Multi-Rig Hydraulic Fracturing Fuel Optimization
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Solution Overview
Problem
Hydraulic fracturing systems face challenges in optimizing fuel consumption and emissions, with existing technologies not effectively considering operational data for efficiency maps, emissions reduction systems, and variable costs, leading to non-compliance with stringent emission standards and increased regulatory costs.
Innovation Solution
A hydraulic fracturing system with multiple rigs, each equipped with fuel consumption and emissions components, controlled by a site controller that receives and processes operational data to determine optimal operational parameters, predicting emissions output and adjusting settings to minimize fuel costs and emissions, using variable frequency drives, motors, and pumps, and incorporating systems like selective catalytic reduction and exhaust gas recirculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple hydraulic fracturing rigs operate at high power to achieve required flow rates, then productivity is improved, but fuel consumption and emissions increase
Solution Approach 1:
The system dynamically adjusts the operating points of multiple hydraulic fracturing rigs based on real-time conditions, power demand, and cost parameters. Controllers continuously optimize the distribution of load across rigs, adjusting power output dynamically rather than operating at fixed high power levels, thereby maintaining productivity while reducing fuel consumption and emissions.
Solution Approach 2:
The system changes operational parameters such as power output, flow rate, and pressure settings of individual rigs based on optimization algorithms that consider fuel costs, emissions costs, and power demand. By varying these parameters optimally across multiple rigs, the system achieves required productivity at lower overall fuel consumption and emissions.
2Productivity
If multiple hydraulic fracturing rigs operate at high power to achieve required flow rates, then productivity is improved, but emissions increase
Solution Approach 1:
The system dynamically optimizes the operating points of multiple rigs to minimize emissions while maintaining required flow rates. Controllers adjust power output and operational parameters in real-time based on emissions predictions and environmental constraints, ensuring productivity is achieved with minimal harmful emissions.
Solution Approach 2:
The system uses feedback from emissions predictions and operational data to continuously adjust rig settings. Controllers receive information about actual and predicted emissions, then modify operational parameters of individual rigs to reduce harmful outputs while maintaining the aggregate flow rate required for productivity.
3Loss of energy
If operational parameters are adjusted to reduce fuel consumption, then energy efficiency is improved, but power delivery may be insufficient
Solution Approach 1:
The system segments the total power demand across multiple hydraulic fracturing rigs, allowing each rig to operate at optimized fuel-efficient points while collectively delivering the required total power. By dividing the load and optimizing each segment's operation, the system achieves both energy efficiency and sufficient power delivery.
Solution Approach 2:
The system combines the output of multiple rigs to achieve required power levels while each individual rig operates at fuel-efficient operating points. The merged collective output maintains sufficient power delivery even though each component operates at lower, more efficient power levels.
4Loss of energy
If traditional control systems are used without optimization algorithms, then device complexity is reduced, but fuel consumption and emissions cannot be optimized
Solution Approach 1:
The control system performs multiple functions including real-time optimization of fuel consumption, emissions reduction, power demand management, and operational coordination of multiple rigs. This multi-functional control system replaces several separate control functions, justifying its complexity through the value of simultaneous optimization across multiple parameters.
Solution Approach 2:
The system implements feedback loops that continuously monitor operational data, fuel consumption, emissions predictions, and power demand, then use this information to adjust rig settings. The feedback mechanism enables automatic optimization without constant manual intervention, making the complex system self-regulating and easier to operate despite its sophistication.
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
The system achieves real-time optimization of fuel consumption and emissions reduction, ensuring compliance with emission standards while maintaining operational performance, by dynamically adjusting operational parameters based on feedback and data analysis, thereby reducing costs and environmental impact.
Implementation Method 1
incorporating systems like selective catalytic reduction and exhaust gas recirculation
Implementation Method 2
incorporating systems like selective catalytic reduction and exhaust gas recirculation
Data Source
AI summary
A method may include receiving power supply-related information, cost-related information, power demand-related information, and operational priority or site configuration-related information associated with hydraulic fracturing rigs. The hydraulic fracturing rigs may be each associated with a fuel consumption component or an emissions component. The method may further include receiving operational data and determining operational parameters based on the operational data and emissions output predictions for the hydraulic fracturing rigs. The method may further include outputting the operational parameters to a computing device or a controller. The method may further include, based on outputting the operational parameters, receiving operational feedback data and determining whether to modify the operational parameters. In addition, based on the outputting, the method may include determining whether to modify the operational data based on determining to not modify the set of operational parameters and modifying the operational data based on determining to modify the operational data.