Automated Drilling Flow Rate Trajectory Optimization
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Solution Overview
Problem
Traditional drilling techniques face challenges in efficiently controlling flow rates during drilling, leading to transient dynamics that can breach safety requirements, impact wellbore integrity, and slow down drilling processes.
Innovation Solution
The implementation of transient dynamical models for non-linear trajectory optimization in automated drilling operations, which addresses wellbore and surface safety requirements while minimizing waiting time and optimizing flow rate control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual pump start-up procedures are used, then field engineers can operate the drilling equipment, but the transient dynamics breach safety requirements and cause sudden pressure peaks
Solution Approach 1:
The optimization framework calculates and determines the optimal pump ramp-up trajectory before actual pump start-up occurs. By pre-computing the flow rate trajectory that minimizes pressure peaks while satisfying safety constraints, the system prepares the optimal control path in advance, preventing harmful transient dynamics before they occur.
Solution Approach 2:
The system transitions from static manual operation to dynamic automated control by continuously adjusting the pump flow rate according to the optimized trajectory. The pump operational parameters are dynamically modified based on the calculated optimal path, allowing the system to adapt to transient rheological dynamics in real-time and maintain safety requirements.
2Reliability
If traditional cautious multi-step ramp-up procedures are used, then wellbore pressure is maintained within limits, but the drilling process is slowed down and waiting time increases
Solution Approach 1:
The optimization framework changes the operational parameters by determining the optimal pump ramp-up trajectory that allows faster flow rate increases. By computing the precise trajectory that maintains wellbore pressure within limits while minimizing ramp-up time, the system enables both safety and efficiency improvements simultaneously.
Solution Approach 2:
The system incorporates drilling fluid rheological properties and wellbore conditions into the optimization framework, which calculates the optimal trajectory based on these parameters. This feedback mechanism allows the system to adapt the ramp-up procedure to specific well conditions, achieving both safety and time optimization.
3Measurement precision
If detailed rheological analyses are used, then drilling fluid characteristics are understood, but the analysis is challenging to apply in field environments and not always available
Solution Approach 1:
The system extracts only the essential rheological parameters needed for optimization from the complex drilling fluid characteristics. By identifying and using only the critical parameters in the optimization framework, the system simplifies the application process while maintaining accuracy, making it feasible for field environments.
Solution Approach 2:
The optimization framework is designed to be self-sufficient by incorporating built-in models of drilling fluid rheology and transient dynamics. This allows the system to perform calculations autonomously using available field data without requiring external detailed rheological analyses, enabling easy deployment in field environments.
4Productivity
If automated optimization framework is implemented, then flow rate trajectories are optimized and safety is improved, but the system complexity increases
Solution Approach 1:
The optimization framework serves multiple functions: it calculates optimal pump trajectories, predicts pressure peaks, ensures safety compliance, and minimizes waiting time. By consolidating these functions into a single integrated system, the complexity is managed efficiently while achieving multiple objectives simultaneously.
Solution Approach 2:
The system replaces manual mechanical operation with automated computational optimization. By substituting the manual multi-step decision-making process with an automated optimization framework that uses mathematical models and algorithms, the system achieves higher productivity while the complexity is managed through software rather than mechanical means.
Data Source
AI summary
Some implementations include a method for minimizing a ramp-up time of drilling equipment used to drill a wellbore through a subsurface formation, the method comprising: determining, via an optimization framework, an optimized ramp-up procedure for the drilling equipment with respect to one or more transient dynamics of a drilling fluid; and performing the optimized ramp-up procedure via the drilling equipment.