Wireline Stage Control Using ML for Speed-Tension Balance
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Solution Overview
Problem
Optimizing wireline speed during multi-stage wireline operations in oil and gas wells is challenging due to constraints on tension, requiring a balance of speed and tension to prevent wireline breakage and extend its lifespan.
Innovation Solution
The use of trained machine learning models to predict optimized wireline parameters, including speed and tension ranges, for each stage of the operation, based on structural and operational data from historical wells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wireline speed is increased to reduce operation time, then productivity improves, but tension increases causing wireline breakage risk
Solution Approach 1:
The system dynamically adjusts wireline speed based on real-time operational conditions and stage-specific parameters. The machine learning models generate stage-wise speed recommendations that adapt to changing well conditions, allowing the wireline to operate at optimal speeds that balance productivity with tension constraints throughout the multi-stage operation.
Solution Approach 2:
The invention changes the operational parameters (speed and tension) based on the stage of operation and well characteristics. The machine learning models predict optimal parameter ranges for each stage, enabling the system to transition from static speed limits to dynamic parameter optimization that reduces overall operation time while maintaining safe tension levels.
2Duration of action of stationary object
If wireline speed is decreased to reduce tension, then wireline lifespan is extended, but operation time increases
Solution Approach 1:
The machine learning models are trained on historical wireline operation data to predict optimal speed and tension parameters before each stage of operation. This preliminary analysis allows the system to plan the most efficient wireline trajectory and speed profile in advance, maximizing wireline lifespan while minimizing operation time through pre-computed optimal paths.
Solution Approach 2:
The system incorporates feedback from actual wireline performance data to continuously refine speed and tension recommendations. By monitoring real-time tension measurements and comparing them against model predictions, the system learns from actual wireline behavior to optimize subsequent operations, extending wireline lifespan through data-driven parameter adjustment.
3Productivity
If stage-specific optimization is implemented using machine learning models, then productivity improves, but system complexity increases
Solution Approach 1:
The invention segments the wireline operation into distinct stages (e.g., run-in-hole, perforation, pull-out-of-hole) and applies specialized machine learning models to each stage. This segmentation allows the complex overall operation to be managed through simpler, stage-specific optimizations, where each model focuses on the unique characteristics and constraints of its particular phase.
Solution Approach 2:
The machine learning models automatically generate stage-specific speed and tension recommendations without requiring complex manual intervention. The system self-services by using historical data and real-time measurements to autonomously determine optimal parameters, reducing the need for complex human decision-making processes while maintaining high productivity.
Data Source
AI summary
Embodiments herein generally relate to a method and system for controlling a multi-stage wireline operation in a well, such as during a plug-and-perf operation. The method can comprise: applying a trained initial stage machine learning model to generate predictions for optimized wireline parameters, for a first stage of the wireline operation; operating a wireline control system, to complete the first stage, in accordance with the optimized wireline parameters; applying a trained later stages machine learning model to generate predictions for optimized wireline parameters for each subsequent stage of the wireline operation; and operating the wireline control system, to complete each subsequent stage, in accordance with the optimized wireline parameters generated for that stage by the trained later stages model.