A Novel Fractal Model for Estimating Permeability in Low-Permeable Sandstone Reservoirs

Dong, Shuning; Xu, Lulu; Dai, Zhenxue; Bin, Xu; Yu, Qingxi; Yin, Shangxian; Zhang, Xiaoxin; Zhang, Changsong; Zang, Xueke; Zhou, X. J.; Zhang, Zhien

doi:10.1142/s0218348x20400058

Cited by 15 publications

(6 citation statements)

References 42 publications

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“…On the basis of the assumption that tight sandstones with natural microfractures can be represented by a bundle of tortuous capillary tubes and parallel plate fractures, Wang and Cheng [35] proposed a fractal stress-sensitive permeability model of tight sandstones considering the effect of tortuosity, and they compared their model with the permeability expression derived from classic theory. Dong et al [36] developed a permeability model by considering the microstructural parameters and tortuosity effects of low-permeability sandstone using fractal geometry theory. All of the above studies presented a method for calculating the permeability of tight oil reservoirs, with each only partially considering the factors and not their combined effect.…”

Section: Introductionmentioning

confidence: 99%

A Fractal Permeability Model of Tight Oil Reservoirs Considering the Effects of Multiple Factors

Cui

Yang

et al. 2022

Fractal Fract

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The prediction of permeability and the evaluation of tight oil reservoirs are very important to extract tight oil resources. Tight oil reservoirs contain enormous micro/nanopores, in which the fluid flow exhibits micro/nanoscale flow and has a slip length. Furthermore, the porous size distribution (PSD), stress sensitivity, irreducible water, and pore wall effect must also be taken into consideration when conducting the prediction and evaluation of tight oil permeability. Currently, few studies on the permeability model of tight oil reservoirs have simultaneously taken the above factors into consideration, resulting in low reliability of the published models. To fill this gap, a fractal permeability model of tight oil reservoirs based on fractal geometry theory, the Hagen–Poiseuille equation (H–P equation), and Darcy’s formula is proposed. Many factors, including the slip length, PSD, stress sensitivity, irreducible water, and pore wall effect, were coupled into the proposed model, which was verified through comparison with published experiments and models, and a sensitivity analysis is presented. From the work, it can be concluded that a decrease in the porous fractal dimension indicates an increase in the number of small pores, thus decreasing the permeability. Similarly, a large tortuous fractal dimension represents a complex flow channel, which results in a decrease in permeability. A decrease in irreducible water or an increase in slip length results in an increase in flow space, which increases permeability. The permeability decreases with an increase in effective stress; moreover, when the mechanical properties of rock (elastic modulus and Poisson’s ratio) increase, the decreasing rate of permeability with effective stress is reduced.

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Section: Introductionmentioning

confidence: 99%

A Fractal Permeability Model of Tight Oil Reservoirs Considering the Effects of Multiple Factors

Cui

Yang

et al. 2022

Fractal Fract

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“…Numerous correlations related to porosity estimations in carbonate reservoirs are available throughout published literature. These correlations span from incorporating basic well log data (Anifowose et al, 2015(Anifowose et al, , 2014, rock and pore throat characterization (Amaefule et al, 1993;Guo et al, 2007;Herron, 1986;Lucia, 1995;Puskarczyk et al, 2018), to advanced theories related to percolation and statistical fractal approach (Babadagli and Al-Salmi, 2004;de Assis and Moraes, 2019;dong et al, 2020;Ge et al, 2016;Pape et al, 1999;Sadeghnejad et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

An Effective Method of Estimating Nuclear Magnetic Resonance Based Porosity Using Deep Learning Approach

