Potential hydrocarbon zone identification: a case study

Hasan, Mahmud; Akter, Sigma; Islam, K.M. Mesbah-Ul; Tanjil, Hossain Al

doi:10.1007/s12517-019-4383-3

Cited by 2 publications

(1 citation statement)

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“…2.1 Fluid feature extraction 2.1.1 Well log and rock physics parameters Tight reservoir evaluation is the practice of interpreting wellbore and well log data to detect and quantify lithology and fluid types [41]. The input parameters of fluid identification can be divided into two main categories: (1) Well log parameters include borehole diameter (CAL), gamma ray (GR), transverse velocity (S-wave velocity, SV), longitudinal velocity (P-wave velocity, PV), shallow lateral resistivity (LLS), compensated neutron log (CN), deep lateral resistivity (LLD), and acoustic log (AC), i.e., 𝐶 1 = {CAL, GR, SV, PV, LLS, CN, LLD, AC}; (2) Rock physics parameters are sensitive to reservoir fluids [42], and include Poisson's ratio ν, bulk modulus K, shear modulus μ, Young's modulus E, Lamé coefficient λ, and longitudinal-to-transverse velocity ratio 𝑉 𝑝 /𝑉 𝑠 (Eqs.…”

Section: Methods and Model Training/methodologymentioning

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: Methods and Model Training/methodologymentioning

confidence: 99%