Automated Well Log Pattern Alignment and History-Matching Techniques: An Empirical Review and Recommendations

Chinedu Pascal Ezenkwu* (Corresponding Author), John Guntoro, Vahid Vaziri, Andrew Starkey, Maurillio Addario

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
15 Downloads (Pure)


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 optimised warping (COW) are investigated. The experiments also consider the effects of filtering and normalisation 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 providing
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.
Original languageEnglish
Article numberSPWLA-2023-v64n1a9
Pages (from-to)115-129
Number of pages15
Issue number1
Publication statusPublished - 1 Feb 2023

Bibliographical note

This work was supported by the Scottish Funding Council, Advanced Innovation Voucher, and ANSA Data Analytics.


  • wll-log
  • automated
  • automation
  • DTW
  • COW
  • depth-matching
  • pattern alignment
  • AI


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