Machine-Learning Fracking Optimization for Variable Reservoir Properties
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Solution Overview
Problem
Existing solutions for fracking optimization are not satisfactory in maximizing natural resource production due to variations in reservoir properties, particularly in low permeability reservoirs.
Innovation Solution
An apparatus and method utilizing a processor and memory to receive reservoir data, generate training data, train a fracking optimization machine-learning model, and determine optimal production parameters for generating an optimal production plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional fracking methods are used, then the fracking process can be completed, but natural resource production is not maximized due to variations in reservoir properties
Solution Approach 1:
The patent applies parameter changes by using machine learning models to dynamically determine optimal fracking parameters (such as fracture spacing, stage length, fluid volume) based on specific reservoir properties like permeability, porosity, and geological characteristics. This allows the fracking process to be customized for each reservoir's unique properties rather than using fixed traditional parameters, thereby maximizing natural resource production while adapting to reservoir variations.
2Productivity
If machine-learning models are implemented for optimization, then production efficiency is enhanced, but system complexity increases
Solution Approach 1:
The patent implements a universal machine learning platform that can handle multiple fracking optimization tasks across different reservoir types and conditions. The system uses trained models that can be applied universally to various scenarios, making the complex technology broadly applicable rather than requiring separate custom solutions for each case, thereby managing system complexity while maintaining high productivity.
Solution Approach 2:
The patent applies preliminary action by pre-training machine learning models using historical fracking data and reservoir characteristics before actual fracking operations. This preparation phase creates ready-to-use optimization models that can quickly provide recommendations during actual fracking, reducing the computational complexity and time required during field operations while maintaining high production efficiency.
Data Source
AI summary
An apparatus and method for fracking optimization, wherein the apparatus includes at least a processor, and a memory, wherein the memory containing instructions configuring the at least a processor to receive a reservoir datum from at least a sensing device, generate a production training data include a plurality of reservoir datums as input correlated to a plurality of optimal production parameters as output, train a fracking optimization machine-learning model using the production training data, determine an optimal production parameter as a function of the fracking optimization machine-learning model, and generating an optimal production plan as a function of the optimal production parameter.