The object of this study was to develop and present a solution based on Artificial Intelligence and a Recurrent Neural Network (RNN) algorithm to identify anomalies and behavioral patterns in real-time drilling sensors. The aim is to enhance data value extraction efficiency during drilling operations, increase drilling speed, and mitigate risks in well development. The algorithm's performance and effectiveness in detecting anomalies will be thoroughly evaluated, and it will be adaptable to specific drilling conditions.
By incorporating the anomalies and behavior patterns recognized through artificial intelligence (AI) during a drilling operation into our Recurrent Neural Network (RNN) algorithm, it examines the values transmitted by each sensor in real-time and determines the probability of identifying an anomaly through the analysis of time series data. If an anomaly is detected, the algorithm sends a signal or visible alarm through a web interface during the operations.
The AI model utilized in this study empowers users to adapt and configure it effectively, ensuring a more accurate representation of specific drilling conditions. Furthermore, we thoroughly examine the testing methodology employed to assess the algorithm's performance and efficacy in detecting anomalies. The outcomes highlight the algorithm's robustness and its potential for enhancing drilling operations by effectively identifying and addressing anomalies in real-time.
The proposed AI-based solution with Recurrent Neural Networks (RNN) has significant potential in the Oil & Gas industry. By integrating real-time anomaly detection and behavioral pattern recognition, it revolutionizes drilling processes. Enhanced drilling efficiency is achieved by optimizing parameters like Bit Depth (BD), Measured Depth (MD), Hook Height (HKHG), Rate of Penetration (ROP), Hook Load (HKLD), (Weight on Bit) WOB, Torque (TQ), and Revolutions Per Minute (RPM). This improves overall efficiency and safety, minimizing equipment failures and risks. The research's significance lies in addressing industry challenges and enhancing data-driven decision-making. The novelty is the advanced AI-RNN methodology, paving the way for intelligent drilling practices.
