Probability distribution method based on the triple porosity model to identify the fluid properties of the volcanic reservoir in the Wangfu fault depression by well log

Zhang, Lihua; Pan, Baozhi; Gang-yi, Shan; Liu, Sihui; Guo, Yuhang; Chun-hui, Fang

doi:10.1007/s10596-016-9608-x

Cited by 4 publications

(1 citation statement)

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“…Datasets for fluid identification consist of seismic data [13,14] and well log data [12], and the latter enable the effective study of the lithology and physical properties of reservoirs and the identification of their fluid properties [15,16]. The fluid response of tight reservoirs is not obvious, because well log data are affected by various factors, and the fluid properties of reservoirs cannot be accurately identified using only the data of single logs or reservoir parameters.…”

Section: Introductionmentioning

confidence: 99%

Hybrid parameters for fluid identification using an improved quantum neural network in a tight reservoir

Luo

Liu

Yang

et al. 2023

Preprint

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This paper proposes a fluid classifier for a tight reservoir using a quantum neural network (QNN). It is difficult to identify the fluid in tight reservoirs, and the manual interpretation of logging data, which is an important means to identify the fluid properties, has the disadvantages of a low recognition rate and non-intelligence, and an intelligent algorithm can better identify the fluid. For tight reservoirs, the logging response characteristics of different fluid properties and the sensitivity and relevance of well log parameter and rock physics parameters to fluid identification are analyzed, and different sets of input parameters for fluid identification are constructed. On the basis of quantum neural networks, a new method for combining sample quantum state descriptions, sensitivity analysis of input parameters, and wavelet activation functions for optimization is proposed. The results of identifying the dry layer, gas layer, and gas-water co-layer in the tight reservoir in the Sichuan Basin of China show that different input parameters and activation functions affect recognition performance. The proposed quantum neural network based on hybrid parameters and a wavelet activation function has higher fluid identification accuracy than the original quantum neural network model, indicating that this method is effective and warrants promotion and application.

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Section: Introductionmentioning

confidence: 99%

Hybrid parameters for fluid identification using an improved quantum neural network in a tight reservoir

Luo

Liu

Yang

et al. 2023

Preprint

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Multi-parameter logging evaluation of tight sandstone reservoir based on petrophysical experiment

Deng

et al. 2021

Acta Geophys.

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Pore structure characterization based on joint numerical and experimental study: a case study of Nanpu Sag

Deng

Yuan

et al. 2019

J Petrol Explor Prod Technol

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Petrophysical properties such as acoustic velocity, resistivity and nuclear magnetic resonance are greatly depending on the pore structures. This is because the pore structures could control the elastic modulus, conductive path and seepage distribution of the rock. The theoretical simulation and acoustic-electrical-nuclear magnetic resonance joint experiments were used to study the pore structure control action of acoustic, electrical and nuclear magnetic characteristics by taking the sandstone samples of Nanpu Sag as an example which the pore structure of the samples was classified based on the mercury intrusion fractal curve. It is found that the control action of pore structure type on the variation law of longitudinal and transverse wave velocity is mainly affected by the aspect ratio of pores; the effect of pore structure type on resistivity change is mainly affected by pore-throat ratio and tortuosity; the influence of pore structure on nuclear magnetic changes is mainly affected by pore size factors.

show abstract

Probability distribution method based on the triple porosity model to identify the fluid properties of the volcanic reservoir in the Wangfu fault depression by well log

Cited by 4 publications

References 0 publications

Hybrid parameters for fluid identification using an improved quantum neural network in a tight reservoir

Hybrid parameters for fluid identification using an improved quantum neural network in a tight reservoir

Multi-parameter logging evaluation of tight sandstone reservoir based on petrophysical experiment

Pore structure characterization based on joint numerical and experimental study: a case study of Nanpu Sag

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