shows that gas chromatograph becomes a necessity to evaluate the process performance in real time, but due to its own maintenance challenges it brings in a major requirement to replace it with the possible mathematical model that can operate for all time without any operational failure in it’s own. This mathematical model can also be a potential replacement for lab analysis if developed with all the required parameters and assumptions.
According to a new market report published by Transparency Market Research, the global oil and gas data monetization market is expected to reach a value of US$ 81,600.0 Mn by 2026 on account of large volume of data generated across the oil and gas industry and the selling/trading or the software/platform usage to derive value from such large volumes of structured, unstructured, and semi-structured data.
The market is projected to expand at a CAGR of 18.5% during the forecast period from 2018 to 2026 driven by the increasing trading of data products such as seismic data, geophysical data, magnetic surveys, well data, production data, interpretive studies, etc.
and the increasing adoption of data management and data analytics solutions in the oil and gas industry.
After the fall in CAPEX during the years 2015 to 2017, spending in the oil and gas industry is anticipated to increase gradually from 2018, thus further driving the digitization across the industry using software/platforms and related services.
North America is anticipated to be the dominant region in the oil and gas data monetization market followed by Middle East & Africa during the forecast period.The exploration segment in the E lifecycle is expected to see flourishing growth to maintain the production and to meet the demand in the next decade with sufficient oil and gas discoveries now.
Further, the use of analytics solutions and data management solutions mainly for seismic data, drilling data, and production data processing drives the data monetization market in the oil and gas industry.
