Machine-Learning Reservoir Modeling for Fracture Geometry Prediction
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Solution Overview
Problem
Existing technologies face challenges in optimizing the fracking process for low permeability reservoirs to maximize natural resource harvesting due to significant differences in reservoir properties after large-scale fracking.
Innovation Solution
An apparatus and method for generating a reservoir model using a processor and memory to receive condition data, generate reservoir conditions, identify flagged data, and predict reservoir geometry, incorporating machine-learning models to enhance fracking optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large-scale fracking is performed to maximize natural resource harvesting, then production volume increases, but reservoir properties become significantly different and harder to control
Solution Approach 1:
The system performs preliminary actions by predicting reservoir geometry and identifying flagged data before fracking operations commence. Machine learning models analyze condition data to pre-determine optimal fracture network configurations and potential issues, allowing operators to prepare mitigation strategies in advance and avoid unexpected reservoir property variations during production.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring reservoir conditions and comparing them against predicted values. The machine learning models process real-time condition data, identify deviations (flagged data), and provide feedback on reservoir performance, enabling dynamic adjustment of fracking parameters to maintain consistent reservoir properties despite high production volumes.
2Reliability
If fracking optimization is implemented to manage reservoir properties, then production stability improves, but the complexity of the optimization system increases
Solution Approach 1:
The system uses copying by creating virtual replicas of the reservoir model through machine learning algorithms. Instead of directly managing complex physical reservoir properties, the system copies reservoir behavior into digital twin models that can be simulated and analyzed, simplifying the optimization process while maintaining production stability.
Solution Approach 2:
The machine learning models serve as intermediaries between raw condition data and fracking control decisions. These intermediary models process complex reservoir properties into actionable predictions and flagged data, reducing the complexity of direct control while maintaining reliable production stability.
3Productivity
If real-time prediction of reservoir conditions is implemented, then operational efficiency increases, but data processing requirements and system complexity increase
Solution Approach 1:
The system extracts and isolates critical information from complex condition data through machine learning models. By taking out only the most relevant features and predictions (such as flagged data indicating potential issues), the system achieves real-time operational efficiency without processing all raw data, thereby reducing overall system complexity.
Solution Approach 2:
The data processing system is segmented into specialized machine learning modules that handle different aspects of reservoir prediction independently. Each model processes specific condition data types and generates targeted predictions, allowing real-time operation while distributing computational complexity across multiple specialized components rather than a single monolithic system.
Data Source
AI summary
In an aspect, an apparatus for generating a reservoir model is disclosed. The apparatus includes at least a processor and memory communicatively connected to the at least a processor. The memory instructs the processor to receive a condition data associated with a target well. The memory instructs the processor to generate a plurality of reservoir conditions associated with the target well as a function of the condition data. The memory instructs the processor to identify a plurality of flagged data as a function of the plurality of reservoir conditions. The memory instructs the processor to predict reservoir geometry associated with the target well as a function of the plurality of flagged data.