Program Agenda:
1100–1130 Registration and Networking
1130–1300 Lunch
1145–1200 Technical Section Updates
1200–1300 Presentation and Q&A
Advances in Predicting Crude Oil Behavior: A Faster, Cheaper and More Reliable Approach
Francisco "Paco" Vargas, Ph.D.
Department of Chemical and Biomolecular Engineering
Rice University, Houston, Texas USA
Abstract
The current downturn of the oil industry urgently calls for new strategies for cost reduction, optimized production, and prompt identification and mitigation of potential problems. Furthermore, the accurate determination of thermo-physical properties and the phase behavior of reservoir fluids is of great commercial importance, which is typically achieved by extensive and costly experimental analysis. Studying more complex problems, such as asphaltene precipitation and deposition poses an even higher level of complexity and requires additional investment, which in many cases can be quite significant.
Modeling methods based on the Perturbed Chain version of the Statistical Associating Fluid Theory Equation of State (PC-SAFT EOS) have been successfully applied to a wide range of cases in the oil and gas industry, such as the prediction of PVT properties and the precipitation of asphaltenes during oil production. Furthermore, a number of technical manuscripts available in the open literature describe the effect of oil based mud contamination, hydrocarbon and carbon dioxide gas injection, comingling of oils and even the compositional grading in the reservoir that can produce tar-mat formation. Nevertheless, the complexity of this modeling approach and the non-trivial process employed for the determination of simulation parameters require a significant amount of time and effort from highly qualified personnel.
In this talk, a series of case studies will be presented, in which the PC-SAFT EOS was successfully applied to reduce the number of required experiments, and for validation of the data obtained. Furthermore a new method that reduces the number of required simulation parameters for certain reservoir fluids to only one will be presented and discussed. Ten different crude oils were tested with this new and much simpler technique and the results of various PVT experiments, such as Constant Composition Expansion, Differential Liberation, Separator Test, and Swell Test experiments are in remarkable agreement with the modeling predictions. The overall average absolute percent deviation (AAPD) from experimental data studied is 3.54% for a total of 924 data points. Also, an important advantage of the new approach over the original method is that SARA analysis is no longer required as an input parameter.
With this work we aim to contribute to the development of more user-friendly (but yet powerful tools) for an accurate prediction of crude oil properties to provide cost/effective solutions when is most needed.