Due to the complexity of lithologies and pore types, the permeability calculation of complex carbonate reservoirs has always been a difficult problem. To accurately calculate the permeability of complex carbonate reservoirs, a data mining technique is introduced. The technical process of data mining is established and divided into seven steps: data warehousing, data preprocessing, classification of reservoir types, selection of sensitive parameters, establishment of the classification model, evaluation of classification model, and application of classification model. The data-driven method can find effective knowledge that conventional reservoir evaluation methods cannot recognize and that are still contained in oil and gas data. Since the data-driven method may acquire a large amount of invalid knowledge while obtaining effective knowledge, the domain knowledge needs to be introduced to participate in the data mining process. The domain-knowledge-driven method can extract the most valuable and effective information from oil and gas data. The combination of data-driven and domain knowledge-driven methods is possible to avoid subdividing lithologies and pore types of complex carbonate reservoirs. As a result, the permeability of complex carbonate reservoirs can be accurately calculated based on the combination of data-driven and domain-knowledge-driven methods. Compared with the permeability calculation result by the previous method, the accuracy of the permeability calculation result by the data mining technique is improved by 18.39%. The combination of data-driven and domain-knowledge- driven methods can solve the difficult problem that traditional reservoir evaluation methods cannot overcome. Additionally, they can also provide new theories and techniques for reservoir evaluation. The permeability calculation result proves the feasibility and correctness of the method.

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