Characterization and Evaluation of Carbonate Reservoir Pore Structure Based on Machine Learning

Jue Hou, Lun Zhao, Xing Zeng, Wenqi Zhao, Yefei Chen, Jianxin Li, Shuqin Wang, Jincai Wang and Heng Song
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Jue Hou: Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Lun Zhao: Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Xing Zeng: Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Wenqi Zhao: Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Yefei Chen: Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Jianxin Li: Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Shuqin Wang: Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Jincai Wang: Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Heng Song: Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China

Energies, 2022, vol. 15, issue 19, 1-18

Abstract: The carboniferous carbonate reservoirs in the North Truva Oilfield have undergone complex sedimentation, diagenesis and tectonic transformation. Various reservoir spaces of pores, caves and fractures, with strong reservoir heterogeneity and diverse pore structures, have been developed. As a result, a quantitative description of the pore structure is difficult, and the accuracy of logging identification and prediction is low. These pose a lot of challenges to reservoir classification and evaluation as well as efficient development of the reservoirs. This study is based on the analysis of core, thin section, scanning electron microscope, high-pressure mercury injection and other data. Six types of petrophysical facies, PG1, PG2, PG3, PG4, PG5, and PG6, were divided according to the displacement pressure, mercury removal efficiency, and median pore-throat radius isobaric mercury parameters, combined with the shape of the capillary pressure curve. The petrophysical facies of the wells with mercury injection data were divided accordingly, and then the machine learning method was applied. The petrophysical facies division results of two mercury injection wells were used as training samples. The artificial neural network (ANN) method was applied to establish a training model of petrophysical facies recognition. Subsequently, the prediction for the petrophysical facies of each well in the oilfield was carried out, and the petrophysical facies division results of other mercury injection wells were applied to verify the prediction. The results show that the overall coincidence rate for identifying petrophysical facies is as high as 89.3%, which can be used for high-precision identification and prediction of petrophysical facies in non-coring wells.

Keywords: carbonate; pore structure; machine learning; reservoir; petrophysics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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