Hydraulic Fracturing Pressure Control for Mixed Fleet Efficiency
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Solution Overview
Problem
Hydraulic fracturing operations face challenges in optimizing pumping efficiency due to variations in equipment characteristics within a fleet, leading to suboptimal pumping and fracture generation, which complicates unified control and affects equipment usage.
Innovation Solution
A system utilizing a trained machine learning model to predict treatment pressure and assess control actions for hydraulic fracturing operations, determining if they increase efficiency and viability, and issuing implementation commands to optimize the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If equipment characteristics vary within a fleet, then equipment diversity and adaptability increase, but unified control difficulty and operational complexity increase
Solution Approach 1:
The system changes the parameter of control actions based on equipment characteristics. The machine learning model predicts treatment pressure and assesses control actions by considering specific equipment parameters, allowing each piece of equipment to operate optimally within the fleet without requiring complex unified control protocols.
2Ease of operation
If traditional control methods are used, then operational simplicity is maintained, but pumping efficiency and fracture generation quality deteriorate
Solution Approach 1:
The system enables self-service through automated machine learning models that predict treatment pressure and assess control actions without requiring complex manual intervention. The system serves itself by automatically optimizing pumping parameters based on real-time data, maintaining operational simplicity while significantly improving pumping efficiency and fracture generation quality.
3Object-affected harmful factors
If water and chemical utilization is reduced, then environmental impact and formation damage decrease, but treatment effectiveness may deteriorate
Solution Approach 1:
The system optimizes treatment parameters including water and chemical utilization by using machine learning models to predict treatment pressure and assess control actions. This allows precise control of fluid injection parameters to achieve effective fracture treatment while minimizing excessive water and chemical usage, thereby reducing formation damage without compromising treatment effectiveness.
Data Source
AI summary
A method can include, for a control action for a hydraulic fracturing operation of a well, using a trained machine learning model that predicts treatment pressure of the hydraulic fracturing operation, determining if the control action increases efficiency; if the control action increases efficiency, assessing viability of the control action with respect to one or more predefined criteria; and, if the control action is viable, issuing the control action for implementation during the hydraulic fracturing operation.