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Abstract

Summary

This extended abstract introduces an innovative approach to enhance well logging data interpretation for sustainable near-field exploration in the oil and gas industry. The traditional reliance on real well logs is often hindered by data limitations and confidentiality constraints. To address this challenge, Generative Adversarial Networks (GANs) are leveraged to generate synthetic well logs that closely resemble actual data. The methodology involves data collection, GAN training, and seamless integration of synthetic logs into the interpretation workflow.

The integration of synthetic logs significantly improves reservoir characterization and decision-making in near-field exploration. Two illustrative examples are provided: a flowchart outlining a GAN-based strategy for multidimensional reservoir characterization and a visualization comparing real and synthetic well logging data. These examples highlight the potential of GANs in reservoir modeling.

In conclusion, this approach has the potential to revolutionize well logging data interpretation, offering benefits such as reduced drilling costs, improved exploration success rates, and minimized environmental impact. The presented research draws from Shahbazi et al. (2020) and signifies a significant advancement in near-field exploration practices.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202471016
2024-01-30
2025-04-22

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References

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/content/papers/10.3997/2214-4609.202471016
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Utilizing GANs for Synthetic Well Logging Data Generation: a Step Towards Revolutionizing Near-Field Exploration
Conference Proceedings 2024, 1 (2024); https://doi.org/10.3997/2214-4609.202471016
/content/papers/10.3997/2214-4609.202471016
/content/papers/10.3997/2214-4609.202471016
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