The phase control modeling of porous carbonate reservoir by machine learning for the Cretaceous Mishrif Formation reservoir of H Oilfield in the Middle East

SHAO Guanming; QIAO Zhanfeng; YIN Nanxin; CAO Peng; SUN Xiaowei; ZHANG Yu

doi:10.16028/j.1009-2722.2022.221

2023 Vol. 39, No. 11

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Article navigation > Marine Geology Frontiers > 2023 > 39(11): 76-85

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SHAO Guanming, QIAO Zhanfeng, YIN Nanxin, CAO Peng, SUN Xiaowei, ZHANG Yu. The phase control modeling of porous carbonate reservoir by machine learning for the Cretaceous Mishrif Formation reservoir of H Oilfield in the Middle East[J]. Marine Geology Frontiers, 2023, 39(11): 76-85. doi: 10.16028/j.1009-2722.2022.221

Citation:

SHAO Guanming, QIAO Zhanfeng, YIN Nanxin, CAO Peng, SUN Xiaowei, ZHANG Yu. The phase control modeling of porous carbonate reservoir by machine learning for the Cretaceous Mishrif Formation reservoir of H Oilfield in the Middle East[J]. Marine Geology Frontiers, 2023, 39(11): 76-85. doi: 10.16028/j.1009-2722.2022.221

The phase control modeling of porous carbonate reservoir by machine learning for the Cretaceous Mishrif Formation reservoir of H Oilfield in the Middle East

1.
Hangzhou Research Institute of Geology, PetroChina, Hangzhou 310023, China
2.
Key Laboratory of Carbonate Reservoirs, CNPC, Hangzhou 310023, China
3.
School of Petroleumand Natural Gas Engineering, Chongqing University of Science and Technology, Chongqing 401331, China

Received Date: 12 August 2022

Publish Date: 28 November 2023

Abstract

Abstract

The chemical and mechanical sedimentation of porous carbonate reservoir blurs geometric shape and external structure of clastic reservoir sedimentary microfacies in space, and complicated the physical properties of reservoirs of different origins. Conventional sedimentary microfacies modeling methods are difficult to reproduce objectively the complex spatial distribution of different microfacies, thereby the accuracy of facies control attribute modeling could be reduced. Therefore, taking the Cretaceous Mishrif Formation bioclastic limestone in the Middle East H Oilfield as the research object, we established a three-dimensional sedimentary microfacies model in the study area by carrying out wave impedance inversion, porosity inversion, and permeability inversion, using machine learning methods. The phase-controlled attribute modeling was performed through the variogram analysis of different microfacies. The sedimentary microfacies model established using machine learning methods conforms to the changes in facies sequence of marine carbonate platforms, fully reflecting the spatial morphology of microfacies and the contact relationship among microfacies. The reservoir attribute model that established with the sedimentary microfacies as a constraint not only meets the requirement of probability consistency between simulation results and known data, but also reflects the spatial variation characteristics of the reservoir by phase zones.
- Mishrif Formation /
- carbonate reservoir /
- sedimentary microfacies /
- machine learning /
- facies-controlled modeling

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References

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