LSTM Neural Network for Wellbore Material Placement Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing well systems face challenges in accurately and efficiently controlling material placement during hydraulic fracturing, particularly in multi-stage, stimulated wellbores, due to complex non-linear physics and spatial variations, which affects the effectiveness and efficiency of hydrocarbon extraction.
Innovation Solution
A recurrent neural network, specifically a long short-term neural network (LSTM) model, is used to predict response variables in real-time by processing surface data from sensors, enabling precise control of material placement through a computing device communicatively coupled with a pump, incorporating features like backpropagation through time, cross-entropy loss, and convolutional layers to handle spatial and temporal changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional control methods are used for material placement during hydraulic fracturing, then the system is simpler to operate, but the precision and accuracy of material placement control deteriorates due to complex non-linear physics and spatial variations
Solution Approach 1:
The patent replaces traditional mechanical control systems with a neural network-based intelligent control system. The neural network processes surface data and predicts downhole conditions, enabling precise material placement control without complex mechanical adjustment mechanisms. This substitution achieves high precision while managing complexity through software-based solutions.
Solution Approach 2:
The patent introduces a neural network as an intermediary between surface data and material placement control decisions. This intermediary processes and interprets complex non-linear physics and spatial variations, translating surface observations into accurate downhole control actions, thereby improving precision without directly increasing operational complexity.
2Productivity
If real-time control of material placement is implemented, then the efficiency of hydrocarbon extraction improves, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary training of the neural network using historical well data before actual material placement operations. This pre-training establishes the model's predictive capabilities in advance, allowing real-time control during operations to proceed efficiently without extensive computational delays. The preliminary action of training transfers computational burden to an offline phase.
Solution Approach 2:
The patent implements continuous real-time monitoring and control of material placement using the trained neural network. By maintaining continuous predictive control based on ongoing surface data, the system optimizes hydrocarbon extraction efficiency without intermittent computational interruptions, achieving sustained productivity improvement.
3Reliability
If accurate prediction of downhole conditions is achieved, then the effectiveness of material placement improves, but the complexity of processing spatially varying data increases
Solution Approach 1:
The patent replaces complex physical measurement and analysis systems with a neural network-based predictive system. The neural network processes spatially varying surface data and predicts downhole conditions through learned patterns rather than direct measurement, achieving high reliability while managing data processing complexity through intelligent algorithms.
Solution Approach 2:
The patent creates a virtual model (neural network) that copies and simulates downhole conditions based on surface data. This virtual copy allows accurate prediction of material placement effectiveness without directly measuring complex downhole parameters, reducing the need for sophisticated physical sensing and processing systems.
Data Source
AI summary
A system for multi-stage placement of material in a wellbore includes a recurrent neural network that can be configured based on data from a multi-stage, stimulated wellbore. A computing device in communication with a sensor and a pump is operable to implement the recurrent neural network, which may include a long short-term neural network model (LSTM). Surface data from the sensor at each observation time of a plurality of observation times is used by the recurrent neural network to produce a predicted value for a response variable at a future time, and the predicted value for the response variable is used to control a pump being used to place the material.