Automated Geosteering While Drilling Using Machine Learning. Case Studies

Abstract

Today's oil & gas industry faces a number of different challenges. Drilling activities are ramping up due to an increase in hydrocarbon demand combined with a reduction of easy-to-recover reserves. Horizontal drilling is growing and has become an integral part of field development. The geology is becoming more and more complex requiring drilling through dense layers targeting thin-layered reservoirs with lateral changes and anisotropy. In recent years, companies have been looking at the ways of optimizing drilling costs by increasing efficiency and process automation. This has been a driver for many companies to stay profitable and efficient in the market. One of the areas of interest for process automation has been a geosteering. Geosteering is the real-time adjustment well trajectory while drilling to maximize effective footage in the target zone. In this paper, innovative new approaches to automation of the geosteering process will be discussed. This approach has been successfully tested and deployed in several leading O&G companies. The main objective of automated geosteering is to optimize horizontal well placement while freeing up time operational geologists had spent doing routine work in order to focus on complex and more intense tasks as well as the reduction of operational errors related to human factors. This paper will provide details on several automated geosteering algorithms. They have been tested successfully on large numbers of wells. The results of automated geosteering were as close as 90% to the manual interpretations done by geologists. When the results diverged, the geologists often "agreed" with the interpretation proposed by the algorithm.