A Review of AI Applications in Unconventional Oil and Gas Exploration and Development DOI Creative Commons
Feiyu Chen,

Linghui Sun,

Siyu Jian

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(2), P. 391 - 391

Published: Jan. 17, 2025

The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving technological advancement industrial upgrading in this field. This paper systematically reviews the current applications trends AI exploration development, covering major research achievements geological exploration; reservoir engineering; production forecasting; hydraulic fracturing; enhanced recovery; health, safety, environment management. how deep learning helps predict distribution classify rock types. It also explains machine improves simulation history matching. Additionally, we discuss use LSTM DNN models forecasting, showing has progressed from early experiments to fully integrated solutions. However, challenges such data quality, model generalization, interpretability remain significant. Based on existing work, proposes following future directions: establishing standardized sharing labeling systems; integrating domain knowledge engineering mechanisms; advancing interpretable modeling transfer techniques. With next-generation intelligent systems, will further improve efficiency sustainability development.

Language: Английский

A Review of AI Applications in Unconventional Oil and Gas Exploration and Development DOI Creative Commons
Feiyu Chen,

Linghui Sun,

Siyu Jian

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(2), P. 391 - 391

Published: Jan. 17, 2025

The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving technological advancement industrial upgrading in this field. This paper systematically reviews the current applications trends AI exploration development, covering major research achievements geological exploration; reservoir engineering; production forecasting; hydraulic fracturing; enhanced recovery; health, safety, environment management. how deep learning helps predict distribution classify rock types. It also explains machine improves simulation history matching. Additionally, we discuss use LSTM DNN models forecasting, showing has progressed from early experiments to fully integrated solutions. However, challenges such data quality, model generalization, interpretability remain significant. Based on existing work, proposes following future directions: establishing standardized sharing labeling systems; integrating domain knowledge engineering mechanisms; advancing interpretable modeling transfer techniques. With next-generation intelligent systems, will further improve efficiency sustainability development.

Language: Английский

Citations

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