Correction: Samnioti, A.; Gaganis, V. Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II. Energies 2023, 16, 6727

Anna Samnioti; Vassilis Gaganis

doi:10.3390/en19020532

and

¹

School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Athens, Greece

²

Institute of Geoenergy, Foundation for Research and Technology-Hellas, 73100 Chania, Greece

^*

Author to whom correspondence should be addressed.

Energies2026, 19(2), 532;https://doi.org/10.3390/en19020532

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In the original publication [1], Figure 1 has been replaced with a new one due to a copyright issue and the withdrawal of reference [116] in the original publication. The updated Figure 1 is as below and the corresponding citation has been added.

Figure 1. Reservoir model with millions of grid blocks [7].

With the new citation below, the references have been reordered.

7.: Fan, Z.; Tian, M.; Li, M.; Mi, Y.; Jiang, Y.; Song, T.; Cao, J.; Liu, Z. Assessment of CO₂ Sequestration Capacity in a Low-Permeability Oil Reservoir Using Machine Learning Methods. Energies 2024, 17, 3979.

The authors state that the scientific conclusions are unaffected. This correction has been approved by the Academic Editor.

Reference

Samnioti, A.; Gaganis, V. Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II. Energies 2023, 16, 6727. [Google Scholar] [CrossRef]

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Correction: Samnioti, A.; Gaganis, V. Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II. Energies 2023, 16, 6727

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