Dynamic Frac Pump Control via Real-Time Sensor Feedback
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Solution Overview
Problem
Current well treatment plans in the oil and gas industry are often manually adjusted based on static system models, which may not accurately reflect the downhole environment, leading to suboptimal control of electric frac pumps and other equipment.
Innovation Solution
The implementation of a system that automatically generates a sequence of stimuli using sensor data to select or refine a representative system model, allowing for dynamic adjustments of parameters like pressure, flow rates, and chemical compositions to optimize well treatments based on real-time formation responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual adjustment based on static system models is used, then device complexity is reduced, but manufacturing precision and reliability deteriorate due to inaccurate representation of downhole environment
Solution Approach 1:
The patent transforms the static system model into a dynamic adaptive model that evolves with real-time sensor data. The model transitions from a fixed manual selection to a continuously updating representation that adapts to changing downhole conditions, resolving the contradiction between model simplicity and accuracy.
Solution Approach 2:
The patent implements feedback loops where sensor data from the downhole environment continuously informs and refines the system model. This feedback mechanism allows the model to self-correct and improve accuracy over time without requiring complex manual adjustments, maintaining simplicity while enhancing precision.
2Ease of operation
If manual selection of representative system model is used, then ease of operation is improved, but adaptability deteriorates because the model may not accurately reflect the specific well conditions
Solution Approach 1:
The patent enables the system model to select and refine itself automatically based on sensor data without requiring manual intervention. The model performs self-adjustment by comparing predicted responses with actual sensor measurements and iteratively improving its parameters, thereby maintaining ease of operation while dramatically improving adaptability to specific well conditions.
Solution Approach 2:
The patent replaces the manual mechanical process of model selection with an automated computational system. Instead of technicians manually selecting models based on observed data, the system uses algorithms to automatically match and refine models based on real-time sensor inputs, eliminating manual effort while enhancing precision.
3Device complexity
If static representative system model is used, then device complexity is reduced, but productivity deteriorates due to suboptimal control of frac pumps and equipment
Solution Approach 1:
The patent implements dynamic control parameters that adjust in real-time based on sensor feedback, allowing the system to optimize fracturing operations continuously rather than relying on fixed static parameters. This dynamic approach improves productivity by adapting to changing well conditions without requiring complex manual reconfiguration.
Solution Approach 2:
The patent establishes real-time feedback loops where sensor data from the fracturing operation continuously informs control parameter adjustments. This feedback enables automatic optimization of pump rates, pressures, and other operational parameters, improving productivity while maintaining manageable system complexity through automated decision-making.
Data Source
AI summary
A sequence of stimuli produced by an electric frac pump can be generated by a treatment optimization system. Well environment responses to the sequence of stimuli may be measured by sensors and respective sensor data may be received. The sensor data may be used to select a representative system model which can then be used to control the electric frac pump. The representative system model may be used to achieve well stage objectives such as particular cluster efficiencies, complexity factors, or proximity indices.