Proxy Model for Downhole Fluid Sampling Contamination Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for miscible contamination cleanup during downhole fluid sampling are computationally demanding and impractical for quick evaluation of multiple scenarios due to high-resolution 3D models, which are necessary for predicting contamination levels and optimizing sampling operations, especially in offshore environments with high rig costs and uncertainty in formation and fluid properties.
Innovation Solution
A proxy model is generated using a true numerical model to approximate the relationship between pumping parameters and contamination levels, allowing for fast and accurate evaluation of contamination levels and optimization of sampling operations, utilizing kriging-based interpolation to reduce the parameter space and facilitate real-time contamination monitoring and closed-loop control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution 3D models are used for contamination prediction, then prediction accuracy is improved, but computational demand and time consumption increase
Solution Approach 1:
The patent creates a proxy model that copies the essential behavior of the complex 3D numerical model but with significantly reduced computational requirements. The proxy model uses kriging-based interpolation to replicate contamination predictions without requiring full 3D numerical simulations, thus achieving comparable accuracy with much faster computation times suitable for real-time decision making.
Solution Approach 2:
The patent transforms the complex multi-parameter 3D model into a simplified parameterization scheme. By identifying and focusing on key parameters that dominate contamination behavior (such as pump rate, formation permeability, and filtrate properties), the model reduces computational dimensions while maintaining predictive accuracy for cleanup time and contamination levels.
2Manufacturing precision
If multiple sampling scenarios are evaluated using true numerical models, then optimization accuracy is improved, but productivity decreases due to computational demands
Solution Approach 1:
The proxy model serves as a computational copy that enables rapid evaluation of multiple sampling scenarios. Instead of running expensive 3D numerical simulations for each scenario, the patent uses the pre-calibrated proxy model to quickly predict contamination outcomes, allowing operators to evaluate numerous scenarios and identify optimal sampling procedures efficiently.
Solution Approach 2:
The patent performs preliminary calibration of the proxy model using a limited set of 3D numerical model results before actual scenario evaluation. This preliminary action creates a ready-to-use predictive tool that can quickly assess multiple scenarios without repeating full numerical simulations, thus improving productivity while maintaining optimization accuracy.
3Reliability
If detailed parameter analysis is performed to reduce uncertainty, then reliability is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent performs sensitivity analysis to identify which parameters have the greatest impact on contamination predictions. By focusing computational resources on analyzing only the most influential parameters (such as formation permeability, pump rate, and filtrate viscosity), the model reduces uncertainty in predictions without requiring complex analysis of all possible parameters, thus improving reliability while controlling model complexity.
Data Source
AI summary
Methods and systems for generating and utilizing a proxy model that generates a pumping parameter as a function of contamination. The pumping parameter is descriptive of a pumpout time or volume of fluid to be obtained from a formation by a downhole sampling tool positioned in a wellbore extending into the formation. The contamination is a percentage of the fluid obtained by the downhole sampling tool that is not native to the formation. The proxy model is based on a true model that utilizes true model input parameters that include the pumping parameter, formation parameters descriptive of the formation, and a filtrate parameter descriptive of a drilling fluid utilized to form the wellbore. The output of the true model is the contamination as a function of the pumping parameter. The proxy model utilizes proxy model input parameters each related to one or more of the true model input parameters.