Real-Time Fracturing Modeling for Multi-Mode Fracture Prediction
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Solution Overview
Problem
Current hydraulic fracturing models are inadequate for complex fracture systems, particularly in multi-mode fracture environments, leading to inefficiencies and suboptimal decision-making due to reliance on historical data and lack of real-time predictive capabilities.
Innovation Solution
A data-driven approach using machine learning and pattern recognition to model hydraulic fracturing treatments in segments, incorporating LFDAS and external pressure gauges to predict and adjust operations in real-time, optimizing energy delivery and reducing uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If historical data and conventional models are used for fracturing treatment decision-making, then existing processes can be maintained with simple operations, but real-time predictive capabilities are lacking and decision-making remains suboptimal
Solution Approach 1:
The fracturing treatment is divided into multiple segments along the wellbore, with each segment modeled independently using machine learning algorithms. This segmentation allows real-time predictive capabilities to be applied to specific zones while maintaining overall system manageability. The system processes data from multiple segments simultaneously but treats each segment as a discrete unit for modeling and decision-making.
Solution Approach 2:
The system incorporates real-time feedback from monitoring wells and sensors to continuously update the machine learning models during the fracturing treatment. This feedback loop enables the system to adapt to changing formation conditions and improve prediction accuracy dynamically, transforming static historical models into dynamic predictive systems that respond to real-time data.
2Reliability
If complex fracture systems are modeled using traditional methods, then existing simplicity is maintained, but the models become inadequate for multi-mode fracture environments
Solution Approach 1:
The machine learning models dynamically adjust parameters such as fracture mode, propagation direction, and stress distribution based on real-time data from monitoring wells and sensors. The system transitions from static, single-mode fracture modeling to dynamic, multi-mode fracture simulation that adapts to complex formation conditions, enabling accurate prediction of various fracture types including mode I, II, and III fractures.
Solution Approach 2:
The system replaces static historical models with dynamic machine learning models that continuously evolve based on real-time data. The models adapt to changing formation conditions, stress states, and fracture propagation patterns during the treatment, providing versatile prediction capabilities for complex multi-mode fracture systems rather than relying on fixed conventional models.
3Productivity
If real-time modeling is implemented to improve fracturing efficiency, then operational costs and production performance can be optimized, but data processing and computational requirements increase
Solution Approach 1:
The system applies machine learning modeling to critical segments and key decision points rather than continuously modeling every possible parameter and location. This partial action approach focuses computational resources on the most impactful areas of the fracturing treatment, such as segment boundaries, monitoring well locations, and zones with uncertain fracture behavior, thereby reducing overall computational energy consumption while maintaining high productivity.
Solution Approach 2:
The system performs preliminary data processing, feature extraction, and model training using historical data before the actual fracturing treatment begins. By pre-processing and preparing the machine learning models in advance, the system reduces real-time computational requirements during the treatment, allowing efficient real-time decision-making without excessive energy consumption at the time of execution.
Data Source
AI summary
A hydraulic fracturing system and method identifies, for each of two or more segments of a hydraulic fracturing treatment applied downhole in a wellbore, at least one contributing factor that leads to a fracturing event during each respective segment, applies pattern recognition to fracturing data associated with one or more monitoring wells to identify at least one precursor for each of the contributing factors of a selected fracturing event, determines, based on the identified at least one precursor for each of the contributing factors of the selected fracturing event, whether the selected fracturing event is likely to occur in the wellbore.