Permeability and porosity prediction using logging data in a heterogeneous dolomite reservoir: An integrated approach

Zhang, Zhao; Zhang, Heng; Li, Jie; Cai, Zhongxian

doi:10.1016/j.jngse.2020.103743

Cited by 53 publications

(16 citation statements)

References 31 publications

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“…FZI and FZI* are two commonly used rock-typing indices to improve the identification of hydraulic flow units (HFU) and the evaluation of permeability. ,, In this study, these two methods are applied to the data set of Weger et al expecting to establish the permeability–porosity binary model based on rock typing. As suggested by Zhang et al, the FZI and FZI* values of the samples are calculated first using eqs and according to the measured core permeabilities and porosities. …”

Section: Results and Discussionmentioning

confidence: 99%

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Permeability Prediction of Carbonate Rocks Based on Digital Image Analysis and Rock Typing Using Random Forest Algorithm

Zhang

Cai

2021

Energy Fuels

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The diversity and multiscale characteristics of pore types in carbonate rocks usually result in extremely complex permeability–porosity relationships. Clarifying the main controlling factors of permeability and their response mechanisms is essential for improving permeability prediction. In this study, a digital image analysis (DIA) framework was developed to reveal the variation trend of permeability with pore structure parameters, and a rock-typing method consisting of flow zone indicator (FZI) and discrete rock type clustering technology (DRT) was applied to the carbonate samples to establish the binary permeability–porosity model. Results showed that permeability was approximately positively correlated with pore equivalent diameter, dominant pore size, and γ and negatively correlated with perimeter over area and fractal dimension. The commonly used multivariate linear regression (MLR) method failed to describe the response relationship between permeability and pore structure parameters. However, different discrete rock types exhibited special permeability–porosity relationships. Analysis showed that different discrete rock types were mainly controlled by the product of shape factor and the square of tortuosity. Further, three permeability prediction schemes were designed using the BP neural network and random forest algorithm based on the pore structure parameters derived from the thin sections. Compared with the direct prediction models trained by the optimized BP neural network and random forest algorithm, the indirect permeability prediction method based on FZI prediction showed better generalization ability with the highest R 2 of 0.936, demonstrating that the combination of digital image analysis, rock typing, and random forest algorithm is a robust and reliable method to realize the permeability prediction of carbonate rocks based on thin-section images.

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Section: Results and Discussionmentioning

confidence: 99%

“…The porosities of the core plugs were measured with a QK-98 gas porosimeter, and the permeabilities were measured with a GDS-90F gas permeameter using the GB/T 29172-2012 method . The porosity values range from 0.41% to 11.43% and permeability from 2.92 × 10 –3 to 401mD.…”

Section: Data Set and Methodsmentioning

confidence: 99%

Permeability Prediction of Carbonate Rocks Based on Digital Image Analysis and Rock Typing Using Random Forest Algorithm

Zhang

Cai

2021

Energy Fuels

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“…Due to their powerful capability to map correlation among data and find solution for different problems, ML models' application has become much popular in various fields of science and engineering over the last few decades (Choubin et al, 2019;Ghalandari et al, 2019;Qasem et al, 2019;Torabi et al, 2019;Ahmadi et al, 2020;Band et al, 2020;Mosavi et al, 2020;Shabani et al, 2020;H Ghorbani and Davarpanah, 2021). For instance, ML methods have been applied for tackling a variety of challenges in petroleum engineering such as petrophysical (Rajabi et al, 2022c;Jafarizadeh et al, 2022;Tabasi et al, 2022;Zhang et al, 2022), reservoir characterization (Hassanpouryouzband et al, 2020;Abad et al, 2021a;Hassanpouryouzband et al, 2021;Zhang et al, 2021;Kamali et al, 2022;Kamali et al, 2022;Rajabi et al, 2022d;Hassanpouryouzband et al, 2022;Ibrahim et al, 2022;Zhang et al, 2022), production (Mirzaei-Paiaman andSalavati, 2012;Ghorbani et al, 2020;Abad et al, 2021b) drilling (Soares and Gray, 2019;Syah et al, 2021;Beheshtian et al, 2022;Pang et al, FIGURE 5 Flowchart for LSSVM-GA/PSO models used for prediction of fracture density. 2022).…”

Section: Methodsmentioning

confidence: 99%

Prediction of fracture density in a gas reservoir using robust computational approaches

Gao

Hazbeh²,

Davoodi³

et al. 2023

Front. Earth Sci.

