Study on the Applicability of Reservoir Fractal Characterization in Middle–High Rank Coals with NMR: Implications for Pore-Fracture Structure Evolution within the Coalification Process

Abstract

In order to evaluate the applicability of the pore-fracture structure fractal characterizations in coal reservoirs and confirm the internal relationships between the porosity, permeability, coal metamorphic grade, and pore-fracture structure, the pore-fracture features of 21 middle–high rank coal samples from Anhe, Jiaozuo, and Huaibei coalfields in northern China were investigated using a low-field nuclear magnetic resonance (NMR). All the coal samples are characterized by low moisture content (M ad), low and medium ash yield (A ad), and high vitrinite (V) in coal maceral. The adsorption space fractal dimension (D A) is positively correlated with the Langmuir volume (V L) under the three-peak transverse relaxation time (T 2) spectrum. The fractal dimension of all effective T 2 points under saturated water (D NMR) is positively correlated with V L and the adsorption pore volume, but negatively correlated with the volume ratio of seepage pores and fractures. The free flow space fractal dimension (D M) is negatively correlated with the porosity of full saturated water (ΦF) and the porosity of movable water (ΦM). There is a negative correlation between ΦF and the seepage space fractal dimension (D S) in the coal samples with one-peak and two-peak T2 spectra, but a positive correlation can be found with the three-peak T2 spectrum. Therefore, it is necessary to consider the types of T2 spectral peak as a prerequisite to analyze the correlations between pore-fracture parameters and NMR fractal dimensions. With the increase of coal rank, the adsorption pore content, ΦF, and bulk volume immovable (BVI) fraction first increase and then decrease, whereas the seepage pore content, fracture development, bulk volume movable (BVM) fraction, and BVM/BVI first decrease and then increase. The inflection points of these changes correspond to the maximum vitrinite reflectance (R o,max) at 2.6–2.8%, which would be attributed to the third coalification jump. Generally, D A is the fractal dimension representing the coal pore surface, and D S and D M are closely related to the pore structure. Furthermore, D NMR not only represents the roughness of the pore surface but also the complexity of the pore structure.

Investigation on Adsorption Pore and Fractal Analyses of Low-Rank Coals in the Northern Qaidam Basin

“…15−17 The calculation methods include the Brunauer−Emmett−Teller (BET) model, Langmuir model, FHH model, thermodynamic model, and Menger sponge model. 18,19 However, due to the existence of high heterogeneity, several scholars have reached different conclusions on the classification of coal pore types and the characterization of fractal dimensions, especially the adsorption and permeability responses of fractal dimensions under different coal rank conditions. 13,14 The quantitative characterization techniques and analysis methods of pore structure in coal are mainly divided into the fluid injection method, the nonfluid injection method, and the image analysis method, including CO 2 adsorption, low-temperature N 2 adsorption, the mercury injection method, nuclear magnetic resonance, CT scanning, optical microscopy, scanning electron microscopy (SEM), transmission electron microscopy, X-ray diffraction, and atomic force microscopy.…”

“…From the perspective of CBM exploration, previous studies have been focused on areas such as coal gathering characteristics, CBM resource evaluation, and CBM formation condition. − Fractal dimension has gradually become known in recent years as a quantitative parameter to characterize the heterogeneity of pore structure . It has been proven that the porous media in coal reservoirs have obvious fractal characteristics at different research scales. − At present, the widely used fractal characterization methods of coal reservoir pores are based on CO 2 adsorption, low-temperature N 2 adsorption, mercury injection method, and nuclear magnetic resonance experiments. − The calculation methods include the Brunauer–Emmett–Teller (BET) model, Langmuir model, FHH model, thermodynamic model, and Menger sponge model. , However, due to the existence of high heterogeneity, several scholars have reached different conclusions on the classification of coal pore types and the characterization of fractal dimensions, especially the adsorption and permeability responses of fractal dimensions under different coal rank conditions. , The quantitative characterization techniques and analysis methods of pore structure in coal are mainly divided into the fluid injection method, the nonfluid injection method, and the image analysis method, including CO 2 adsorption, low-temperature N 2 adsorption, the mercury injection method, nuclear magnetic resonance, CT scanning, optical microscopy, scanning electron microscopy (SEM), transmission electron microscopy, X-ray diffraction, and atomic force microscopy. − The pore structure of coal is characterized by pore surface area, pore volume, pore distribution, average pore size, pore morphology, pore connectivity, and fractal dimension. , A part of studies usually focuses on the pore fractal characteristics within relatively narrow coal-grade ranges in the same region. However, there is little attention on whether the research results from a narrow coal-grade range are in agreement with those from a large coalification range.…”

Prediction of Total Organic Carbon in Organic-Rich Shale Rocks Using Thermal Neutron Parameters

“…13,15,19,33 New logging techniques such as nuclear magnetic resonance and lithology scanning tools offer developments in TOC quantification accuracy. 34 However, they incur prohibitively high costs and cannot be used conventionally, hence requiring new robust and more convenient methods for TOC quantification. 7 Artificial intelligence (AI) has been considerably used in the last few years in oil and gas research, and much work has been made on the prediction of TOC based on core and well log data.…”

“…New logging techniques such as nuclear magnetic resonance and lithology scanning tools offer developments in TOC quantification accuracy . However, they incur prohibitively high costs and cannot be used conventionally, hence requiring new robust and more convenient methods for TOC quantification .…”

Study on the Evolution of Fault Permeability and the Retention of Coal (Rock) Pillar under the Mining Conditions of Thick Coal Seam in the Footwall of Large Normal Fault

“…Yang et al , studied the seepage characteristics and evolution laws of broken rock mass. Hou et al , studied the evolution law, variation characteristics, and internal relations of coal porosity and permeability. On the other hand, the study of fault activation mechanisms and waterproof coal (rock) pillar retention is the basis for ensuring safe production in mines near faults, conducted by many domestic and foreign scholars .…”