AI Drilling Advisory Engine for Real-Time Optimization
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Solution Overview
Problem
Conventional material processing systems, such as drilling rigs, lack real-time optimization capabilities, resulting in significant Invisible Lost Time (ILT) that accounts for up to three times the cost of Non-Productive Time (NPT), and fail to provide direct support to field personnel with actionable recommendations, hampering operational efficiency.
Innovation Solution
A drilling advisory engine is implemented within the material processing system, utilizing machine learning and physical models to process real-time and historical data, providing prescriptive recommendations for optimizing drilling operations, including Rate of Penetration and connection durations, to reduce ILT and enhance operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional material processing systems are used without real-time optimization capabilities, then device complexity is reduced, but productivity decreases due to significant Invisible Lost Time
Solution Approach 1:
The patent replaces conventional mechanical drilling control systems with an AI-based drilling advisory engine that uses machine learning models, statistical models, and physical models to process sensor data and generate prescriptive recommendations. This substitution enables real-time optimization of drilling operations, reducing Invisible Lost Time and improving productivity while managing system complexity through automated intelligent decision-making.
Solution Approach 2:
The drilling advisory engine acts as an intermediary between raw sensor data and field personnel decisions. It processes real-time sensor data, historical drilling data, and contextual data through multiple models to generate actionable prescriptive recommendations, thereby bridging the gap between complex data analysis and practical drilling operations without requiring field personnel to directly manage system complexity.
2Loss of information
If conventional analytical tools are used that focus on engineering and office support, then device complexity is minimized, but loss of information occurs because no direct support is provided to field personnel
Solution Approach 1:
The drilling advisory engine performs preliminary analysis and generates prescriptive recommendations in advance before field personnel need decisions. It continuously processes sensor data and model outputs to prepare actionable guidance on drilling parameters, connection durations, and operational optimizations, ensuring that field personnel receive timely recommendations without information loss.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from drilling operations is processed through models, and the resulting recommendations are fed back to field personnel in real-time. This feedback mechanism ensures that actionable information reaches the right users at the right time, preventing information loss while managing computing infrastructure through automated processing.
3Productivity
If real-time data processing and machine learning models are implemented, then productivity improves through reduced Invisible Lost Time, but use of energy increases due to continuous computing operations
Solution Approach 1:
The drilling advisory engine processes data at varying levels of intensity based on operational needs. It continuously monitors critical parameters through sensor data but applies full machine learning model processing only when optimization opportunities are detected or when parameters change significantly. This partial processing approach maintains productivity improvements while reducing unnecessary computing energy consumption.
Solution Approach 2:
The system dynamically adjusts processing parameters and model complexity based on drilling conditions, data quality, and operational context. It modifies the level of computational effort required for different drilling scenarios, using simpler models when appropriate and more complex analysis only when needed, thereby optimizing the balance between productivity gains and energy consumption.
Data Source
AI summary
Methods, systems, and computer storage media for providing drilling advisory recommendation using a drilling advisory engine in a material processing system that supports drilling operations. A drilling advisory recommendation identifies prescriptions (e.g., an arrangement of components and settings) in the material processing system to support real time optimization of drilling operations. The drilling advisory engine can be implemented as a real time prescriptive tool for operators (e.g., field personnel) to offer guidance for drilling operations. In operation, input data comprising real time sensor data of drilling operations, historical drilling information, and contextual drilling information associated with a drilling site are accessed. The input data is analyzed using two or more drilling advisory models that support generating drilling advisory recommendations that identify prescriptions for drilling operations. Based on analyzing the input data, a drilling advisory recommendation for drilling operations of the drilling site are generated. The drilling advisory recommendation is communicated.