Molecular Based Assays for the Practical Correlation and Prediction of Crude Oil and Petroleum Fraction Properties

Molecular Based Assays for the Practical Correlation and Prediction of Crude Oil and Petroleum Fraction Properties

Keywords: crude oil assay, molecular characterization, planning and scheduling, process simulation

Abstract:

For the first time, a comprehensive molecular based methodology has been developed for practical correlation and prediction of assays and properties for crude oils and petroleum fractions. Based on limited and commonly available crude oil assay characterization data such as distillation yield curve, API gravity, and viscosity, the method identifies chemical compositions of pre-determined surrogate model hydrocarbon molecules designed to mimic major chemical constituent components for the purpose of characterizing physical and chemical properties of crude oils. The resulting molecular representation of the crude oil then provides a self-consistent molecular basis to interpolate, extrapolate, and predict all physical and chemical properties of crude oils, petroleum fractions, and blends.

The determination of crude oil assay is a lengthy, tedious and costly process. The conventional approach to perform an assay consists of a limited set of measurements on the crude oil and its fractions. Most often only a few boiling points, densities, and other property measurements are available for selected distilled fractions or the whole crude. Therefore it is necessary for crude oil assay experts to predict or estimate missing properties to meet various business needs, such as refinery planning and scheduling and refinery process simulation. Typically lower order polynomial expressions are used for interpolation and arithmetic probability scale is used for extrapolation of boiling point curves. Statistically derived predictive methods also have been extensively used in the industry for the prediction or estimation of crude oil assays. Analytical methods may predict crude oil properties by correlating the data obtained with rapid surrogate measurements (usually spectroscopic) to those of reference crude assays.

The key elements of this molecular based assay characterization approach include:

·         identifying optimal classes of surrogate model hydrocarbon molecules, practical rules to construct these model molecules from molecular “repeat units” or "segments," and distribution functions of the model molecules required to account for properties of crude oils around the world,

·         applying a thermodynamically-consistent framework to accurately estimate key physical properties of the individual surrogate model molecules and their mixtures, and

·         developing an engineering methodology to robustly identify compositions of the surrogate model molecules from commonly available assay data.  

Implemented in commercial software, molecular based assays have been successfully validated against hundreds of crude assays worldwide including many heavy crude oils. The methodology not only facilitates extraction of molecular insights for the crude oils, it also allows self-consistent interpolation, extrapolation, and prediction of a comprehensive set of physical and chemical properties such as distillation yield curve, API gravity, viscosity, Reid vapor pressure, Watson K, PNA contents, sulfur content, nitrogen content, acid content, asphaltene content, C/H ratio, etc. The methodology offers a superior alternative to conventional and empirical assay characterization technologies. Furthermore, it suggests a highly prized new frontier over the traditional “pseudocomponent,” “hypothetical component,” or “micro-cut” modeling approach for planning, scheduling, and process simulation of oil production and petroleum refining operations.