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Data-Augmented Deep Learning for Downhole Depth Sensing and Validation.

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Accurate downhole depth measurement is crucial for oil and gas operations. This study introduces data augmentation techniques to improve neural network models for casing collar locator (CCL) data, enhancing precision in well operations.

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Area of Science:

  • Petroleum Engineering
  • Machine Learning Applications
  • Data Science

Background:

  • Accurate downhole depth measurement is critical for oil and gas operations, impacting reservoir contact, production, and safety.
  • Casing collar locator (CCL) technology is fundamental for precise depth calibration in wells.
  • Existing neural network approaches for collar recognition are hindered by underdeveloped preprocessing and limited real-world data.

Purpose of the Study:

  • To develop and evaluate data augmentation methods for training neural network models on casing collar locator (CCL) data.
  • To address the challenge of limited real well data for training robust collar recognition models.
  • To improve the accuracy and generalization capabilities of neural networks used in downhole operations.

Main Methods:

  • Integrated a downhole toolstring system for CCL log acquisition to build datasets.
  • Proposed and systematically evaluated comprehensive data augmentation techniques, including standardization, label distribution smoothing (LDS), label smoothing regularization (LSR), time scaling, and random cropping.
  • Analyzed the contribution of each augmentation method through systematic experimentation on baseline neural network models (TAN and MAN).

Main Results:

  • Standardization, LDS, and random cropping were identified as essential prerequisites for model training.
  • LSR, time scaling, and multiple sampling significantly enhanced model generalization.
  • Proposed augmentation methods led to maximum F1 score improvements of 0.027 (TAN) and 0.024 (MAN) and gains up to 0.045 (TAN) and 0.057 (MAN) compared to prior studies.
  • Effectiveness was confirmed on real CCL waveforms, demonstrating practical applicability.

Conclusions:

  • The proposed data augmentation strategies effectively address data limitations for training casing collar recognition models.
  • These methods provide a technical foundation for automating downhole operations by improving CCL data analysis.
  • The study highlights the importance of specific augmentation techniques for both foundational training and enhanced generalization in neural network models for well logging.