Automated well-log pattern alignment and depth-matching techniques: an empirical review and recommendations.

Ezenkwu, Chinedu Pascal; Guntoro, John; Starkey, Andrew; Vaziri, Vahid; Addario, Maurillio

Abstract

Well logging has been an integral part of decision making at different stages (drilling, completion, production, abandonment) of a well's history. However, the traditional human-reliant approach to well-log interpretation, which has been the most common practice in the industry, can be time consuming, subjective, and incapable of identifying fine details in log curves. Previous studies have recommended automated approaches as a candidate for addressing these challenges. Despite the progress made so far, what is not yet clear from the existing literature is the extent to which these automated approaches can dispense with human interventions in real-life scenarios. This paper presents an empirical review of different depth-matching techniques in real-life timelapse well logs, primarily focusing on gamma ray and the extent to which the outcomes of these techniques match the results from a human expert. Specifically, the performances of dynamic time warping (DTW), constrained DTW (CDTW), and correlation optimized warping (COW) are investigated. The experiments also consider the effects of filtering and normalization on the performance of each of the techniques. Concerning the correlations of each technique's outcome with the reference data and an expert-generated outcome, this research identifies and discusses its key challenges, as well as provides recommendations for future research directions. Although the COW technique has its limitations, as discussed in this paper, our experiments demonstrate that it shows more potential than DTW and its variants in the well-log pattern alignment task. The work entailed by this research is significant because identifying and discussing the limitations of these techniques is vital for solution-oriented future research in this area.

Citation

EZENKWU, C.P., GUNTORO, J., STARKEY, A., VAZIRI, V. and ADDARIO, M. 2023. Automated well-log pattern alignment and depth-matching techniques: an empirical review and recommendations. Petrophysics [online], 64(1), pages 115-129. Available from: https://doi.org/10.30632/PJV64N1-2023a9

• Journal Article Type: Article
• Acceptance Date: Oct 25, 2022
• Online Publication Date: Feb 28, 2023
• Publication Date: Feb 28, 2023
• Deposit Date: Jun 22, 2023
• Publicly Available Date: Jul 19, 2023
• Journal: Petrophysics
• Print ISSN: 1529-9074
• Publisher: Society of Petrophysicists and Well Log Analysts
• Peer Reviewed: Peer Reviewed
• Volume: 64
• Issue: 1
• Pages: 115-129
• DOI: https://doi.org/10.30632/PJV64N1-2023a9
• Keywords: Well logging; Data analysis; Automation
• Public URL: https://rgu-repository.worktribe.com/output/1987640