New York : Wiley, c1986.

Logging has come a long way from the simple electrical devices of the early years. Today's tools are considerably more accurate and are used for an increasingly diverse number of tasks. Among these are tools that characterise geological properties of rocks in the borehole. Combined with new technology to drill deviated wells, the geoscientist now has tools which allow him to characterise and develop reservoirs more accurately than ever. This book, written for researchers, graduate students and practising geoscientists, documents these techniques and illustrates their use in a number of typical case studies.

Following the success of the Drilling Data Handbook, Editions Technip has designed this book to cover the well logging principles and its applications. This well logging handbook first edition starts with a summary on geology and petrophysics focusing mainly on its applications. The wide range of logging measurements and applications is covered through eleven sections, each of them organized into four chapters. All in all, this is a strongly-bound, user-friendly book with useful information for those involved in all aspects and applications of well-logging. The paging is notched and externally labelled alphabetically to allow a quick access.

This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, machine learning will ultimately benefit these users. The book is written by a practitioner of machine learning and statistics, keeping geoscientists in mind. It highlights the need to go beyond concepts covered in STAT 101 courses and embrace new computational tools to solve complex problems in geosciences. It also offers practitioners, researchers, and academics insights into how to identify, develop, deploy, and recommend fit-for-purpose machine learning models to solve real-world problems in subsurface geosciences.