Data-driven Approach for Kick Detection and Trend Analysis in Drilling Operations

Abstract

In the rapidly evolving landscape of the oil and gas industry, maintaining operational safety and optimizing drilling efficiency in complex wells, such as horizontal, extended reach, and HPHT wells, have emerged as paramount concerns. This bachelor thesis proposes a novel approach to address these challenges through real-time trend analysis and predictive modeling of drilling parameters.

Utilizing OpenLab, a sophisticated drilling simulator developed by NORCE, a unique method is tested using real-time drilling data. The ability to analyze real-time data significantly enhances our understanding of drilling processes in intricate well structures. This program is uniquely designed to detect parameter anomalies that deviate from set thresholds, effectively flagging potential issues for parameters such as downhole pressure, active pit volume, and various flow rates.

A key aspect of the program lies in its inherent flexibility, allowing users to select and analyze any well parameter. This customized approach, underpinned by real-time data analysis, facilitates informed decision-making, thus optimizing drilling performance across a wide range of scenarios.

This bachelor thesis illuminates a practical and innovative approach to automating drilling processes in complex wells, through the strategic blend of real-time trend analysis, predictive modeling, and user configurability. The results of this study provide substantial implications for the oil and gas industry, offering a viable route to improved drilling efficiency, cost reduction, and heightened safety standards.

In the rapidly evolving landscape of the oil and gas industry, maintaining operational safety and optimizing drilling efficiency in complex wells, such as horizontal, extended reach, and HPHT wells, have emerged as paramount concerns. This bachelor thesis proposes a novel approach to address these challenges through real-time trend analysis and predictive modeling of drilling parameters.

Utilizing OpenLab, a sophisticated drilling simulator developed by NORCE, a unique method is tested using real-time drilling data. The ability to analyze real-time data significantly enhances our understanding of drilling processes in intricate well structures. This program is uniquely designed to detect parameter anomalies that deviate from set thresholds, effectively flagging potential issues for parameters such as downhole pressure, active pit volume, and various flow rates.

A key aspect of the program lies in its inherent flexibility, allowing users to select and analyze any well parameter. This customized approach, underpinned by real-time data analysis, facilitates informed decision-making, thus optimizing drilling performance across a wide range of scenarios.

This bachelor thesis illuminates a practical and innovative approach to automating drilling processes in complex wells, through the strategic blend of real-time trend analysis, predictive modeling, and user configurability. The results of this study provide substantial implications for the oil and gas industry, offering a viable route to improved drilling efficiency, cost reduction, and heightened safety standards.