CAMO Software
Oil & Gas

Oil & Gas


Oil & Gas is an industry faced with with constant challenges, including fluctuating prices and product demands, technological innovations and environmental impact issues. It is becoming ever more important to be in the front seat of the evolution of the industry and to constantly think smarter when it comes to locating new reserves and utilizing these reserves, minimizing dry wells and keep the cost as low as possible. Therefore, more and more oil and gas companies are investing in improved process control and data analytics to optimize their operations and give them a competitive advantage.

CAMO Software’s Unscrambler® X suite uses multivariate data analysis (MVA) to retrieve information from huge and complex data sets. It sets itself apart from univariate analysis by examining all given variables and their relationships holistically, instead of isolating them in groups of two or three. This means that the final result is both more accurate, as well as offering information unavailable through univariate analysis. When it comes to energy, the large amount of variables and often massive sample sizes make MVA an ideal solution for the industry.

Whether it is for composition analysis, trend analysis, on-line monitoring of a production process or predictive modeling, MVA can be a very valuable tool.


MVA is widely used in the oil and gas sector for performance prediction, understanding complex processes, planning maintenance and forecasting. Specific applications include:

  • Implementation and improvement of process control with multivariate analysis
  • Principal Component Analysis (PCA) for locating new oil reserves based on geochemical data
  • Controlling the octane number of gasoline during gas blending with real-time prediction from NIR data
  • Raw material identification, classification and chemometric analysis of raw material
  • At-line process control using a NIR spectrometer
  • Design of Experiments to minimize costly trial and error experiments with raw materials
  • Multivariate Calibration for Process Analyzers
  • Systems studied by high-pressure NIR spectroscopy
  • Process understanding

New Opportunities

Searching for new resources or new processes to produce more energy from what is available, so as to meet the rising demand. Fast evaluation of raw material quality and adaptation of the process to available raw material is a competitive advantage that is well served by qualitative modeling and Design of Experiment.


Modeling demand

Understanding the variation in energy demand is also a key tool for optimizing production, it can be achieved using multivariate tools like regression analysis.


Climate change issues

Limiting environmental impact in the production and use of energy is both cost saving and environmental friendly. Running a Principal Component Analysis (PCA) and regression help to get an general overview help in understanding complex processes and determine leverages.


Quality Assessment

Ensuring the extraction of raw material with a specified quality and purity is a key to control the cost of production of oil derived products. On-line analyzers that ensure real-time measurements of this quality using multivariate models is critical in providing consistent high quality products.