Tariq

Gudala

et al. 2022

Day 3 Wed, November 02, 2022

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Carbonate rocks are very heterogeneous and have very complex pores structure due to the presence of intra-particle and inter-particle porosities. This makes the characterization and evaluation of the petrophysical data, and the interpretation of the carbonate rocks a big challenge. Porosity in complex lithologies, particularly carbonate reservoirs, is difficult to measure using conventional (Quad-Combo) well logs. Nuclear Magnetic Resonance (NMR) derived porosity is considered the total porosity "gold standard", as it is measured exclusive of matrix and mineralogy. However, due to NMR tools existing as relatively new technology, and the extra expense in logging runs and rig time, most wells lack these data. Most of the existing approaches to predict the rock porosity was developed on the Neutron-density porosity logs that usually are resulted in inaccurate estimation, especially in the fractured zone and highly dolomitized rocks. In this study, deep learning model was efficiently utilized to predict the Nuclear Magnetic Resonance based effective porosity in carbonate rocks. The petrophysical well logs such as bulk density, gamma-ray, neutron porosity, photoelectric log, and caliper log were used as predictors. A total of 3800 data points were obtained from several wells located in a carbonate reservoir. A comprehensive data exploratory analysis tools (EDA) was utilized to evaluate the quality of the dataset which led to removing the extreme values and outliers. A fully connected Deep Neural Network (DNN) was trained to predict NMR based effective porosity. The hyperparameters of DNN model such as number of hidden layers, number of neurons, activation functions, and learning algorithms were varied using a grid search optimization approach. The K-fold cross-validation criteria were used to enhance the generalization capabilities of ML models. The evaluation of ML models was assessed by the coefficient of determination (R2), root means square error (RMSE), and. average absolute percentage error (AAPE). The results showed that the DNN resulted in a significantly low error and high R2 between actual and predicted values. An accuracy of 87% was recorded between actual and predicted NMR values. The new model to predict the NMR porosity is trained on the NMR-determined porosity. NMR porosity is based on the number of hydrogen nuclei in the pore spaces that are independent of the rock minerals and related to the pore spaces only.

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“…Accordingly, many fractal-based expressions have been driven for transport analysis of the reservoir and to describe its petrophysical, fluid flow, and hydraulic properties. A limited number of these studies have also revised/improved the fractal-based models of, e.g., absolute and relative permeability, flow, heterogeneity, connectivity, tortuosity, and pore throat morphology under the impact of the swelling clays [15][16][17]. Swelling reduces the porosity and questions the reliability of those models that assume a constant unchanging pore network with a single value of the fractal dimensions at all length-time scales.…”

Section: Introductionmentioning

confidence: 99%

Fractal Properties of Various Clay Minerals Obtained from SEM Images

et al. 2021

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Clay minerals significantly alter the pore size distribution (PSD) of the gas hydrate-bearing sediments and sandstone reservoir rock by adding an intense amount of micropores to the existing intragranular pore space. Therefore, in the present study, the internal pore space of various clay groups is investigated by manually segmenting Scanning Electron Microscopy (SEM) images. We focused on kaolinite, smectite, chlorite, and dissolution holes and characterized their specific pore space using fractal geometry theory and parameters such as pore count, pore size distribution, area, perimeter, circularity, and density. Herein, the fractal properties of different clay groups and dissolution holes were extracted using the box counting technique and were introduced for each group. It was observed that the presence of clays complicates the original PSD of the reservoir by adding about 1.31-61.30 pores/100 μm2 with sizes in the range of 0.003-87.69 μm2. Meanwhile, dissolution holes complicate the pore space by adding 4.88-8.17 extra pores/100 μm2 with sizes in the range of 0.06-119.75 μm2. The fractal dimension ( D ) and lacunarity ( L ) values of the clays’ internal pore structure fell in the ranges of 1.51-1.85 and 0.18-0.99, respectively. Likewise, D and L of the dissolution holes were in the ranges of, respectively, 1.63-1.65 and 0.56-0.62. The obtained results of the present study lay the foundation for developing improved fractal models of the reservoir properties which would help to better understand the fluid flow, irreducible fluid saturation, and capillary pressure. These issues are of significant importance for reservoir quality and calculating the accurate amount of producible oil and gas.

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A Novel Fractal Model for Estimating Permeability in Low-Permeable Sandstone Reservoirs

Cited by 15 publications

References 42 publications

A Fractal Permeability Model of Tight Oil Reservoirs Considering the Effects of Multiple Factors

A Fractal Permeability Model of Tight Oil Reservoirs Considering the Effects of Multiple Factors

An Effective Method of Estimating Nuclear Magnetic Resonance Based Porosity Using Deep Learning Approach

Fractal Properties of Various Clay Minerals Obtained from SEM Images

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