Vug and fracture characterization and gas production prediction by fractals: Carbonate reservoir of the Longwangmiao Formation in the Moxi-Gaoshiti area, Sichuan Basin

A comprehensive knowledge of the development and connectivity of fractures and vugs in carbonate reservoirs plays a key role in reservoir evaluation, ultimately affecting the gas prediction of this kind of heterogeneous reservoir. The carbonate reservoirs with fractures and vugs that are well developed in the Longwangmiao Formation, Sichuan Basin are selected as a research target, with the fractal dimension calculated from the full-bore formation microimager (FMI) image proposed to characterize the fractures and vugs. For this purpose, the multipoint statistics algorithm is first used to reconstruct a high-resolution FMI image of the full borehole wall. And then, the maximum class-variance method (the Otsu method) realizes the automatic threshold segmentation of the FMI image and acquisition of the binary image, which accurately characterizes the fractures and vugs. Finally, the fractal dimension is calculated by the box dimension algorithm, with its small value difference enlarged to obtain a new fractal parameter ([Formula: see text]). The fractal dimensions for four different kinds of reservoirs, including eight subdivided models of vugs and fractures, show that the fractal dimension can characterize the development and the connectivity of fractures and vugs comprehensively. That is, the more developed that the fractures and vugs are, the better the connectivity will be, and simultaneously the smaller that the values of the fractal dimensions are. The fractal dimension is first applied to the gas production prediction by means of constructing a new parameter ([Formula: see text]) defined as a multiple of the effective thickness ([Formula: see text]), porosity (Por), and fractal dimension ([Formula: see text]). The field examples illustrate that the fractal dimensions can effectively characterize the fractures and vugs in the heterogeneous carbonate reservoir and predict its gas production. In summary, the fractals expand the characterization method for the vugs and fractures in carbonate reservoirs and extend its new application in gas production prediction.

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Study on the improving method for gas production prediction in tight clastic reservoir

Arabian Journal of Geosciences ◽

10.1007/s12517-017-2840-4 ◽

2017 ◽

Vol 10 (3) ◽

Author(s):

Amir Maher Sayed Lala ◽

Hany Maher Sayed Lala

Keyword(s):

Gas Production ◽

Gas Production Prediction ◽

Clastic Reservoir ◽

Production Prediction

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Linear and nonlinear approach for DEM smoothening

Discrete Dynamics in Nature and Society ◽

10.1155/ddns/2006/63245 ◽

2006 ◽

Vol 2006 ◽

pp. 1-10

Author(s):

S. Dinesh ◽

P. Radhakrishnan

Keyword(s):

Fractal Dimension ◽

Fractal Dimensions ◽

Surface Generation ◽

Box Dimension ◽

Drainage Network ◽

Generation Process ◽

Nonlinear Approach ◽

Digital Elevation ◽

Gaussian Blurring ◽

Linear And Nonlinear

One of the biggest problems faced while analyzing digital elevation models (DEMs), particularly DEMs that are produced using photogrammetry, is to avoid pits and peaks in DEMs. Peaks and pits, which are errors, are generated during the surface generation process. DEM smoothening is an important preprocessing step meant for removing these errors. This paper discusses two linear DEM smoothening methods, Gaussian blurring and mean smoothening, and two nonlinear DEM smoothening methods, morphological smoothening and morphological smoothening by reconstruction. The four methods are implemented on a photogrammetrically generated DEM. The drainage network of the resultant DEM is obtained using skeletonization by morphological thinning, and the fractal dimension of the extracted network is computed using the box dimension method. The fractal dimensions are then compared to study the effects of the four smoothening methods. The advantages of nonlinear DEM smoothening over linear DEM smoothening are discussed. This study is useful in landscape descriptions.

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Classification of Void Space Types in Fractured-Vuggy Carbonate Reservoir Using Geophysical Logging: A Case Study on the Sinian Dengying Formation of the Sichuan Basin, Southwest China

Energies ◽

10.3390/en14165087 ◽

2021 ◽

Vol 14 (16) ◽

pp. 5087

Author(s):

Kunyu Wang ◽

Juan Teng ◽

Hucheng Deng ◽

Meiyan Fu ◽

Hongjiang Lu

Keyword(s):

Sichuan Basin ◽

Southwest China ◽

Carbonate Reservoir ◽

Carbonate Reservoirs ◽

Void Space ◽

Identification Method ◽

Geophysical Logging ◽

Dengying Formation ◽

Porosity And Permeability ◽

Core Description

The fractured-vuggy carbonate reservoirs display strong heterogeneity and need to be classified into different types for specific characterization. In this study, a total of 134 cores from six drilled wells and six outcrops of the Deng #2 and Deng #4 members of the Dengying Formation (Sichuan Basin, Southwest China) were selected to investigate the petrographic characteristics of void spaces in the fractured-vuggy carbonate reservoirs. Four void space types (VSTs) were observed, namely the solution-filling type (SFT), cement-reducing type (CRT), solution-filling breccia type (SFBT) and solution-enlarging fractures and vugs type (SEFVT). The CRT void spaces presented the largest porosity and permeability, followed by the SEFVT, SFBT and SFT. The VSTs presented various logging responses and values, and based on these, an identification method of VSTs using Bayes discriminant analysis (BDA) was proposed. Two test wells were employed for the validation of the identification method, and the results show that there is good agreement between the identification results and core description. The vertical distribution of VSTs indicates that the SFT and SEFVT are well distributed in both the Deng #2 and Deng #4 members. The CRT is mainly found in the Deng #2 member, and the SFBT occurs in the top and middle of the Deng #4 member.

