Abstract
Sidewall coring is a cost-effective process to complement conventional fullbore coring. Because sidewall cores target exact depth points, verification of the sidewall core recovery depth is required. We present an automated, fast workflow to perform the depth verification using borehole images, thereby providing consistent results. An application example using a typical dataset is used to showcase the workflow. A novel automated approach based on image analysis techniques and Bayesian statistical analysis is developed to verify sidewall core recovery depth using borehole image logs. A complete workflow is presented covering: 1) utilization of reference logs, e.g., gamma ray, to correct image log depth using cross correlation and/or dynamic time warping, 2) automated identification of sidewall core cavity in borehole image log using the circle Hough transform, and 3) estimation of confidence in the identification using Bayesian statistics and specialized metrics. The workflow is applied on a typical dataset containing tens of sidewall core cavities with varying quality. Results are comparable to the manual interpretation from an experienced engineer. A number of observations are made. First, the use of reference logs to correct the image log allows for determining the exact well logs values where the sidewall core was sampled, which is then compared to the initial target well logs values. This increases the confidence that the target lithofacies was sampled as planned. Second, the circle Hough Transform is suitable for this problem because it provides stable solutions for partially imaged sidewall core cavities typical in pad-based borehole images. Third, the use of Bayesian statistics and specialized metrics for the problem, such as average and standard deviation borehole image intensity in the cavity, provides customizability to work with multiple types of borehole images and with varying initial depth guess uncertainties. Overall, the use of fast and automated methodology for depth verification opens up avenues for near real-time combined sidewall coring, imaging, and verification workflows. The novelty in this study lies in using a combination of image processing techniques and statistical analysis to automate an established manual workflow. The automated workflow provides consistent results in minutes rather than hours. Results also incorporate a confidence index estimation.
JASP for Audit: Bayesian Tools for the Auditing Practice
