Machine Learning Fracture Height Prediction for Staged Fracturing
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Solution Overview
Problem
Current methods for predicting fracture height in tight gas formations are time-consuming and inaccurate due to the lack of models that account for petrophysical and geomechanical properties, leading to inefficient hydraulic fracturing treatments with potential overlap and incomplete stimulation of pay zones.
Innovation Solution
A system utilizing machine learning algorithms trained with downhole sensor data to predict fracture height and reconstruct physical property logs, integrating real-time physical diagnostic measurements to enhance fracture identification and treatment optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to design staged hydraulic fracturing treatments, then treatment design can be performed with existing tools, but the process is time-consuming and inefficient when dealing with large numbers of pay zones
Solution Approach 1:
The patent replaces manual mechanical design processes with automated computer-based systems that integrate machine learning algorithms and physics-based models. The system automatically processes well log data, predicts fracture heights, and optimizes staging designs, eliminating time-consuming manual calculations and iterations while maintaining engineering accuracy.
Solution Approach 2:
The system transforms the design process by changing from static, experience-based parameter selection to dynamic, data-driven parameter optimization. Machine learning models analyze historical treatment data and formation properties to automatically determine optimal fracture spacing, stage locations, and treatment parameters, significantly reducing design time while improving productivity.
2Measurement precision
If fracture height prediction models incorporate petrophysical and geomechanical properties, then prediction accuracy improves, but model complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex prediction problem into distinct computational modules: data preprocessing, feature extraction from well logs, machine learning model training, physics-based fracture propagation simulation, and result integration. This modular architecture manages complexity by allowing each component to be developed and validated independently while maintaining high overall prediction accuracy.
Solution Approach 2:
The system creates a universal platform that handles multiple functions: acquiring and processing various types of well log data (petrophysical and geomechanical), training machine learning models, running fracture propagation simulations, and generating treatment designs. This multi-functional approach reduces overall system complexity compared to using separate specialized tools for each function.
3Measurement precision
If machine learning algorithms are trained with comprehensive downhole sensor data, then prediction accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction during the model training phase using historical data. Downhole sensor data is preprocessed, normalized, and transformed into relevant features before training begins. This preliminary preparation reduces computational burden during actual prediction operations and accelerates real-time fracture identification while maintaining high accuracy.
Solution Approach 2:
The system implements a two-stage approach where machine learning models first identify potential fracture locations from comprehensive sensor data, then physics-based models validate and refine predictions. This partial application of different methods balances computational efficiency with prediction accuracy, avoiding the need to process all data through all algorithms simultaneously.
4Productivity
If automated systems are implemented for fracture identification and treatment optimization, then treatment efficiency improves, but system complexity and initial setup requirements increase
Solution Approach 1:
The patent introduces an intermediary layer between data acquisition and treatment decision-making: a centralized processing platform that integrates machine learning algorithms, physics-based models, and user interface components. This intermediary system automatically processes sensor data, generates fracture predictions, and provides treatment recommendations, reducing the complexity burden on individual components while maintaining high overall productivity.
Data Source
AI summary
Systems and methods presented herein are configured to predict fracture height and reconstruct physical property logs using models based on machine learning algorithms and physical diagnostic measurements. In particular, physical diagnostic measurements may be used to train machine learning algorithms that can be used to predict the existence of a fracture as a function of depth. For example, physical diagnostic measurements collected by downhole sensors can be used to train the machine learning algorithms, which may then be used to predict the existence of a fracture as a function of depth based on subsequently collected physical diagnostic measurements, for example, to determine fracture height of the fracture.