Downhole Fluid Contamination Estimation via Iterative Curve Fitting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current oil-based mud (OBM) contamination monitoring methods are inadequate for accurately estimating contamination levels during focused sampling due to early phase cleanup challenges and discrepancies between focused and non-focused sampling tools, leading to uncertainty in optical density estimation.
Innovation Solution
A method involving real-time data collection from downhole sampling tools, using robust moving percentile filtering and iterative nonlinear curve fitting to estimate fluid properties like optical density, minimizing model fit errors and integral nonlinearity, while accounting for noise and non-Gaussian distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If OCM approach is used for focused sampling, then contamination level can be estimated, but accuracy deteriorates during early phases due to high average contamination and slow cleanup at guard inlet
Solution Approach 1:
The sampling process is divided into two separate flowlines: a sample flowline for obtaining formation fluid and a guard flowline for removing contamination. This segmentation allows the sample flowline to provide accurate contamination estimates without being delayed by the slow cleanup process in the guard flowline, resolving the contradiction between measurement accuracy and time loss.
2Measurement precision
If commingled flow behavior is assumed for focused sampling, then contamination can be estimated, but measurement precision deteriorates due to large discrepancies with non-focused sampling tools
Solution Approach 1:
The invention extracts the contamination estimation function from the commingled flow measurement and applies it specifically to the sample flowline of the focused sampling tool. By separating the estimation process from the complex commingled flow behavior, the system achieves accurate optical density estimation while maintaining the simplified focused sampling configuration.
3Measurement precision
If flow measurements with error are used for commingled flow computation, then contamination can be estimated, but uncertainty increases
Solution Approach 1:
The system uses real-time optical density measurements from the sample flowline as feedback to continuously monitor and adjust contamination level estimates. This feedback mechanism allows the system to compensate for errors in flow measurements by directly observing the actual contamination state, thereby reducing estimation uncertainty and improving both precision and reliability.
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
This approach improves the accuracy of contamination level estimation by reducing noise sensitivity and computational complexity, enabling more precise and efficient contamination monitoring in focused sampling scenarios.
Implementation Method 1
it is too early to accurately estimate the optical density (OD) of a formation fluid using the behavior of a commingled flow
Data Source
AI summary
Disclosed are methods and apparatus pertaining to processing in-situ, real-time data associated with fluid obtained by a downhole sampling tool. The processing includes generating a population of values for Ĉ, where each value of Ĉ is an estimated value of a fluid property for native formation fluid within the obtained fluid. The obtained data is iteratively fit to a predetermined model in linear space. The model relates the fluid property to pumpout volume or time. Each iterative fitting utilizes a different one of the values for Ĉ. A value Ĉ* is identified as the one of the values Ĉ that minimizes model fit error in linear space based on the iterative fitting. Selected values Ĉ that are near Ĉ* are then assessed to determine which one has a minimum integral error of nonlinearity in logarithmic space.