Neural Network Completion Equipment Selection
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Solution Overview
Problem
The existing methods for selecting completion equipment for wellbores are time-consuming and costly, as they often require trial and error, and lack efficiency in identifying the most suitable equipment for specific wellbore conditions.
Innovation Solution
A neural network system that processes data on wellbore characteristics to determine the likelihood of success for different types of completion equipment, allowing for automated or assisted selection of the most suitable equipment based on quality metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trial and error methods are used to select completion equipment, then equipment selection can be made, but the process is time-consuming and costly
Solution Approach 1:
The system performs preliminary analysis by training a neural network model on historical wellbore data and completion equipment performance before actual equipment selection is needed. This pre-computed knowledge base enables rapid equipment selection without trial-and-error during the actual selection process, resolving the contradiction between selection accuracy and time consumption.
Solution Approach 2:
The patent replaces the mechanical trial-and-error process with an intelligent information processing system. A neural network model processes wellbore characteristics and historical data to predict optimal completion equipment, substituting physical experimentation with computational analysis, thereby eliminating time loss while maintaining or improving selection reliability.
2Reliability
If multiple types of completion equipment are tested through trial and error, then the most suitable equipment can be identified, but the process becomes costly
Solution Approach 1:
The system performs preliminary evaluation by analyzing historical performance data of multiple equipment types across similar wellbores before deployment. The neural network predicts which equipment types are most suitable based on matched characteristics, eliminating the need for costly trial-and-error testing of multiple equipment types in the actual wellbore.
Solution Approach 2:
The system uses historical wellbore data and performance records as copies of real-world scenarios to train the neural network model. This virtual copying of past experiences allows the system to learn optimal equipment selections without physically testing equipment in each new wellbore, reducing costs while maintaining reliability.
3Productivity
If conventional methods are used to select completion equipment, then equipment can be deployed, but efficiency in resource extraction is reduced
Solution Approach 1:
The system performs preliminary optimization by identifying the most suitable completion equipment before well construction begins. By pre-analyzing wellbore characteristics and predicting optimal equipment performance, the system eliminates delays associated with trial-and-error equipment testing during construction, thereby improving both construction speed and eventual resource extraction efficiency.
Solution Approach 2:
The neural network model incorporates feedback from historical wellbore performance data to continuously improve equipment selection accuracy. This learned feedback mechanism enables the system to predict which equipment configurations will maximize resource extraction efficiency for specific wellbore types, avoiding inefficient equipment choices that would waste time during construction and operation.
Data Source
AI summary
In an example method, a system obtains first data indicating a plurality of properties of a wellbore, and determines a plurality of types of completion equipment available for deployment at the wellbore. Further, the system determines, using a computerized neural network, a plurality of quality metrics based on the first data. Each of the quality metrics represents an estimated likelihood of success of operating a respective one of the types of completion equipment at the wellbore. Further, the system causes a graphical user interface to be displayed to a user. The graphical user interface includes a concurrent presentation of an indication of each of the types of completion equipment, and an indication of each of the quality metrics.