Pore Structure and Fractal Character of Lacustrine Oil-Bearing Shale from the Dongying Sag, Bohai Bay Basin, China

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The pore structure and its complexity and heterogeneity control the occurrence states and fluidity of shale oil. The multifractal theory effectively characterizes the complexity and heterogeneity of the shale pore structure. In this study, serial technologies were applied to detect the pore systems of shale obtained from Dongying Sag, Bohai Bay Basin in China. The multifractal characteristics of the pore structure of the shale were analyzed based on its nuclear magnetic resonance (NMR) T 2 spectrum. The analysis results show that shale oil reservoirs can be classified into four types based on their T 2 spectra. Type I shales T 2 spectra show large p2 (1−20 ms), moderate p3 (>20 ms), but little p1 (<1 ms), characterized by large NMR and connected porosity, the lowest BET specific surface area (SSA), and the largest average pore throat diameter and S 1 contents. Large p2, moderate p1, and tiny p3 are the main distinctions of type II shales with the largest NMR porosity, large connected porosity, and BET SSA. The T 2 spectra of type III shales have large p1, moderate p2, and little p3, corresponding to large NMR porosity and BET SSA and the largest total organic content (TOC) and S 1 contents but lower connected porosity. Type IV shales have the most significant contents of micropores with the relatively largest p1 in the T 2 spectra characterized by the lowest NMR and connected porosity, the largest BET SSA, the lowest TOC, and S 1 contents but the largest clay mineral contents. Both types III and IV shales are unfavorable shale oil reservoirs. D q decreases monotonically as q increases, indicating the multifractal nature of shale pore structures. D 0 varies from 0.88 to 1.00 (mean: 0.95), and Δα ranges from 1.24 to 2.82 (mean: 1.79), suggesting complex and heterogeneous pore structures. Types I and II shales have lower D 0 values than types III and IV shales. Thus, type I organic-bearing massive felsic and type II organic-rich layered calcareous shales are favorable for shale oil reservoirs with large pores and large porosity. They have the least complex pore structures among the four shale types considered.

Section: Methodsmentioning

confidence: 99%

Insights into Pore Structures and Multifractal Characteristics of Shale Oil Reservoirs: A Case Study from Dongying Sag, Bohai Bay Basin, China

Zhang

Yin

et al. 2022

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“…Since the pore–throat distribution controls the fluidity of fluid in shale, the mercury injection curve is appropriate for classifying the shale pore–throat system. The author’s team investigated hundreds of shale samples from Chinese shale strata, including the Shahejie Formation in Dongying Sag of the Jiyang Depression, ,,, the Qingshankou Formation in the Songliao Basin, , the Shahejie Formation in Raoyang Sag of the Jizhong Depression, , the Lucaogou Formation in Jimsar Sag of the Junggar Basin, , the Funing Formation in the Northern Jiangsu Basin, , and the Xingouzui Formation in the Jianghan Basin. , These shale samples were analyzed using high-pressure mercury intrusion testing (for the experimental method, see the literature).…”

Section: Characterization and Classification Of Shale Microscopic Por...mentioning

confidence: 99%

“…On the basis of this, the grading evaluation criteria and the lower limit of formation of shale reservoirs are established and applied to the evaluation of target reservoirs. Combined with the research results of the author’s team in recent years, − ,− ,,− this paper reviews and summarizes the latest progress and development trends in this area.…”

Section: Introductionmentioning

confidence: 99%

“…Several methods or techniques can be used to characterize the microscopic pore–throats of shales. Although each technique has its application scope, comprehensively using various techniques can effectively characterize the full-scale pore–throat distribution in shales. ,,− The classical schemes of Hodot and the International Union of Pure and Applied Chemistry (IUPAC) have been proposed and widely used for microscopic pore classification. On the basis of the industrial adsorbents, Hodot divided the coal pore system into micropores (<10 nm), small pores (10–100 nm), mesopores (100–1000 nm), and macropores (>1000 nm).…”

Section: Introductionmentioning

confidence: 99%

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Research Progress of Microscopic Pore–Throat Classification and Grading Evaluation of Shale Reservoirs: A Minireview