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Box-Counting Dimension of Fractal Urban Form

International Journal of Artificial Life Research ◽

10.4018/jalr.2012070104 ◽

2012 ◽

Vol 3 (3) ◽

pp. 41-63 ◽

Cited By ~ 7

Author(s):

Shiguo Jiang ◽

Desheng Liu

Keyword(s):

Fractal Dimension ◽

Urban Form ◽

Fractal Dimensions ◽

Reliable Estimate ◽

Box Dimension ◽

Beijing City ◽

Box Counting ◽

Window Method ◽

Box Counting Dimension ◽

Data Points

The difficulty to obtain a stable estimate of fractal dimension for stochastic fractal (e.g., urban form) is an unsolved issue in fractal analysis. The widely used box-counting method has three main issues: 1) ambiguities in setting up a proper box cover of the object of interest; 2) problems of limited data points for box sizes; 3) difficulty in determining the scaling range. These issues lead to unreliable estimates of fractal dimensions for urban forms, and thus cast doubt on further analysis. This paper presents a detailed discussion of these issues in the case of Beijing City. The authors propose corresponding improved techniques with modified measurement design to address these issues: 1) rectangular grids and boxes setting up a proper box cover of the object; 2) pseudo-geometric sequence of box sizes providing adequate data points to study the properties of the dimension profile; 3) generalized sliding window method helping to determine the scaling range. The authors’ method is tested on a fractal image (the Vicsek prefractal) with known fractal dimension and then applied to real city data. The results show that a reliable estimate of box dimension for urban form can be obtained using their method.

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Production Prediction Based on the Heterogeneity Analysis of the Tight Carbonate Reservoir in the Sichuan Basin, China

10.2118/176091-ms ◽

2015 ◽

Author(s):

B. Xie ◽

Y. Wang ◽

X. Zhou ◽

Z. A. Zhao ◽

X. Zhou ◽

...

Keyword(s):

Sichuan Basin ◽

Carbonate Reservoir ◽

Tight Carbonate ◽

The Sichuan Basin ◽

Heterogeneity Analysis ◽

Production Prediction

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Full‐Scale Pore Structure and Fractal Dimension of the Longmaxi Shale from the Southern Sichuan Basin: Investigations Using FE‐SEM, Gas Adsorption and Mercury Intrusion Porosimetry

Minerals ◽

10.3390/min9090543 ◽

2019 ◽

Vol 9 (9) ◽

pp. 543 ◽

Cited By ~ 10

Author(s):

Wang ◽

Jiang ◽

Chang ◽

Zhu ◽

...

Keyword(s):

Organic Matter ◽

Fractal Dimension ◽

Pore Structure ◽

Clay Minerals ◽

Sichuan Basin ◽

Mercury Intrusion Porosimetry ◽

Fractal Dimensions ◽

Full Scale ◽

Mercury Intrusion ◽

Longmaxi Shale

Pore structure determines the gas occurrence and storage properties of gas shale and is a vital element for reservoir evaluation and shale gas resources assessment. Field emission scanning electron microscopy (FE‐SEM), high‐pressure mercury intrusion porosimetry (HMIP), and low‐pressure N2/CO2 adsorption were used to qualitatively and quantitatively characterize full‐scale pore structure of Longmaxi (LM) shale from the southern Sichuan Basin. Fractal dimension and its controlling factors were also discussed in our study. Longmaxi shale mainly developed organic matter (OM) pores, interparticle pores, intraparticle pores, and microfracture, of which the OM pores dominated the pore system. The pore diameters are mainly distributed in the ranges of 0.4–0.7 nm, 2–20 nm and 40–200 μm. Micro‐, meso‐ and macropores contribute 24%, 57% and 19% of the total pore volume (PV), respectively, and 64.5%, 34.6%, and 0.9% of the total specific surface area (SSA). Organic matter and clay minerals have a positive contribution to pore development. While high brittle mineral content can inhibit shale pore development. The fractal dimensions D1 and D2 which represents the roughness of the shale surface and irregularity of the space structure, respectively, are calculated based on N2 desorption data. The value of D1 is in the range of 2.6480–2.7334 (average of 2.6857), D2 is in the range of 2.8924–2.9439 (average of 2.9229), which indicates that Longmaxi shales have a rather irregular pore morphology as well as complex pore structure. Both PV and SSA positively correlated with fractal dimensions D1 and D2. The fractal dimension D1 decreases with increasing average pore diameter, while D2 is on the contrary. These results suggest that the small pores have a higher roughness surface, while the larger pores have a more complex spatial structure. The fractal dimensions of shale are jointly controlled by OM, clays and brittle minerals. The TOC content is the key factor which has a positive correlation with the fractal dimension. Clay minerals have a negative influence on fractal dimension D1, and positive influence D2, while brittle minerals show an opposite effect compared with clay minerals.

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