Machine Learning Hydraulic Fracturing Prediction Framework
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Solution Overview
Problem
Hydraulic fracturing operations are constrained by geological complexities, operational constraints, and economic considerations, with existing numerical or analytical techniques being computationally intensive, time-consuming, and unable to capture multi-dimensional complexities of real-world reservoir conditions, leading to suboptimal decision-making and optimization.
Innovation Solution
A data-driven approach utilizing machine learning (ML) techniques to process large datasets, predict production data, and optimize hydraulic fracturing parameters, including fluid volumes, proppant volumes, and injection rates, through frameworks that integrate ML models with optimizers for real-time adaptability and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If numerical or analytical techniques are used to assess hydraulic fracturing constraints, then prediction accuracy is improved, but computational time and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by training machine learning models offline using historical well data before actual hydraulic fracturing operations. The ML models are pre-trained to capture complex geological patterns, enabling rapid predictions during field operations without requiring intensive real-time computational resources.
Solution Approach 2:
The system creates simplified copies of complex physical processes by training ML models to replicate the behavior of detailed numerical simulations. Once trained, these model copies provide accurate predictions at a fraction of the computational cost, allowing rapid assessment of multiple fracturing scenarios.
2Measurement precision
If detailed numerical models are used to capture multi-dimensional complexities of reservoir conditions, then prediction accuracy is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The system introduces machine learning models as intermediary components between raw well data and production predictions. These ML intermediaries automatically learn and capture complex multi-dimensional relationships from historical data, eliminating the need for operators to directly manage complex numerical models while maintaining high prediction accuracy.
Solution Approach 2:
The system transforms the problem from solving complex partial differential equations to using ML models that learn parameter relationships directly from data. By changing the mathematical representation from physics-based numerical models to data-driven ML models, the system maintains accuracy while reducing operational complexity.
3Productivity
If traditional analytical methods are used for hydraulic fracturing optimization, then implementation simplicity is maintained, but productivity and optimization quality deteriorate
Solution Approach 1:
The system implements dynamic optimization by using ML models that can rapidly evaluate multiple fracturing scenarios and adapt predictions to specific well conditions. The optimization process dynamically adjusts fracturing parameters based on learned patterns from historical data, enabling high-quality optimization that adapts to each unique reservoir situation.
Solution Approach 2:
The system incorporates feedback mechanisms where ML models continuously learn from historical well performance data and operational outcomes. This feedback loop improves prediction accuracy over time and enables the optimization system to incorporate real-world results, progressively enhancing optimization quality while maintaining ease of use.
Data Source
AI summary
A method can include receiving data for a well in a field and parameter values for hydraulic fracturing of the well in the field; predicting production data responsive to the hydraulic fracturing of the well using at least a portion of the data and at least a portion of the parameter values as input to a machine learning model, where the machine learning model is trained using historical data for the field; and outputting the predicted production data.