Energy Production Potential of Ultra-Deep Reservoirs in Keshen Gas Field, Tarim Basin: From the Perspective of Prediction of Effective Reservoir Rocks
The identification and prediction of effective reservoir rocks are important for evaluating the energy production potential of ultra-deep tight sandstone reservoirs. Taking the Keshen gas field, Tarim Basin, as an example, three distinct petrofacies are divided according to petrology, pores, and dia...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/11/2913 |
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| Summary: | The identification and prediction of effective reservoir rocks are important for evaluating the energy production potential of ultra-deep tight sandstone reservoirs. Taking the Keshen gas field, Tarim Basin, as an example, three distinct petrofacies are divided according to petrology, pores, and diagenesis. Petrofacies, well logs, and factor analysis are combined to predict effective reservoir rocks. We find that petrofacies A has a relatively coarse grain size, moderate mechanical compaction, diverse but low-abundance authigenic minerals, and well-developed primary and secondary pores. It is an effective reservoir rock. Petrofacies B and petrofacies C are tight sandstones with a poorly developed pore system and almost no dissolution. Petrofacies B features abundant compaction-susceptible ductile grains, intense mechanical compaction, and underdeveloped authigenic minerals, while petrofacies C features pervasive carbonate cementation with a poikilotopic texture. We combine well logging with gamma ray, acoustic, bulk density, neutron porosity, resistivity, and factor analyses to facilitate the development of petrofacies prediction models. The models reveal interbedded architecture where effective reservoir rocks are interbedded with tight sandstone, resulting in the restricted connectivity and pronounced reservoir heterogeneity. Classifying and combining well logs with a factor analysis to predict petrofacies provide an effective means for evaluating the energy potential of ultra-deep reservoirs. |
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| ISSN: | 1996-1073 |