Automated Downhole Formation Testing with Real-Time Parameter Adjustment
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Solution Overview
Problem
Current downhole formation testing for oil and gas wells requires highly trained operators to manually set complex parameters, leading to sub-optimal results due to human error and operational limitations, despite the importance of accurate data for hydrocarbon reserve evaluation and production optimization.
Innovation Solution
An automated and optimized formation testing method that adjusts control parameters based on real-time data from initial tests, using a formation tester equipped with a telemetry network and adjustable probe systems to refine testing procedures for subsequent tests, thereby optimizing the testing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual operation by highly trained operators is used, then testing can be performed with current equipment, but human error and operational limitations lead to sub-optimal results
Solution Approach 1:
The system performs self-optimization by automatically adjusting control parameters based on real-time test data without requiring continuous human intervention. The automated controller analyzes test results and autonomously modifies testing parameters to improve measurement precision while eliminating human error.
Solution Approach 2:
The system implements a feedback loop where test data from initial formation tests is continuously monitored and used to adjust control parameters for subsequent tests. This closed-loop control ensures optimal testing accuracy by adapting to actual formation conditions rather than relying on fixed manual settings.
2Measurement precision
If multiple formation tests are performed to optimize parameters, then testing accuracy improves, but testing time and operational complexity increase
Solution Approach 1:
The system performs an initial formation test to gather preliminary data about formation characteristics before optimizing subsequent testing parameters. This preliminary action provides the foundation for automated parameter adjustment, allowing the system to learn formation properties and adapt control settings without requiring extensive manual trial-and-error testing.
Solution Approach 2:
The system dynamically adjusts control parameters between tests based on real-time analysis of formation response. Rather than using static pre-determined parameters, the system continuously adapts testing conditions to optimize the balance between measurement precision and testing duration, performing only the necessary number of tests to achieve reliable results.
3Adaptability or versatility
If complex control parameters are manually adjusted, then testing can be customized to formation conditions, but operator training requirements and potential for error increase
Solution Approach 1:
The system replaces manual mechanical adjustment of control parameters with an automated electronic control system. The automated controller receives test data, processes formation characteristics, and electronically adjusts parameters without requiring physical manual intervention. This substitution maintains full parameter customization capability while eliminating the need for highly trained operators and reducing human error.
Data Source
AI summary
A method, and corresponding system, for formation testing includes establishing a control parameter and a test criterion for testing a formation, providing a formation tester equipped with the control parameter and the test criterion in a well bore at a test location, performing a first formation test, controlling the first formation test using the control parameter and the test criterion, collecting test data from the first formation test, and adjusting the control parameter using the test data. The method may include performing a second formation test, and controlling the second formation test using the adjusted control parameter and the test criterion. The method may include collecting additional test data from the second formation test, and re-adjusting the control parameter using the additional test data.