APR EOS Modeling for Asphaltene Precipitation Prediction
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Solution Overview
Problem
Existing computational models for predicting asphaltene precipitation in petroleum fluids, such as the Cubic-Plus-Association equation of state (EOS) and the Perturbed Chain form of the Statistical Associating Fluid Theory (PC-SAFT, struggle to accurately represent the phase behavior changes due to varying chemical nature of crude oils, necessitating individual tuning for each fluid type.
Innovation Solution
Adaptation of the Advanced Peng Robinson (APR) equation of state (EOS) for petroleum fluid modeling, incorporating binary interaction parameters based on pseudo-components of varying carbon number and molecular structure, with correlations that account for molecular interactions and solubility effects, and a tuning process to match asphaltene onset pressure with experimental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Cubic-Plus-Association equation of state (EOS) or PC-SAFT is used to predict asphaltene precipitation, then prediction accuracy is improved, but model complexity increases and individual tuning is required for each fluid type
Solution Approach 1:
The patent modifies the APR EOS by changing the functional form of the attractive term from the traditional cubic form to a form that incorporates association effects through adjustable parameters (kij, lij, mij). This parameter change allows the model to capture complex asphaltene precipitation behavior while maintaining the simpler APR EOS framework, thus improving prediction accuracy without proportionally increasing model complexity
Solution Approach 2:
The patent develops a universal correlation framework for binary interaction parameters that can be applied across different crude oil compositions. By establishing general correlations based on molecular weight and composition parameters, the model achieves multi-functionality in predicting asphaltene precipitation for various fluid types without requiring individual tuning for each case, thereby reducing the practical complexity despite the enhanced theoretical framework
2Reliability
If existing computational models are used for different crude oil types, then prediction capability is maintained, but individual tuning is required for each fluid type reducing adaptability
Solution Approach 1:
The patent introduces composition-dependent binary interaction parameters that vary with molecular weight and fluid composition. This allows the same APR EOS model to adapt to different crude oil types by automatically adjusting parameters based on the specific fluid's molecular characteristics, eliminating the need for individual tuning while maintaining prediction capability across diverse fluid types
Solution Approach 2:
The model employs self-adjusting binary interaction parameters that are calculated automatically based on the input fluid composition and molecular weight distribution. The system serves itself by internally determining the appropriate parameters for each specific crude oil type without requiring external calibration or tuning, thereby improving both reliability and adaptability simultaneously
Data Source
AI summary
Fluid modeling methods and systems are provided for predicting phase behavior of petroleum fluids using a computational model that employs pseudo-components of varying carbon numbers that represent asphaltenes, asphaltene-like hydrocarbon, and non-asphaltene hydrocarbons. The computational model models the phase equilibria of the mixture of components with mixing rules that employ binary interaction parameters determined from correlations that involve certain properties or parameters associated with respective pseudo-component pairs as input variables. The binary interaction parameters are tuned by adjusting at least one parameter of the respective correlations based on matching at least one measured property of one or more petroleum fluids to a corresponding property predicted by the computational model for the one or more petroleum fluids. The tuned values for the binary interaction parameters can be stored as part of the computational model for subsequent fluid modeling. In embodiments, the computational model is based on the Advanced Peng Robinson equation of state.