Distinguishing oil and water layers in a cracked porous medium using pulsed neutron logging data based on Hudson's crack theory

Zhang, Xueang; Yang, Zhichao; Tang, Bo; Wang, Renbo; Xiong, Wei

doi:10.1093/gji/ggy065

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…During the drilling process, wellbore * Authors to whom any correspondence should be addressed. fractures affect the flow pattern of drilling fluid [3], the activity trajectory of the borehole [4], as well as the performance and depth of the reservoir [5]. However, wellbore fractures, if not recognized in time, may lead to the collapse of the borehole, which will impede oil and gas extraction, and increase the cost.…”

Section: Introductionmentioning

confidence: 99%

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Liu,

Luo,

et al. 2024

Smart Mater. Struct.

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Real-time monitoring of wellbore status information can effectively ensure the structural safety of the wellbore and improve the drilling efficiency. It is especially important to recognize the wellbore fractures and identify their parameters, which motivates us to propose a wellbore fracture recognition and parameter identification method using piezoelectric ultrasonic and machine learning. To realize a Self-model Emission Detection (SED), we innovatively utilize a single transducer to act as both an actuator and a sensor, allowing for the efficient acquisition of ultrasonic echo signals of the wellbore. For fracture recognition, we use the wavelet packet transform to extract features from the ultrasonic echo signal, while constructing a Convolutional Neural Network (CNN) model for fracture recognition. Then, we establish the relationships between the fracture width-depth parameter and the echo signal, including the peak value as well as the arrival time difference. The experimental results show that the proposed method effectively recognizes the fractures from the ultrasonic echo signal of the wellbore. At the same time, the established function truly reflects the relationship between the fracture parameters and the echo signal. Therefore, the proposed method can provide an identification function for quantitative monitoring of wellbore fracture parameters. Moreover, the functions can be used as a reference for other structural health monitoring, which has good application prospects.

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Section: Introductionmentioning

confidence: 99%

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Liu,

Luo,

et al. 2024

Smart Mater. Struct.

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Propagation characteristics of acoustic waves in a borehole surrounded by a viscoelastic medium with horizontal cracks

Chongwang

Li²,

et al. 2021

Appl. Geophys.

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Simulation of multi-angle fractured uranium deposits by neutron logging

Zhang

Yang

2020

Geophysical Journal International

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Summary Uranium is an important energy material and a key exploration object in the field of radioactive geophysical exploration. Through field study, it was found that fractured uranium is one of the most abundant sources of uranium, therefore it is an important target of energy geophysical exploration. However, the fractured structure increases the difficulty of uranium exploration and analysis. Therefore, in order to identify the distribution and analyse the content of fractured uranium deposits, it is necessary to study the influence of fracture parameters on the occurrence state of uranium ore and its manifestation status in neutron logging response. Considering the fracture structure from different aspects, this study simulated the uranium ore occurrence state in the pore-fracture medium environment and obtained corresponding logging response results by setting two fracture parameters (number density and angle). The study generated several findings, namely: 1. high angle and high porosity fracture type uranium strata have the greatest influence on thermal neutrons; 2. under the condition of high porosity, the neutron energy spectrum displays a dispersion peak and moves towards the high energy region. It is noteworthy that in a high number density environment, medium-low angle uranium ore has the strongest thermal neutron absorption in a low energy range. 3. energy decay rate is the highest in the high angle fracture environment; and 4. the higher the density of the fracture number, the more susceptible the counting is to the influence of the angle factor. These findings can be used to quantitatively analyse the effects of fracture parameters on uranium deposits. These results have important guiding significance to the evaluation of a fractured uranium reservoir.

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Distinguishing oil and water layers in a cracked porous medium using pulsed neutron logging data based on Hudson's crack theory

Cited by 4 publications

References 21 publications

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Propagation characteristics of acoustic waves in a borehole surrounded by a viscoelastic medium with horizontal cracks

Simulation of multi-angle fractured uranium deposits by neutron logging

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