Statistical Tools for Managing and Manipulating Crude Oil Data
Figure 6: A Predictive Condensate Model Where the Relationship Between the API and Sulphur Content of Various Condensates Is Shown
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0
-100 -200
40 50 60 70 Condensate ºAPI
The figure shows an example of a predictive condensate model, where the relationship between the API and sulphur content of various condensates is shown. A naive approach would predict a negative sulphur content for a crude with an API of 80. Although this is a simple example, it highlights the importance of engineering knowledge to supplement the predictions based on statistical models.
The Need for Expert Knowledge
A key final consideration when implementing statistical models is expert knowledge. Mathematical approaches to understanding crude oil data sets will inevitably be limited, unless they are guided by chemical and physical constraints, as well as refining experience or industry standards. As an example, it would make little sense to use predictive mathematical techniques to relate specific gravity and API gravity data: that relationship is already defined by industry convention.
Similarly, there are several other facts from established research that should be incorporated into any tools that manipulate assay data. For
80 90 100
example, the variation of density or viscosity with temperature is defined in API and ASTM publications and is generally adopted throughout the industry. Furthermore, many property behaviours have physical constraints (e.g. sulphur content should lie above zero) or expected relationships due to the test methods used (e.g. pour point should generally be less than cloud point).
Conclusion
There are many considerations when building powerful, user-friendly software for harnessing the knowledge present in crude oil databases. One of the challenges faced is how to develop a suitable way to represent crude oil yield and quality data, taking into account all available whole-crude, cut and residue data. When characterising assays, predictive modelling is important to achieve coverage across the boiling range and across all properties required in downstream tools. Although industry-standard correlations can be adopted, this can be a time-consuming process, requiring considerable expertise and potentially resulting in very different results from expert to expert. Statistically derived predictive models can be used to complete this process more efficiently, with greater reliability and greater reproducibility. Further incorporating categorical data, such as crude origin, can improve the modelling quality yet further by identifying local regions of stronger crude quality relationships.
Spiral Software has extensive experience of developing software tools in this area to allow users to straightforwardly apply powerful statistical models when working up assay data. Working with large databases of crude oil data, Spiral has developed powerful models to describe the relationships between crude oil properties across all boiling ranges. In addition, relevant physical and chemical constraints are built in at a core level, further improving the accuracy of these models. n
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EXPLORATION & PRODUCTION – VOLUME 8 ISSUE 1
Sulphur (ppm)
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