COURSE OVERVIEW

 This practical and highly-interactive course includes real-life case studies and exercises where participants will be engaged in a series of interactive small groups and class workshops. 

 

This course is designed to provide participants with a detailed and up-to-date overview of Pulsed Neutron Cased Hole Formation Evaluation (Basic to Intermediate). It covers the importance of formation evaluation and the differences between open hole and cased hole logging; the  principles of neutron physics, pulsed neutron logging tools and measurement principles in pulsed neutron logging; the impact of casing and cement on neutron measurements and wellbore fluids and their influence; and the Sigma logging and difference between gas and water zones using Sigma. 

 

Further, the course will also cover the well candidate selection for pulsed neutron logging including tool calibration procedures and pre-job environmental assessment; the data acquisition techniques, neutron interaction with reservoir rock and cross-section (Sigma) analysis; the neutron lifetime and decay curves as well as advanced modes covering spectral gamma ray (SGR) logging, pulsed neutron spectroscopy for elemental analysis, neutron capture spectroscopy (NCS) and neutron-induced gamma-ray spectroscopy; the reservoir saturation monitoring and formation porosity analysis; and the effect of water salinity on Sigma, cross-section, fresh versus saline water zones, variable water salinity and challenges in mixed salinity environments. 

 

During this interactive course, participants will learn the gas zone identification and cement and casing integrity analysis; the pulsed neutron spectroscopy and time-lapse monitoring in reservoirs and integration of pulsed neutron logs with other logs; the formation lithology and mineralogy, data quality control (QC), advanced crossplot techniques and workflow for cased hole reservoir evaluation; identifying problems in high-pressure, high-temperature wells and addressing challenges in horizontal and deviated wells; managing tool calibration errors; and the advances in tool technology and role of machine learning in log analysis.