Next Article in Journal
Building Resilience: Youth Learning Through Earthquake Shaking Simulations
Previous Article in Journal
Which Offers Greater Techno-Economic Potential: Oil or Hydrogen Production from Light Oil Reservoirs?
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Geosciences
Volume 15
Issue 6
10.3390/geosciences15060215
geosciences-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Applying Quantitative Fluorescence Techniques to Investigate the Effectiveness of Deep-Seated Mudstone Caprocks in the Junggar Basin, NW China

by
Jiangxiu Qu
1,
Keshun Liu
2,*,
Hailei Liu
3,
Minghui Zhou
4,
Xiujian Ding
1 and
Ming Zha
1
1
School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China
2
Chengdu Exploration and Development Research Institute of PetroChina Daqing Oilfield, Chengdu 610051, China
3
Research Institute of Exploration and Development, PetroChina Xinjiang Oilfield Company, Karamay 834000, China
4
Baikouquan Oil Production Plant, PetroChina Xinjiang Oilfield Company, Karamay 834000, China
*
Author to whom correspondence should be addressed.
Geosciences 2025, 15(6), 215; https://doi.org/10.3390/geosciences15060215
Submission received: 19 April 2025 / Revised: 4 June 2025 / Accepted: 6 June 2025 / Published: 10 June 2025
(This article belongs to the Section Geochemistry)
Browse Figures
Versions Notes

Abstract

The Central Depression of the Junggar Basin relies heavily on Permian lacustrine mudstone for deep-seated hydrocarbon sealing. This research investigated how the fluorescence parameters of caprock samples responded to the leakage of palaeo-oil zones based on measurements from SEM, Rock-Eval, and X-ray diffraction analysis. First, two sets of control experiments were conducted to establish the proper grain-size range of 100–140 mesh for testing caprock samples in the research area using quantitative fluorescence technology. Subsequently, based on the examination of the rock pyrolysis parameters and the fluorescence parameters against TOC values, the conjecture was formed that the quantitative fluorescence technology test results were mostly unaffected by the primary hydrocarbons. Lastly, four fluorescence parameters were used to assess seal integrity: quantitative grain fluorescence intensity of the extract (QGF E intensity, the meaning of QGF is the same in this study), QGF spectral peaks (QGF λmax), the ratio of QGF intensity to fluorescence intensity at 300 nm on the QGF spectrum (QGF index), and total scanning fluorescence spectral ratio R1 (TSF R1). The Permian caprock can effectively seal hydrocarbons as evidenced by the decrease of QGF E intensity and QGF index values with depth. When hydraulic fracturing causes caprock failure, it can lead to complete leakage of hydrocarbons from the palaeo-oil zones. As the depth becomes shallower, the QGF E intensity value increases, the QGF index value decreases. Due to the differences in the migration pathways of hydrocarbons in the caprock, those leaked from the Permian palaeo-oil zone into the well PD1 caprock are mainly condensate and light–normal crude oil, while the hydrocarbons from the Carboniferous palaeo-oil zone into the well MS1 caprock consist predominantly of light–normal crude oil and medium–heavy crude oil.

