Wellbore Construction Improved by Real-Time Borehole Images
Authors: Fulda Christian , Hartmann Andreas , Prilliman Jeff , Wessling Stefan 
Published: May 2016

Abstract
Borehole imaging is a useful and well-known formation evaluation technology. In recent years, two major developments have opened the door to several new application areas. First, the technology to acquire borehole images was conveyed from pure wireline systems to logging-while-drilling systems. Imaging-while-drilling technology benefits from acquiring borehole images from a nearly unaltered borehole in an almost virgin formation. Secondly, high-end mud pulse telemetry systems, in combination with advanced data-compression methods, enable the reception and use of high-resolution borehole images while drilling. This data is available for further real-time analysis at the surface during different stages of the wellbore construction process. Several examples going beyond the pure formation evaluation aspect, demonstrate how high-resolution borehole images are used to improve wellbore construction. In particular, methods with respect to wellbore integrity, geosteering, and completion stage identification are demonstrated. These applications use the high resolution borehole images to identify borehole events such as induced fractures or breakouts, as well as formation features such as bedding, faults, or natural fractures. Wellbore integrity methods mainly use the images to identify borehole breakouts and induced fractures. Geosteering is predominantly based on the evaluation of bedding at a dipping angle with respect to the borehole that can be determined from images. The completion stage of operations benefits from the identification of natural fractures and fractures that were created during hydraulic stimulation of offset wells.

Introduction
Imaging has been used in the industry for many years in a variety of applications such as structural and sedimentary analysis, as well as fracture system analysis. Especially noteworthy are resistivity images having the highest resolution which is a direct consequence of the physical nature of the measurement. With resistivity images even small features such as natural or induced fractures can be identified.
High-resolution borehole images are data sets where a physical quantity of the formation is measured with respect to time or depth and toolface azimuth. The toolface azimuth is defined by the angle through which the sensor has been rotated around the borehole axis in a plane perpendicular to the borehole axis from a reference direction such as magnetic north or borehole high-side. The measurement is color-coded and plotted as a two-dimensional map with the first axis of the map representing either time or depth, the second axis representing the toolface azimuth, and the color corresponding to the measured resistivity. Since the penetration depth of the measurement is relatively small in the range of 1" - 2", the map corresponds in some respect to an unwrapped image of the borehole wall (Fig. 1). In this image, features in the borehole wall such as boundary layers or fractures can be identified. For example, if the borehole was drilled through a substantially planar bed boundary, this boundary will appear on the image as a sinusoid.
Historically, images were acquired with wireline-based acquisition systems which represent an enhancement of the so-called dipmeter tool where three or more substantially identical sensors are moved in close proximity to the borehole wall in a direction parallel to the borehole axis. Wireline tools often utilize a mechanism for pressing the sensors at different toolface azimuths against the borehole wall which causes them to slide along the formation while the tool is moved through the borehole. While drilling, the concept of sliding sensors is not preferred because in a drilling environment the sensors would be subject to excessive wear. Instead, the rotating drilling tool is used as a platform for a single sensor which rotates and moves along the borehole wall on a helix-shaped sensor path ([1, 2, 3]).