Well-log depth matching is a long-standing challenge within the oil industry despite its importance in developing log interpretation algorithms exploiting correlations between different measurements. Gamma-ray logs are widely used as a proxy to match the depth of measurements acquired from different logging passes. Existing approaches are either manual or algorithm-assisted and are based on correlations. None performs well without user intervention. We have developed a supervised machine-learning-based solution that uses clever problem abstraction and data formation to alleviate the difficulty of the problem. A fully connected neural network was trained on data labeled through manual depth matching of field data with label-preserving data augmentation. A relaxed-accuracy criterion was adopted to improve the training effectiveness to deal with the unavoidable human error during manual labeling. Stacking techniques were employed to guarantee the robustness of this method. The solution led to well synchronized signals and made automation possible for the depth-matching process. The current development mainly focuses on wireline applications for vertical or low-deviation wells.
Machine-Learning-Based Automatic Well-Log Depth Matching
Zimmermann, Timon, Liang, Lin, and Smaine Zeroug. "Machine-Learning-Based Automatic Well-Log Depth Matching." Petrophysics 59 (2018): 863–872. doi: https://doi.org/10.30632/PJV59N6-2018a10
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