Casing Wear Prediction Using Physics and Data Models
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Solution Overview
Problem
The existing methods for predicting casing wear in hydrocarbon wells are either economically unfeasible, such as frequent deployment of casing wear logging tools, or inaccurate, like physics-driven models that become overly complex, leading to costly well repairs and potential abandonment due to casing failures.
Innovation Solution
A system combining a physics-driven model and a data-driven model to predict casing wear by integrating differential equations from Newton's laws and using sensor-based data to train a regression-based data-driven model, which estimates wear based on frictional forces, drilling parameters, and wellbore parameters, allowing for real-time adjustments in drilling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent deployment of casing wear logging tools is performed, then casing wear measurement accuracy is improved, but drilling cost increases and drilling speed decreases
Solution Approach 1:
The patent replaces the mechanical wireline logging tools with a physics-driven computational model that uses differential equations to simulate casing wear. This substitution eliminates the need for frequent physical logging operations while providing continuous wear predictions throughout the drilling process, thereby maintaining measurement accuracy without sacrificing drilling speed or increasing costs.
Solution Approach 2:
The patent introduces a physics-driven model as an intermediary between the drilling process and wear measurement. This model acts as a virtual sensor that continuously predicts casing wear based on input parameters such as drill string characteristics, drilling parameters, and wellbore conditions, eliminating the need for direct physical measurement operations.
2Strength
If excessively thick casing segments or high-grade materials are used, then casing strength and integrity are improved, but material cost increases
Solution Approach 1:
The patent performs preliminary wear prediction using a physics-driven model before actual wear occurs. By continuously simulating casing wear throughout the drilling process and identifying potential failure points in advance, the system enables proactive adjustments to drilling parameters or casing design, avoiding the need for overly conservative thick casing segments or high-grade materials while preventing casing failures.
Solution Approach 2:
The patent changes the approach from static material selection to dynamic parameter optimization. By using the physics-driven model to predict wear under different drilling conditions and adjusting drilling parameters (such as rotation speed, weight on bit, or mud pressure) in real-time, the system optimizes casing performance without requiring excessive material thickness or high-grade materials, thereby reducing material costs while maintaining integrity.
3Quantity of substance
If physics-driven models are used to estimate casing wear, then material cost is reduced, but model accuracy deteriorates due to complexity and recalibration requirements
Solution Approach 1:
The patent implements feedback mechanisms where the physics-driven model predictions are continuously refined using actual wear measurements from logging tools when deployed. The model uses feedback from these measurements to adjust and recalibrate the differential equations, improving accuracy over time. This feedback loop allows the system to maintain high prediction accuracy while avoiding the need for overly complex models, as the physics-based framework provides a solid foundation that requires only minor adjustments based on actual data.
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 provides accurate and cost-effective prediction of casing wear, reducing the likelihood of failure by enabling informed adjustments in drilling parameters and well planning, thereby minimizing economic losses and extending well lifespan.
Implementation Method 1
The physics-driven model employs well-understood physics principles, such as frictional forces, force balancing, energy conservation, and erosion rates to formulate a casing wear prediction
Implementation Method 2
Example casing wear logging tools employ acoustic, electromagnetic (EM), or multi-finger caliper technology
Data Source
AI summary
A casing wear estimation method includes obtaining a set of input parameters associated with extending a partially-cased borehole and applying the set of input parameters to a physics-driven model to obtain an estimated casing wear log. The method also includes employing a data-driven model to produce a predicted casing wear log based at least in part on the estimated casing wear log. The method also includes storing or displaying information based on the predicted casing wear log.