Author Correction: Machine learning analysis of CO2 and methane adsorption in tight reservoir rocks

The Introduction, Data collection and specific description sections in the original version of the Article were incomplete.The use of data originally characterized by Tavakolian et al. (Reference 1) was not fully justified or acknowledged.For the avoidance of doubt about the original contribution of the Article, explanation of data sourcing and methodology have been updated, and additional context about the study's rationale has now been included.Consequently, in the Introduction section, "This study investigates the adsorption capacity of methane (CH 4 ) and CO 2 in tight reservoirs, specifically shale and coal, utilizing ML techniques.The research focuses on the application of ML algorithms to predict, evaluate, and optimize adsorption data for CH 4 and CO 2 .The dataset was compiled from previous studies conducted by researchers in the field of underground hydrocarbon storage.Given the complexities involved in predicting gas behavior in unconventional reservoirs, this study holds significant importance.Traditional prediction methods, such as mathematical models, numerical simulations, and laboratory measurements, are often constrained by oversimplifications, high costs, and time-intensive processes.Consequently, ML techniques emerge as a promising alternative, offering higher accuracy and reducing computational complexity." now reads.