1887

Abstract

Summary

For enhancing hydrogen recovery and storage projects, one of the main challenges in tracking hydrogen fronts in reservoirs is the geological and petrophysical heterogeneity and complexity of such formations. We present an innovative artificial intelligence framework to track hydrogen fronts in subsurface storage reservoirs based on deep measurement data such as electromagnetics surveys and acoustic impedance, as well as porosity profiles.

This framework was tested on a Pohokura field simulation study for hydrogen storage incorporating interwell deep electromagnetic surveys, acoustic impedance, and porosity profiles. Expert processing was also included in the pre-processing stage to remove inconsistent measurements in the training data. Overall, all regression trees showed a high resemblance to the original hydrogen front. However, the tree with the smallest minimum leaf size, which is the ultra-fine tree, has the highest accuracy and certainty. As for the computational time, the coarse tree is considered to be the fastest. Comparison of the outcomes is discussed and illustrated.

This AI framework represents an innovative approach to support hydrogen fronts tracking for hydrogen storage, by combining deep measurement data such as electromagnetic surveys, acoustic impedance, and porosity profiles with an artificial intelligence framework.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202310061
2023-06-05
2026-02-06

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References

  1. Alkinani, H. H., Al-Hameedi, A. T., Dunn-Norman, S., Alsaba, M. T., & Amer, A. S. (2019). Review of the Applications of Decision Tree Analysis in Petroleum Engineering with a Rigorous Analysis. SPE Middle East Oil and Gas Show and Conference. Manama.
     [Google Scholar]
  2. Fuad, I. I., Rosli, L. H., & Husni, H. (2019). Application of Hybrid ARIMA - Decision Tree Algorthim in Reservoir Pressure Surveillance. Abu Dhabi International Petroleum Exhibition & Conference. Abu Dhabi.
     [Google Scholar]
  3. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. New: Springer.
     [Google Scholar]
  4. Katterbauer, K., Al Qasim, A., Al Shehri, A., & Yousif, A. (2022). A Novel Artificial Intelligence Framework for the Optimal Control of Wireless Temperature Sensors for Optimizing Oxygen Injection in Subsurface Reservoirs. Offshore Technology Conference Asia. Kuala Lumpur: SPE.
     [Google Scholar]
  5. Katterbauer, K., Qasim, A., Marsala, A., & Yousef, A. (2021). A Data Driven Artificial Intelligence Framework for Hydrogen Production Optimization in Waterflooded Hydrocarbon Reservoir. Abu Dhabi International Petroleum Exhibition & Conference. Abu Dhabi.
     [Google Scholar]
  6. Turner, J. (2004). Sustainable hydrogen production. Science, 305(5686), 972–974.
     [Google Scholar]
  7. Zhang, T., Zhang, Y., Katterbauer, K., Al Shehri, A., Sun, S., & Hoteit, I. (2022). Phase equilibrium in the hydrogen energy chain. Fuel, 328, 125324.
     [Google Scholar]
/content/papers/10.3997/2214-4609.202310061
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Tracking hydrogen fronts from deep measurements utilizing AI – a Pohokura field study
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202310061
/content/papers/10.3997/2214-4609.202310061
/content/papers/10.3997/2214-4609.202310061
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