AI Well Intervention Equipment Selection Using Reinforcement Learning
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Solution Overview
Problem
The selection of well interventions in hydrocarbon operations is resource-intensive and prone to uncertainties, often leading to disruptions and inefficiencies due to improper choices.
Innovation Solution
An AI-driven system utilizing Convolutional Neural Networks (CNN) and Reinforcement Learning (RL) to analyze well data and optimize well intervention sequences, providing tailored recommendations for intervention equipment and methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional well intervention selection methods are used, then comprehensive analysis can be performed, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The patent replaces traditional mechanical analysis methods with an AI-based neural network system. The neural network automatically processes well data, operating conditions, and intervention options to generate optimized intervention sequences, substituting manual expert analysis with automated intelligent processing that is both faster and equally reliable.
Solution Approach 2:
The system enables self-service by allowing the neural network to autonomously analyze well data, evaluate multiple intervention scenarios, and generate optimized intervention sequences without requiring extensive manual intervention. The system serves itself by continuously learning from historical data and improving its decision-making capabilities.
2Reliability
If extensive analysis and simulations are performed for well intervention selection, then better decisions can be made, but resource consumption increases
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on extensive historical well data and simulation results before actual intervention selection. This pre-processing phase allows the network to learn optimal intervention patterns in advance, so that during actual operation, quick decisions can be made without performing extensive real-time simulations, thus reducing computational resource consumption while maintaining high accuracy.
3Productivity
If multiple intervention options are evaluated to optimize well operation, then operational performance improves, but the complexity of the selection process increases
Solution Approach 1:
The patent implements universality by designing a multi-functional neural network system that can handle various well types, operating conditions, and intervention options through a single unified platform. The network is trained on diverse data and can adapt to different scenarios, eliminating the need for separate analysis systems for different well conditions and reducing overall system complexity.
Data Source
AI summary
A method for determining and performing an optimum well intervention sequence on a well operation described by an operating condition. The method includes obtaining a first well data for the well operation and determining, using an artificial intelligence (AI) model with the first well data as input, a first operating condition for the well operation. The method further includes obtaining a plurality of well interventions that can be performed on the well operation, determining, using a reinforcement learning (RL) policy, an optimum well intervention sequence that optimizes a performance of the well operation and performing the optimum well intervention sequence on the well operation.