Context-Based Dysfunction Codex for Accurate Drilling Prediction
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Solution Overview
Problem
As the complexity of physical systems increases, accurately predicting dysfunctions becomes more challenging, leading to difficulties in controlling complex systems like those used in hydrocarbon exploration and production, resulting in increased downtime and costs.
Innovation Solution
A dysfunction codex is employed, which includes a plurality of selectable dysfunction models based on context, using unsupervised and supervised learning to cluster system data and generate models that predict dysfunctions by applying the appropriate model to input system data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single comprehensive dysfunction model is used for complex physical systems, then it can cover all possible dysfunctions, but the prediction accuracy decreases due to the overwhelming complexity and number of potential dysfunctions
Solution Approach 1:
The patent segments the single comprehensive dysfunction model into multiple context-specific dysfunction models. Each model is trained on data from a specific context (e.g., normal operation, high stress, extreme conditions), allowing the system to divide the complex prediction task into manageable, context-specific sub-tasks that maintain high accuracy while collectively covering all possible dysfunctions.
Solution Approach 2:
The patent implements a dynamic model selection mechanism that automatically selects the appropriate dysfunction model based on the current operational context of the physical system. This dynamic adaptation allows the system to switch between different models as conditions change, ensuring both comprehensive coverage and high prediction accuracy without requiring a single static comprehensive model.
2Measurement precision
If multiple context-specific dysfunction models are used, then prediction accuracy improves for each context, but the system complexity increases due to having multiple models to manage
Solution Approach 1:
The patent creates a universal dysfunction prediction system that uses a single model selection mechanism to manage multiple context-specific models. The model selection component serves as a universal interface that handles all prediction requests regardless of context, automatically routing them to the appropriate specialized model. This allows the system to maintain high prediction accuracy through context-specific models while presenting a simplified, unified interface that masks the underlying complexity.
Solution Approach 2:
The patent introduces a model selection mechanism as an intermediary between the user and the multiple dysfunction models. This intermediary automatically determines which model to use based on the input data and operational context, eliminating the need for users to manually manage or select between multiple models. The intermediary handles the complexity of having multiple models internally while presenting a simple, unified interface to the user.
3Measurement precision
If context-based model selection is implemented, then prediction accuracy is improved by using the most relevant model, but the computational overhead increases due to determining the appropriate context
Solution Approach 1:
The patent performs preliminary actions by pre-defining context criteria and thresholds for each dysfunction model before actual prediction is needed. The system pre-processes reference data and establishes decision rules for context determination, so that during runtime, the model selection process can quickly compare current system state against pre-established criteria without performing complex computations. This preliminary preparation significantly reduces the computational overhead during actual prediction while maintaining high accuracy.
Data Source
AI summary
Aspects of the subject technology relate to systems and methods for predicting dysfunctions in physical systems. A dysfunction codex can be provided that includes a plurality of dysfunction models for predicting one or more dysfunctions in a physical system based on one or more specific contexts of the physical system. The dysfunction codex can be applied by selecting a dysfunction model of the plurality of dysfunction models within the dysfunction codex to apply based on the one or more specific contexts of the physical system. Further, a dysfunction of the physical system can be predicted by applying the dysfunction model to input system data of the physical system to predict the dysfunction of the physical system.