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One of the challenges that reservoir engineers, drilling engineers, and geoscientists face in the oil and gas industry is determining the fracture density (FVDC) of reservoir rock. This critical parameter is valuable because its presence in oil and gas reservoirs boosts productivity and is pivotal for reservoir management, operation, and ultimately energy management. This valuable parameter is determined by some expensive operations such as FMI logs and core analysis techniques. As a result, this paper attempts to predict this important parameter using petrophysics logs routinely collected at oil and gas wells and by applying four robust computational algorithms and artificial intelligence hybrids. A total of 6067 data points were collected from three gas wells (#W1, #W2, and #W3) in one gas reservoir in Southwest Asia. Following feature selection, the input variables include spectral gamma ray (SGR); sonic porosity (PHIS); potassium (POTA); photoelectric absorption factor (PEF); neutron porosity (NPHI); sonic transition time (DT); bulk density (RHOB); and corrected gamma ray (CGR). In this study, four hybrids of two networks were used, including least squares support vector machine (LSSVM) and multi-layer perceptron (MLP) with two optimizers particle swarm optimizer (PSO) and genetic algorithm (GA). Four robust hybrid machine learning models were applied, and these are LSSVM-PSO/GA and MLP-PSO/GA, which had not previously used for prediction of FVDC. In addition, the k-fold cross validation method with k equal to 8 was used in this article. When the performance accuracy of the hybrid algorithms for the FVDC prediction is compared, the revealed result is LSSVM-PSO > LSSVM-GA > MLP-PSO > MLP-GA. The study revealed that the best algorithm for predicting FVDC among the four algorithms is LSSVM-PSO (for total dataset RMSE = 0.0463 1/m; R2 = 0.9995). This algorithm has several advantages, including: 1) lower adjustment parameters, 2) high search efficiency, 3) fast convergence speed, 4) increased global search capability, and 5) preventing the local optimum from falling. When compared to other models, this model has the lowest error.

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“…Recent studies have shown that rock typing methods originated from traditional petrophysical models are very practical tools for hydraulic and electrical flow unit (HFU and EFU) identification, representative sample selection, permeability prediction, cementation exponent estimation, and fluid saturation determination. ,− Mirzaei-Paiaman et al and Kolah-Kaj et al gave a good summary of different rock typing indices. Different from the rock textural classifications of Dunham and the pore type classifications of Choquette and Pray, rock typing is to classify rocks according to special microstructure parameters or petrophysical properties.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Hydraulic and Electrical Transport Properties of Carbonate Rocks Based on Rock Typing

et al. 2023

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The permeability (k) and electrical resistivity of carbonate rocks with similar porosity (ϕ) may exhibit multiple orders of magnitude differences due to the diversity of pore types and the multiscale characteristics of pore size. As a result, traditional permeability-porosity and resistivity-porosity models usually have strong limitations when applied to carbonate reservoirs, let alone evaluate permeability from electrical resistivity. In this study, a new rock typing parameter named relative transport quality index (RTQI) is proposed to categorize rock samples using permeability and formation resistivity factor (F). Samples with similar RTQI values tend to have similar relative capacities of hydraulic and electrical transport. Subsequently, the classification performance and microscopic differences between RTQI and other rock typing indices including flow zone indicator (FZI), current zone indicator (CZI), and electrical quality index (EQI) were compared and discussed based on the reported petrophysical data of 316 carbonate rock samples. The results indicate that the RTQI method significantly reduces the scattering of k and F by dividing the samples into distinct groups, and each RTQI group corresponds to a special k−F correlation with a high determination coefficient. However, the number of rock types identified by different rock typing indices is different, meaning that samples belonging to the same rock type in the k−F plot of the RTQI method may be classified into different rock types in the k−ϕ plot of the FZI method and the F−ϕ plots of the CZI and EQI methods, which may be caused by the microscopic differences of rock typing parameters. Different from the strong dependence of FZI on hydraulic tortuosity and CZI/EQI on electrical tortuosity, RTQI is mainly dominated by the ratio of electrical tortuosity to hydraulic tortuosity. Additionally, in view of the good correlation between petrophysical data after rock typing, two workflows are proposed to estimate the hydraulic and electrical parameters of carbonate rocks from log data.

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Permeability and porosity prediction using logging data in a heterogeneous dolomite reservoir: An integrated approach

Cited by 53 publications

References 31 publications

Permeability Prediction of Carbonate Rocks Based on Digital Image Analysis and Rock Typing Using Random Forest Algorithm

Permeability Prediction of Carbonate Rocks Based on Digital Image Analysis and Rock Typing Using Random Forest Algorithm

Prediction of fracture density in a gas reservoir using robust computational approaches

Comparative Study on Hydraulic and Electrical Transport Properties of Carbonate Rocks Based on Rock Typing

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