Edge AI Sensor Fusion for Closed-Loop Drilling Control
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Solution Overview
Problem
Conventional industrial equipment control systems operate in isolated loops, leading to fragmented optimization and reactive adjustments, failing to integrate human-like perceptual filtering and predictive cognitive modeling, which results in inefficiencies and increased operational costs due to delayed data feedback and lack of proactive responses to wellbore dynamics.
Innovation Solution
An anthropomorphic data intelligence control system with AI-enabled computing devices that process real-time sensor data, perform decision-making at the sensor edge, and control actuators across operational domains, integrating human-like reasoning and adaptive fluid formulations to manage industrial processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional isolated control loops are used for monitoring drilling parameters, then system simplicity is maintained, but real-time holistic optimization and predictive control are lost
Solution Approach 1:
The patent merges multiple isolated control loops into a single integrated control system that processes data from various sensors (pressure, temperature, flow rate, vibration, acoustic emissions) through a unified neural network architecture. This integration enables holistic optimization by analyzing interrelationships between drilling parameters that were previously managed separately, achieving real-time predictive control while maintaining manageable complexity through modular neural network design.
Solution Approach 2:
The integrated control system performs multiple functions simultaneously: it monitors drilling parameters, predicts tool failures, optimizes drilling fluid properties, controls wellbore pressure, and adjusts drilling rates. This multi-functionality is achieved through a universal neural network architecture that can process diverse sensor inputs and generate coordinated control outputs for various drilling operations, replacing multiple specialized control systems with one adaptable platform.
2Speed
If periodic manual sampling and lab analyses are used for fluid property control, then measurement accuracy is maintained, but response time to wellbore dynamics increases
Solution Approach 1:
The system performs preliminary real-time measurements of fluid properties using downhole sensors that continuously monitor density, viscosity, and composition. By establishing baseline measurements and trends before critical changes occur, the neural network can predict fluid property deviations and trigger corrective actions proactively, maintaining measurement precision while enabling faster response times compared to periodic sampling.
Solution Approach 2:
The patent replaces mechanical periodic sampling and laboratory analysis systems with electronic continuous sensing and digital signal processing. Downhole sensors transmit real-time data through the drill string to surface computers, where neural networks process the information instantly. This substitution eliminates the time delays inherent in manual sampling and lab procedures while maintaining or improving measurement precision through advanced sensor technology and digital filtering.
3Productivity
If reactive adjustments based on delayed feedback are made, then system simplicity is preserved, but non-productive time and operational costs increase
Solution Approach 1:
The system implements real-time closed-loop feedback where neural networks continuously process sensor data from the drilling environment and immediately adjust control parameters. The feedback loop monitors drilling rate, wellbore pressure, fluid properties, and tool status, predicting failures and optimizations before they occur. This proactive feedback mechanism reduces non-productive time by preventing tool failures and maintaining optimal drilling conditions, thereby improving overall productivity compared to reactive adjustments.
Solution Approach 2:
The neural network performs preliminary analysis of drilling data to predict tool failures, fluid property deviations, and wellbore instability conditions before they manifest as problems. By taking preliminary corrective actions based on predictive insights, the system prevents non-productive events such as tool failures, stuck pipe, and lost circulation, thereby maintaining continuous productive operations and reducing downtime.
4Adaptability or versatility
If separate control systems are used for individual assets, then system complexity is reduced, but adaptive control to evolving conditions is lost
Solution Approach 1:
The control system is designed to be dynamic and adaptive, with neural networks that continuously learn from incoming sensor data and adjust control strategies in real-time. The system adapts to evolving downhole conditions by modifying drilling parameters, fluid formulations, and operational procedures based on predictive analytics. This dynamic adaptability is achieved through a unified control architecture that coordinates adjustments across multiple assets simultaneously, optimizing the entire drilling operation rather than individual components.
Data Source
AI summary
An anthropomorphic AI control system including a plurality of sensors configured to collect industrial input data. The control system also includes an artificial intelligence-enabled edge-deployed computing device configured to analyze and fuse multimodal input data and generate an output through anthropomorphic computing. The computing device includes a cognitive module performing real-time decision-making at the sensor edge and a controller to manage industrial process actuators across operational domains. In management of industrial fluid flow, the control system may include AI-enabled fluid characterization modules to calculate the Reynolds number and incorporate compliance with AGA3 and AGA8 standards.