Intelligent Additive Dosing for Hydraulic Fracturing
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Solution Overview
Problem
Traditional methods for optimizing additive concentrations during hydraulic fracturing operations rely on manual adjustments and lack a robust mathematical model to control hydraulic horsepower (HHP) effectively, due to variations in water quality and total dissolved solids (TDS) between wells.
Innovation Solution
Implementing a real-time calibration and model-building approach that generates a mathematical model to predict surface treating pressure as a function of additive concentration, allowing for optimal additive concentration adjustments to achieve desired performance objectives, such as minimizing operational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment methods are used to optimize additive concentrations, then operational flexibility is maintained, but optimization precision and performance improvement are insufficient
Solution Approach 1:
The system implements real-time feedback by continuously monitoring operational parameters (pressure, temperature, flow rate) and using this data to dynamically adjust additive concentrations. The feedback loop enables the control system to compare actual performance against target values and automatically modify dosing rates to achieve optimal HHP control, thereby improving precision without requiring complex manual intervention
Solution Approach 2:
The patent replaces manual mechanical adjustment mechanisms with an automated electronic control system that uses sensors, processors, and actuators. This substitution eliminates the limitations of human judgment and manual operation while maintaining system flexibility through programmable logic, resolving the contradiction between precision and complexity by automating the control function
2Productivity
If iterative adjustments are made to reach desired additive concentrations, then adaptability to varying conditions is achieved, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The system performs preliminary calculations and predictions of optimal additive concentrations based on pre-stored models and historical data before actual fracturing operations begin. By pre-computing optimal dosing strategies and having the control system ready to execute predetermined adjustment sequences, the system eliminates time-consuming iterative trial-and-error adjustments during critical operations, thereby improving productivity without sacrificing adaptability
Solution Approach 2:
The control system operates autonomously by self-adjusting additive concentrations based on real-time sensor feedback and embedded control algorithms. The system monitors its own performance and automatically modifies dosing rates without requiring external intervention or iterative manual adjustments, enabling continuous optimization while maintaining high operational efficiency and reducing time losses
3Use of energy by moving object
If friction reducers are introduced to lower HHP, then energy consumption is reduced, but control precision and friction management effectiveness worsen due to water quality variations
Solution Approach 1:
The system dynamically changes the concentration parameter of friction reducers in real-time based on monitored water quality parameters (TDS, temperature, pH) and operational conditions. By continuously adjusting the additive concentration to compensate for water quality variations, the system maintains precise friction control and consistent HHP reduction effectiveness, resolving the contradiction between energy savings and control precision
Solution Approach 2:
The control system implements feedback loops that monitor both friction-related parameters (pressure, flow rate) and water quality parameters (TDS, temperature). This multi-parameter feedback enables the system to detect deviations in friction control effectiveness caused by water quality changes and automatically adjust friction reducer dosing to maintain optimal performance, thereby preserving both energy efficiency and control precision
Data Source
AI summary
Some implementations include a method comprising: systematically changing a first value of one or more controllable variables used in a wellbore treatment operation; monitoring a value of a first operation parameter in response to systematically changing the first value of the one or more controllable variables; building at least a first relationship between the one or more controllable variables, the first operation parameter, and one or more cost functions; selecting a second value of the one or more controllable variables based on an optimized cost function; and adjusting the wellbore treatment operation based, at least in part, on the second value of the one or more controllable variables.