Downhole Fluid Property Model for Interfacial Tension Prediction
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Solution Overview
Problem
Current methods for estimating interfacial tension (IFT) between oil and water phases in petroleum reservoir fluids are inaccurate due to the lack of consideration for surface-active components, leading to uncertainties in reservoir evaluations and recoveries, with existing models overestimating IFT values by up to 25%.
Innovation Solution
A new physics-based computational model that uses downhole measurements of fluid properties to predict oil-water IFT, relating critical temperature of the oil phase to IFT and incorporating fluid density and viscosity, as expressed in the Sutton-SLB-Aramco model, to provide a more accurate estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current models are used to estimate IFT based solely on hydrocarbon composition, then the estimation process is simple, but the accuracy of IFT prediction deteriorates with deviations up to 25%
Solution Approach 1:
The patent transforms the IFT prediction approach by changing from using only hydrocarbon composition parameters to incorporating downhole-measured fluid properties (density, viscosity, critical temperature) as new parameters. This parameter transformation enables the model to achieve within 6.5% accuracy by using physically measurable quantities that directly reflect the actual reservoir conditions and surface-active component effects.
Solution Approach 2:
The patent replaces the traditional chromatographic analysis-based estimation method with a physics-based computational model that uses downhole fluid measurements. This substitution moves from a composition-based empirical approach to a physics-based approach using density, viscosity, and critical temperature measurements, thereby improving accuracy while maintaining practical simplicity.
2Reliability
If surface-active components are not considered in IFT estimation, then the measurement process remains simple, but the reliability of reservoir evaluations deteriorates
Solution Approach 1:
The patent uses downhole fluid property measurements (density, viscosity, critical temperature) as intermediary parameters that indirectly capture the effects of surface-active components. Instead of directly detecting and measuring trace surface-active components, the model uses these bulk fluid properties as mediators that reflect the net effect of all components including surface-active ones, thereby achieving reliable IFT prediction without direct detection of trace components.
Solution Approach 2:
The patent changes the measurement parameters from direct composition analysis to downhole-measured fluid properties. By measuring density, viscosity, and critical temperature at reservoir conditions, the model indirectly accounts for surface-active components without requiring their direct detection, thus improving reliability while avoiding the difficulty of detecting trace components.
3Productivity
If IFT values are overestimated by current models, then the evaluation process remains straightforward, but the productivity of oil recovery calculations deteriorates due to significant errors
Solution Approach 1:
The patent incorporates feedback from downhole fluid measurements into the IFT prediction model. By using actual reservoir condition measurements (density, viscosity, critical temperature) as input, the model receives feedback about the actual fluid state and adjusts the IFT prediction accordingly, preventing the systematic overestimation that occurs in models without such feedback.
Solution Approach 2:
The patent changes the input parameters from static composition data to dynamic downhole measurements taken at actual reservoir conditions. This parameter change enables the model to adapt to actual reservoir states, improving both the precision of IFT values and the productivity of oil recovery calculations by eliminating systematic overestimation errors.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The model reduces deviations in IFT predictions to within 6.5% compared to experimental values, improving the accuracy of reservoir evaluations and recoveries by accounting for surface-active components.
Implementation Method 1
Knowledge of the interfacial tension (IFT) between the hydrocarbon phase and water phase of petroleum reservoir fluids can play an important role in the evaluation of reservoir potential and its performance
Implementation Method 2
IFT is an important property between fluids controlling distribution and flow of a fluid of oil and water in porous media, as capillary pressure Pc is linearly dependent on it
Data Source
AI summary
Methods and systems are provided for characterizing interfacial tension (IFT) of reservoir fluids, which involves obtaining fluid property data that represents fluid properties of a reservoir fluid sample measured downhole at reservoir conditions, and inputting the fluid property data to a computational model that determines a value of oil-water IFT of the reservoir fluid sample based on the fluid property data. In embodiments, the fluid property data represents single-phase fluid properties of the reservoir fluid sample, such as fluid density and viscosity of an oil phase of the reservoir fluid sample and fluid density of a water phase of the reservoir fluid sample. In embodiments, the computation model can be based on machine learning or analytics combined with a thermodynamics-based physics model.