Prediction of Intraformational Remaining Oil Distribution Based on Reservoir Heterogeneity: Application to the J-Field
Autor(en): |
Jie Zhang
Feifei Fang Jie Wang Yajie Tian Fei Mo Qi Li Sainan Li Xiaoliang Huang Yi Yang |
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Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Advances in Civil Engineering, Januar 2021, v. 2021 |
Seite(n): | 1-10 |
DOI: | 10.1155/2021/8870274 |
Abstrakt: |
At high water cut stage, the study of remaining oil distribution in water-flooding reservoir is the basis of implementing potential-tapping measures and enhancing oil recovery. At present, most of the oilfields in China have entered the stage of ultrahigh water cut. The reserves of the oilfields are highly developed, the situation of water flooding is extremely complex, and it is difficult to predict the distribution of the remaining oil, which seriously restricts the adjustment of the production measures, tapping the potential and improving the ultimate recovery rate. In view of aforementioned difficulties, this study puts forward a research approach to predict remaining oil distribution based on reservoir heterogeneity, which can quantitatively characterize reservoir heterogeneity. In order to avoid the drawback that a single parameter cannot fully describe the characteristics of pore structure, the composite index of pore structure (SQRT(K/Φ)) is introduced to study the pore microstructure. The composite index of pore structure is used to predict the distribution of remaining oil in the formation, and the results are basically consistent with those calculated by numerical simulation. It is concluded that the larger the fractal dimension of the composite index of pore structure is, the stronger the heterogeneity of reservoir is; the smaller the composite index of pore structure is, the smaller the recovery degree is. The composite index of pore structure is used to analyze and predict the distribution of remaining oil in the layer, which provides a new direction for the prediction method of remaining oil. |
Copyright: | © 2021 Jie Zhang et al. |
Lizenz: | Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden. |
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