Today the oil and gas industry is in the midst of a digital revolution of reducing cost and gaining efficiency by automating many human-intensive processes. Well log depth matching from multiple wells has been traditionally labor-intensive and continues to pose a challenge to efficiently automate and remove human intervention. Multiple attempts and techniques such as cross-correlation (Zangwill et al., 1982; Kerzner et al., 1984) and Dynamic Time Warping (Kholmatov and Yanikoglu, 2005) produced mixed successes. In complex reservoirs, human intervention is required from expert geologists for manual adjustment of some intervals thus negating the advantage of a fully automated depth-matching system.
Reinforced learning technique for multi-well logs depth matching yield better reservoir delineation
Bittar, Michael, Wang, Shirui, Chen, Jiefu, and Xuqing Wu. "Reinforced learning technique for multi-well logs depth matching yield better reservoir delineation." Paper presented at the SEG International Exposition and Annual Meeting, Virtual, October 2020. doi: https://doi.org/10.1190/segam2020-w13-02.1
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