Real-time Diverter Diagnostics via Pressure Response Waveforms
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Solution Overview
Problem
Current wellbore stimulation techniques lack real-time monitoring and optimization of diverter particle size distribution, leading to suboptimal diversion and production outcomes.
Innovation Solution
A system utilizing a pressure sensor and processing device to measure diversion pressure response waveforms, determine near-wellbore or far-field bridging, and adjust particle size distribution in real-time using machine-learning models and diverter reservoirs to achieve optimal diversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and optimization of diverter particle size distribution is implemented, then diversion success and well production are improved, but device complexity and operational complexity increase
Solution Approach 1:
The system continuously monitors pressure responses during diverter deployment and uses machine learning models to analyze the data in real-time. The feedback loop compares actual pressure responses with predicted responses to determine if bridging is occurring as intended, and automatically adjusts particle size distribution recommendations to optimize diversion effectiveness.
Solution Approach 2:
The machine learning models are pre-trained with formation characteristics and diverter performance data, enabling the system to autonomously interpret pressure responses and recommend optimal particle size distributions without requiring constant expert intervention. The system serves itself by continuously learning from new data and improving its predictions.
2Manufacturing precision
If real-time pressure measurements and analysis are performed, then diversion optimization is achieved, but measurement and detection difficulty increases
Solution Approach 1:
Machine learning models serve as intermediaries between the raw pressure measurements and the diversion optimization decisions. These models translate complex, noisy pressure response data into clear indicators of bridging status and particle size effectiveness, making the measurement and analysis process more manageable and interpretable.
Solution Approach 2:
The system creates virtual copies of pressure response scenarios through machine learning simulations, allowing operators to analyze predicted pressure responses for different particle size distributions without having to physically test each scenario. This copying approach simplifies the evaluation of multiple diversion strategies.
3Reliability
If particle size distribution is adjusted in real-time based on pressure responses, then diversion effectiveness is improved, but loss of time in decision-making occurs
Solution Approach 1:
Machine learning models are pre-trained with extensive formation data and diverter performance characteristics before deployment. This preliminary action prepares the system to quickly process real-time pressure responses and generate particle size recommendations without requiring time-consuming analysis during the actual diversion operation.
Solution Approach 2:
The system replaces traditional mechanical trial-and-error approaches to diverter optimization with automated computational analysis. Machine learning algorithms rapidly process pressure data and generate particle size distribution recommendations, eliminating the time loss associated with manual analysis and iterative testing.
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
Enables real-time decision-making for optimized diverter deployment, improving diversion success and well production by correlating pressure responses with fracture width and flow distribution.
Implementation Method 1
measuring an onset of diversion pressure response (DPR) waveform using the pressure sensor during or after pumping diverter into a wellbore
Data Source
AI summary
Certain aspects and features relate to a system that determines a result or outcome of a diverter drop in terms of whether far field or near-wellbore bridging, plugging, or diversion has been achieved. The system can provide for real-time measurements and real-time decision making in terms of an optimized diverter to be used in a wellbore. A system for diverter diagnostics can includes a pressure sensor and a processing device communicatively coupled to the pressure sensor. A non-transitory memory device includes instructions that are executable by the processing device to measure an onset of diversion pressure response (DPR) waveform and output or store indications of a result or results of a diverter drop. These results can then be used to adjust a particle size distribution for the diverter.