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Pore structure impacts the capability of seepage pattern of subsurface fluid mineral and mineral exploitation efficiency. Because of the strong heterogeneity in carbonate reservoirs, the pore structure is nonlinearly varying and complex in reservoirs. It is necessary to establish a method for pore structure type (PST) prediction. Machine learning provides an efficient prediction method by finding the relationship between core test data and well‐logging data. In this paper, a reservoir identification method based on Gradient Boost Decision Trees (GBDT) is proposed for the pore structure characteristics of carbonate reservoirs integrating core test and logging data. Core testing data are utilized to form the training set for PST prediction. Then, we adopt the mutual information method to optimize the logging data as the input of the machine learning model. GBDT hyperparameters are optimized using the learning curve to make it have optimal performance. Finally, we compare the predicted result of GBDT with K‐Nearest Neighbor and support vector machines, demonstrating the superior performance of GBDT. The results indicate that the four PS are different in some properties, such as permeability, pore radius and shape of pore and throat. 20%, 10 and 0.15 are recommended as the optimal parameter values for Maximum feature, iteration number and Sub‐sample number to improve GBDT accuracy in carbonate reservoir PST prediction. Evaluation indicators of machine learning, including the Kappa coefficient, Heming distance, Jaccard similarity coefficient and Confusion matrices, indicate that GBDT has better performance. This research proves the high accuracy and applicability of machine learning technology in the carbonate reservoir and also provides a meaningful reference for the identification of the carbonate reservoirs in the central area of Iraq. [ABSTRACT FROM AUTHOR]

Due to the diversity of pore types, it is challenging to characterize the Middle East's Cretaceous carbonate reservoir or accurately predict its petrophysical properties. In this paper, pore structure in the reservoir is first classified using a comprehensive method. Then, based on the identified pore structure types, a new permeability model with high prediction precision is established. The reservoir is dominated by 6 pore types, such as intergrain pores and moldic pores, and 6 rock types. Grainstone, algal packstone, algal wackestone, and foraminifera wackestone are porous rock types, and echinoderm wackestone and mudstone are nonporous rock types. The types of pore structure in the study area can be divided into four types. Type I has midhigh porosity and medium-high permeability due to its large throat, while type II has a fine throat type with midhigh porosity and midpermeability. Due to their isolated pores, the permeability is low in types III and IV, and out of these two, type III has better storage capacity. Movable fluid saturation calculated by the spectral coefficient method and r apex can characterize the boundary between the connected pores and unconnected pores very well in the research area. It is not accurate enough to simply classify the pore structure by permeability and porosity. The combination of porosity, permeability, r apex , flow zone indicator, and the reservoir quality index can effectively distinguish and classify pore structure types in noncoring wells. The characteristics of each pore structure type are consistent with those of the fractal dimension, which thereby proves the effectiveness of the pore structure classification. New permeability prediction models are proposed for different pore structure types, and good prediction results have been obtained. This study is of great significance for enhancing oil recovery. [ABSTRACT FROM AUTHOR]