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Early Kick Detection: Sensors, Data Acquisition, and Analysis

Date: May 15th
Time: 1800 (Presentations start 1830)
Victoria Hotel

The presentation is free of charge and open to everyone. Registration is only required for the following dinner. Cancellations must be made before the registration deadline and are subject to a small fee.

Dinner Menu:

Starter: Tartar of calf
Main: Coconut poached cod
Dessert: Tiramisu

Early Kick Detection: Sensors, Data Acquisition, and Analysis

By Distinguished Lecturer Jaideva Goswami (NOV)


One key message from this lecture: Kicks can be catastrophic, but recent and future developments in sensor technology, data processing, and telemetry are enabling timely identification and mitigation plans.
A kick or an influx may be defined as an unintended flow of formation fluid into the borehole. It occurs when the wellbore pressure falls below the formation pore pressure. Not all kicks are dangers; however, an uncontrolled kick can lead to catastrophic events. A reliable early kick detection and monitoring system is critical to maintaining wellbore stability. Typically, the gain in pit volume and change in flow rate serve as some of the primary kick indicators. Secondary indicators include borehole pressure, temperature, resistivity, mud properties, and cuttings. While the gain in pit volume can indicate the kick with higher reliability, the downhole measurements in conjunction with high-speed telemetry and advanced real-time processing algorithms can be an effective early warning system, enabling timely mitigation plans. This lecture begins with some basics of wellbore stability, kick indicators, and monitoring systems. Various downhole sensor measurements and their relevance to kick detection are discussed. Both experimental and field examples are presented to illustrate the methodology. Pressure data are analyzed to estimate change in density and correlate it with the type, location, and evolution of the influx along the wellbore. Some innovative ideas on sensor design and real-time data processing for event detection and uncertainty quantification are discussed.   

Jaideva Goswami received his Ph.D. in Electrical Engineering from Texas A&M University in 1995. He is a Chief Scientist at NOV Inc, Houston. Previously, he was an Engineering Advisor and a Global Métier Manager at Schlumberger. He was also a Professor at the Indian Institute of Technology (IIT), Kharagpur, and has held academic positions at the University of Illinois, Urbana-Champaign and IIT, Kanpur. He has many publications and patents in the areas of electromagnetics, signal processing, sensor design, inverse problems, nuclear magnetic resonance, geophysical measurements, and interpretation. Dr. Goswami is a Fellow of the Institute of Electrical and Electronics Engineers.

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