Atlantic Margin Oil Properties Prediction (AMOPP), Predicting Oil Quality from Geochemical Data
In its continuing effort to develop technological solutions for oil and gas E&P, GEOCHEMICAL SOLUTIONS INTERNATIONAL, INC. (GSI) is offering a predictive model for crude oil quality based on geochemical data. The Atlantic Margin Oil Properties Prediction (AMOPP) model will be specific for oils from the South Atlantic marginal basins (west Africa and Brazil), and will be developed in region-specific versions, with the initial version specific to offshore Brazil oils. The model will be based on a multivariate regression approach using data from GSI’s Brazil Oil Study for calibration, and will predict oil quality indicators such as API gravity, pour point and viscosity. It will be applicable to well samples such as conventional and sidewall cores, drill cuttings, outcrops, and near-surface seeps, where the small quantities of oil preclude direct measurement.
Oil quality is a central issue in the economic assessment of liquid hydrocarbon accumulations. Poor oil quality typically diminishes resource value, and can make some prospects uneconomic. The primary sub-surface processes governing oil quality include source rock type and thermal evolution of the source rocks (pre-migration), as well as biodegradation, water-washing and phase separation (post-migration). The combined effects of these processes are often reflected in the chemical composition of crude oils.
GSI’s recently completed Brazil Oil Study analyzed more than 175 crude oils representing the major offshore basins and stratigraphic horizons. Oils were characterized for physical properties (API gravity, weight percent sulfur, heavy metal content, viscosity and pour point, gross composition) in addition to the use of state-of-the-art oil fingerprinting techniques such as gas chromatography, stable carbon isotopes and analysis of biomarker distributions. The oils were grouped into compositionally-distinct oil families using multivariate statistical methods, primarily Principal Components Analysis and Hierarchical Clustering. In addition, the oils were characterized according to their thermal maturity and degree of alteration.
These data have been used as the training set for the Brazil AMOPP model. The results have been analyzed by Principal Component Regression (PCR) and Partial Least Squares (PLS) to arrive at a predictive algorithm between a quantitative sample property, (in this case, oil quality measures API gravity, pour point, viscosity and sulfur content), and several independent variables (geo- chemical properties). An example of predicted versus measured API gravity for a subset of the calibration oils from the Campos and Espirito Santo basins is shown in the figure.
Input data for AMOPP includes C15+ bulk composition. alkane and isoprenoid distributions, triterpane and sterane biomarkers, and fluorescence characteristics. These properties can be easily measured on residual samples such as conventional and sidewall cores, drill cuttings, outcrops, and near-surface seeps, where small sample quantities prevent direct measurement of oil quality measures.
AMOPP can be run from a Microsoft Excel workbook, in which input data is entered in a worksheet and predicted properties are automatically calculated, including generation of figures (a browser-based HTML interface is also projected). Full printed and on-line documentation describing the development and calibration of the model will be provided.
This Project is complete and available for immediate delivery.
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