AI Drilling Hazard Prediction via Data Preprocessing
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Solution Overview
Problem
During oil and gas well drilling, drilling hazards such as stuck pipes and loss of drilling fluid circulation lead to non-productive time and costly corrective measures, often resulting from complex interactions of multiple factors including subterranean formation characteristics and drilling process parameters, which existing methods fail to effectively predict and mitigate.
Innovation Solution
A data-centric artificial intelligence approach is employed, utilizing machine learning models to analyze drilling data from reports and real-time sensors to predict drilling hazards, determine optimal well locations, and adjust drilling operations in real-time, incorporating data preprocessing, filtering, and machine learning techniques like Logistic Regression and Deep Neural Networks to identify and prevent hazards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional drilling methods are used without AI prediction, then drilling operations can be performed with simple equipment and processes, but drilling hazards occur frequently leading to non-productive time and costly corrective measures
Solution Approach 1:
The system performs preliminary action by predicting drilling hazards before they occur during the drilling process. AI models analyze historical data and real-time parameters to forecast potential hazards such as stuck pipes or loss of circulation, allowing preventive measures to be taken before the hazards manifest, thereby improving reliability without requiring complex real-time intervention systems
Solution Approach 2:
The system implements feedback by continuously monitoring drilling parameters and comparing them against predicted hazard thresholds. The AI model processes real-time data from sensors and historical records, providing feedback signals that alert operators to potential hazards, enabling proactive adjustment of drilling parameters to maintain reliable operations
2Reliability
If AI models are used to predict drilling hazards, then drilling hazards can be predicted and prevented, but data processing and model training requirements increase system complexity
Solution Approach 1:
The system applies segmentation by dividing the data processing workflow into distinct modules: data collection from multiple sensors, data cleaning and preprocessing, feature extraction, model training, and hazard prediction. This modular approach manages complexity by handling data processing in manageable segments rather than as a monolithic system
Solution Approach 2:
The system uses an intermediary data processing layer that bridges raw sensor data and AI model predictions. This intermediary layer includes data cleaning, normalization, and feature engineering components that transform raw data into meaningful inputs for the AI models, simplifying the overall system architecture by abstracting the complexity of data processing from the prediction function
3Reliability
If real-time data monitoring and AI analysis are implemented, then drilling hazards can be detected and prevented, but non-productive time may increase due to data processing and analysis requirements
Solution Approach 1:
The system performs preliminary action by pre-training AI models on historical drilling data before actual drilling operations begin. The models are trained in advance to recognize hazard patterns, so during real-time drilling, the system only needs to feed new data into the pre-trained models for quick predictions, minimizing the time required for data processing and analysis during critical drilling operations
Solution Approach 2:
The system replaces manual data analysis methods with automated AI models that can process drilling data in real-time without human intervention. The AI models automatically identify hazard patterns from sensor data, eliminating the need for manual review of drilling parameters and reducing the time loss associated with human analysis while maintaining high detection accuracy
Data Source
AI summary
A method includes inputting a dataset including data about a wellbore, a geographical area, a hydrocarbon formation, or any combination thereof, to one or more artificial intelligence models, generating a prediction, via the one or more artificial intelligence models, of a probability of a hazard event for the wellbore, an impending hazard event during active drilling of the wellbore, an optimal location for the wellbore in the geographical area, or any combination thereof, and performing or modifying drilling operations in response to the prediction generated by the one or more artificial intelligence models.