Xiao

et al. 2022

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The grading evaluation of shales helps in understanding the shale reservoir formation and sweet-spot screening of physical properties. Reservoir formation of shale is closely related to the size, distribution, and connectivity of shale pore−throats. Therefore, characterization and classification of microscopic pore−throats are the basis for shale reservoir grading. However, previous microscopic pore−throat classification schemes for materials science are unsuitable for shales with strong heterogeneity. In recent years, the author's team has proposed a new classification method for microscopic pore−throats of shale reservoirs based on the comprehensive characterization of numerous shales with varied maturities in different basins. Results show that the microscopic pore−throats for all shales can be divided into micropores, small pores, mesopores, and macropores. The component, size, and distribution of microscopic pore−throats in shales in different basins differ due to the differences in mineral composition and diagenetic evolution of shales caused by varying depositional environments and burial histories. Correspondingly, the demarcation points of different types of pore−throats are different. After clustering statistical analysis of different types of microscopic pore−throats, shales can be divided into grade I, II, III, and IV reservoirs, regarded as good, medium, poor, and nonshale reservoirs, respectively. Grading evaluation criteria for shale reservoirs were then established. Further, a new technique for evaluating the reservoir flow zone index (FZI) and dividing flow units based on logging data was constructed using the relationship of permeability with porosity in the same hydraulic flow unit. The grading evaluation criteria can be applied to single wells, multiwells, and planes using logging data. The application in numerous shale oil and gas exploration areas in China shows that the established classification scheme can be applied to the grading evaluation and sweet-spot screening of shale reservoirs.

“…Due to developments in horizontal well and hydraulic fracturing techniques, shale (including mudstone) has emerged as a commercial reservoir, and a series of achievements have been made in shale gas exploration and exploitation. , Moreover, shale oil is regarded as a future worldwide energy source because of its considerable resources and remarkable exploration breakthroughs achieved. , The assessment of shale oil resources is one of the critical steps in shale oil exploration. The pyrolysis parameter S 1 (called residual hydrocarbon in this paper) and chloroform-extractable bitumen “A” are commonly used parameters to estimate shale oil content. − Rock-Eval experiments are efficient and simple and can provide many shale parameters. Therefore, S 1 is more suitable for characterizing the oil content in assessing shale oil resources .…”

Section: Introductionmentioning

confidence: 99%

Key Oil Content Parameter Correction of Shale Oil Resources: A Case Study of the Paleogene Funing Formation, Subei Basin, China

Zhang

Lin

et al. 2022

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Estimating shale oil resources is critical in shale oil exploration, and the pyrolysis parameter S 1 is a frequently used parameter to assess the oil amount in shale. However, S 1 loses some light and heavy hydrocarbons due to core storage conditions and experimental technology, resulting in underestimating shale oil resources. In this paper, a set of models were developed to correct the light and heavy hydrocarbon losses for S 1 based on conventional and multistage Rock-Eval experiments on liquefrozen and room-temperature shales collected from the Funing Formation, Subei Basin. Two types of correction models of heavy hydrocarbon loss for liquefrozen and room-temperature shales were determined by comparing the conventional and multistage Rock-Eval experiments, respectively. The light hydrocarbon loss correction model was obtained according to conventional Rock-Eval experiments on liquefrozen and room-temperature shales. Moreover, the estimation models of adsorbed, free, and movable amounts of oil were determined and validated by the oil saturation index (OSI) method. The results show that the total, adsorbed, free, and movable oil contents can be estimated well by these correction models. A case study from the Funing Formation, Subei Basin, indicates that the higher the content of total oil, the higher the amounts of free and movable oil, indicating that shale with more oil also has a more excellent mobile and developable potential. Organic matter is the main adsorbent for shale oil. Shales with TOC greater than 1.5% generally have greater free (movable) oil amounts, which may be the optimal target for shale oil exploration and exploitation. This study provides an innovative approach to correct the key parameters of shale oil resources, and thus, is crucial for the exploration and development of shale oil in the Funing Formation, Subei Basin.