Cloud Dashboard for Hydraulic Fracturing Data Management
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Solution Overview
Problem
Current hydraulic fracturing operations lack real-time data management and decision-making capabilities, leading to inefficiencies and increased completion costs due to suboptimal equipment performance and operational deviations.
Innovation Solution
A cloud-based dashboard supported by cloud architecture that processes and visualizes real-time data from hydraulic fracturing operations, enabling real-time adjustments and predictive analytics to optimize equipment performance and operational parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time data processing and cloud-based management systems are implemented, then operational efficiency and productivity are improved, but device complexity and initial costs increase
Solution Approach 1:
A cloud-based platform serves as an intermediary between hydraulic fracturing equipment and operators, receiving data from multiple equipment sources, processing it through predictive analytics models, and delivering actionable insights. This mediator handles the complexity of data integration and analysis, allowing operators to benefit from advanced analytics without directly managing the underlying system complexity.
Solution Approach 2:
The patent replaces traditional mechanical data collection and analysis methods with cloud-based digital systems. Instead of on-site data processing equipment, the system uses remote cloud infrastructure to receive, process, and analyze operational data, substituting physical data processing machinery with virtual computing resources that reduce on-site device complexity.
2Reliability
If real-time monitoring and predictive analytics are implemented, then equipment reliability is improved, but loss of time for data processing and system setup increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing operational data in real-time, maintaining ready-to-analyze data buffers that can be immediately processed when anomalies occur. Predictive models are pre-trained and ready to execute, eliminating the need for time-consuming data preparation and model training when equipment issues arise.
Solution Approach 2:
The system implements continuous feedback loops where operational data is constantly monitored, analyzed, and used to adjust predictions and alerts in real-time. This ongoing feedback mechanism ensures that the system adapts to changing conditions without requiring manual intervention or time-consuming re-calibration, maintaining high reliability while minimizing processing delays.
Data Source
AI summary
The method includes receiving raw data at a cloud service relating to a hydraulic fracturing operation. The raw data can be streamed to the cloud service. The method further includes pre-processing the raw data to generate pre-processed data. The pre-processed data can be ingestible by a cloud-based dashboard. Additionally, the method includes identifying at least one parameter relating to the hydraulic fracturing operation using the pre-processed data. The method can further include determining a difference between the at least one parameter and at least one optimized parameter. Further, the method can include adjusting the hydraulic fracturing operation based the difference between the at least one parameter and the at least one optimized parameter.