Downhole Fluid Cleanup Prediction via Simulation Lookup
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Solution Overview
Problem
Current methods for obtaining a representative fluid sample from a subterranean formation require significant time to reduce contamination levels, making it difficult to estimate the time needed for cleanup and the anticipated contamination level accurately.
Innovation Solution
A graphical user interface integrated with a simulation engine and lookup table allows for rapid prediction of cleanup dynamics by inputting parameter values, enabling efficient job planning and monitoring through forward and inversion processes, reducing the need for extensive simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to obtain a representative fluid sample, then the contamination level can be reduced, but the time required for cleanup increases significantly
Solution Approach 1:
The system performs preliminary actions by conducting numerical simulations and generating lookup tables before the actual sampling operation. These pre-computed models predict cleanup dynamics and contamination levels, allowing operators to plan sampling operations more efficiently and reduce actual cleanup time without compromising sample representativeness.
Solution Approach 2:
The patent creates a computational copy of the physical cleanup process through numerical simulations. This virtual model replicates fluid flow and contamination transport behavior, enabling prediction of cleanup dynamics without requiring extensive physical trial-and-error or real-time complex calculations during the actual sampling operation.
2Ease of operation
If operators estimate cleanup time and contamination levels, then job planning can be performed, but the accuracy of estimation requires significant data acquisition time
Solution Approach 1:
The system performs preliminary computations by pre-calculating cleanup dynamics for various scenarios and storing them in lookup tables. During job planning, operators can quickly query these pre-computed models without needing to acquire extensive new data, significantly reducing the time required for accurate estimation while maintaining planning capability.
Solution Approach 2:
The patent introduces numerical simulations and lookup tables as intermediary components between physical sampling operations and job planning decisions. These intermediaries process complex physical relationships in advance, providing operators with accurate estimation tools that require minimal real-time data acquisition during actual planning.
3Measurement precision
If extensive simulations are performed to accurately predict cleanup dynamics, then prediction accuracy improves, but the computational time and complexity increase
Solution Approach 1:
The patent segments the complex simulation process into discrete, pre-computed scenarios stored in lookup tables. Instead of running one large complex simulation, the system divides the parameter space into multiple manageable cases that are pre-solved and stored, allowing accurate predictions through simple table lookup during actual operations.
Solution Approach 2:
The system performs the complex computational work in advance during model development, creating lookup tables that contain pre-computed simulation results. During actual sampling operations, only simple queries to these tables are needed, dramatically reducing real-time computational complexity while maintaining high prediction accuracy.
Data Source
AI summary
The examples described herein relate to methods and apparatus for cleanup prediction and monitoring. A disclosed method of predicting cleanup of a sample fluid obtained by a downhole tool includes drawing the sample fluid into the downhole tool via a probe assembly; measuring optical densities of the sample fluid at a plurality of different respective times; selecting at least some of the measured optical densities as fitting points; identifying one or more inversion parameters; and performing, via a processor, an inversion using the fitting points, the inversion parameters and simulation data to generate data associated with a predicted cleanup of the sample fluid.