Real-Time Drilling Trouble Prediction Using Stand-Pipe-Pressure Estimation
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Solution Overview
Problem
The petroleum industry faces significant challenges with stuck pipe issues in horizontal wells, leading to decreased drilling efficiency due to the difficulty in accessing and correcting the problem, which requires substantial resources and time.
Innovation Solution
A real-time drilling trouble prediction system using stand pipe pressure (SPP) real-time estimation, employing artificial intelligence (AI) techniques to monitor drilling operations, identify abnormal behavior, and generate proactive alerts and recommendations to prevent stuck pipe and wellbore instability issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time monitoring and AI predictive models are implemented to detect drilling troubles early, then drilling efficiency and safety are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting drilling parameters (stand pipe pressure, flow rate, temperature, etc.) and using AI predictive models to forecast potential stuck pipe conditions before they occur. This allows drilling operations to be adjusted proactively, preventing efficiency losses from actual stuck pipe incidents.
Solution Approach 2:
The patent introduces an intermediary AI-based predictive system that acts as a mediator between raw drilling data and operational decisions. The system processes multiple drilling parameters through machine learning models to generate predictions, serving as an intelligent intermediary that translates complex data into actionable insights without requiring direct human analysis of all parameters.
2Reliability
If AI predictive models and real-time data processing are deployed to predict drilling issues, then drilling safety is improved, but use of energy and computational resources increase
Solution Approach 1:
The system applies partial action by selectively monitoring and processing the most critical drilling parameters (such as stand pipe pressure and flow rate) that have the highest predictive value for stuck pipe conditions. Rather than analyzing all possible drilling data equally, the AI models focus on key indicators that provide the greatest safety benefit with minimal computational overhead.
Solution Approach 2:
The AI predictive models are designed to self-optimize their computational resource usage by automatically adjusting their processing intensity based on the significance of detected patterns. When anomalies are detected, the system increases analysis depth; during normal operations, it reduces computational load, allowing the system to self-regulate energy consumption while maintaining safety.
Data Source
AI summary
Raw, real-time drilling data is pulled from a centralized database for processing. The raw, real-time drilling data is re-formatted into a format required for processing by one or more predictive models. Real-time processing is performed with respect to one or more drilling parameters associated with the re-formatted data using the one or more predictive models to generate output data. The output data received from the one or more predictive models is re-formatted for storage in the centralized database. The reformatted output data is retrieved from the centralized database for analysis with respect to visualization, generating alerts, or generating recommendations.