1. Introduction

The most common sedimentary rock in basin sediment is mudstone, a type of fine-grained sediment with grain sizes smaller than 62.5 μm that makes up over 70% of the sediment [1]. It can be a caprock for hydrocarbons [2,3,4], a source rock in conventional systems [5], a major target for unconventional oil and gas development [6,7,8], and a necessary condition for CO2 storage [9,10,11]. For non-wetting phase fluids, like gas and oil, to move through caprocks, they must overcome the capillary pressure resistance [12]. Important elements that influence the efficacy of the caprock are its lithology, sedimentary facies, continuity, thickness, and mechanical characteristics [13,14,15,16,17]. Caprock failure accounts for 48% of all exploration risk variables and is the main factor contributing to exploration failure in superimposed basins that have undergone multiple tectonic movements [18].
Both mechanical and chemical compaction are particularly powerful in the deep-seated (>4500 m) sedimentary basins [19,20,21,22], and mudstone possesses the necessary physical and chemical conditions to produce a good seal. Simultaneously, the high temperature and pressure found in deep-seated geological environments will alter the phase state of oil and gas [23,24], accelerate the hydrocarbon–water rock interaction [25,26], and encourage the formation of microcracks [27]. Scholars typically use breakthrough pressure to quantitatively evaluate the effectiveness of caprocks [28]. However, because of the heterogeneity of the mudstone itself [29,30] and the impact of the aforementioned factors, it is challenging for the tested samples to accurately represent the entire caprock [12].
With the development of oil and gas geochemical exploration technology, geochemical information contained in geological formations can be continuously measured [31,32,33,34]. By analyzing the geochemical information carried by rock cuttings to identify oil and gas reservoirs, hydrocarbon maturity, and migration patterns, the effectiveness of caprocks can also be indirectly characterized [33,35,36,37]. Liu et al. (2003) proposed utilizing quantitative fluorescence technology to swiftly identify oil inclusions within grains and adsorbed hydrocarbons on grain surfaces based on information such as fluorescence intensity and spectral characteristics [38]. Quantitative grain fluorescence (QGF), QGF on Extract (QGF E), and Total Scanning Fluorescence (TSF) are among the technologies that fall under this category [32,39]. Quantitative fluorescence technology has the advantages of being quick, easy to use, affordable, sensitive, requiring less sample weight, and having a long detection fluorescence band. Following years of development, it has found widespread application in studies such as analyzing hydrocarbon properties, tracking hydrocarbon migration paths, and evaluating shale oil maturity [40,41,42]. Nevertheless, the efficacy of the caprock has not yet been evaluated using this technology. Sandstone and mudstone are both clastic rocks that have undergone roughly similar diagenesis and essentially the same mineral composition type [5,20,43]. In the event that hydrocarbons leak through the caprock, they will also be captured by mineral lattice defects in mudstone to form inclusions [44,45] or adsorbed on grain surfaces through physical and chemical bonds [46]. As a result, quantitative fluorescence technology should be able to test mudstone samples and capture geochemical information left behind by hydrocarbon leakage in caprock.
Although the reservoir in the deep-seated part of the Junggar Basin is closer to the source rock, there has not been much exploration done at present because of the depth of burial (usually exceeding 5 km) [47]. According to the results of the fourth national resource assessment, the amount of unproven oil resources in the deep-seated part of the Junggar Basin exceeds 2.5 × 109 t, with an undiscovered natural gas resource exceeding 950 × 109 t. Regional caprocks play an important role in hydrocarbon accumulation in the basin, as well as controlling the vertical distribution of hydrocarbons, their phase states, and the abundance of oil and gas [48]. The Upper and Lower Urho formations are two sets of regional caprocks that have been deposited in this basin and are the main focus of this study. X-ray diffraction analysis, Rock-Eval pyrolysis, TOC, and fluorescence parameters were all measured. The results of these analyses were considered in the context of the variation patterns and causes of fluorescence parameters inside the caprock in order to attempt to establish the relationship between sample fluorescence parameters and the effectiveness of the caprock in the study area. We believe that this study not only has the potential to expand the application prospects of quantitative fluorescence technology but also has significant implications for promoting the development of oil and gas geochemical exploration technology.

2. Geological Setting

The Junggar Basin is an important part of the Central Asia Orogenic Belt, located between the Kazakhstan Plate, the Tarim Plate, and the Siberian Plate [49,50]. The Junggar Basin experienced the Hercynian movement, Indosinian movement, Yanshan movement, and Himalayan movement before forming a large superimposed basin [51]. It is divided into six primary structural units, including the Western Bulge, the Eastern Bulge, the Luliang Bulge, the Northern Tianshan piedmont thrust belt, the Central Depression, and the Wulungu depression, as well as 44 secondary structural units, based on the structural characteristics of the Permian system within the basin and the characteristics of the later structural transformation [52]. The study area includes the Mosuowan Bulge and the Mobei Bulge within the Central Depression of this basin (Figure 1a,b). Hercynian faults cut through the Carboniferous and Permian, and the fault distance is generally less than 300 m.
There is a relatively continuous sequence from the Carboniferous to the Quaternary, resulting in a total thickness of the strata exceeding 10 km [53]. Near the well MS1, the absence of the Jiamuhe Formation and Fengcheng Formation was caused by weathering and erosion. In the study area, the sandstones of Xiazijie Formation, Baikouquan Formation, Badaowan Formation, and the weathering crust at the top of the Carboniferous system are all potential reservoirs for hydrocarbons. These reservoirs mainly produce condensate oil, light oil, medium oil, and a small amount of natural gas. According to Wang et al. (2018), the Upper Urho Formation (P3w) consists of fan delta shore shallow lacustrine deposits, while the Lower Urho Formation (P2w) is a large lacustrine deposit from the Permian in the Junggar Basin [54]. These are the primary subjects of this investigation, in addition to being the regional caprocks of the Junggar Basin’s deep hydrocarbon deposits. In the study area, the Upper Urho Formation is mainly composed of grayish brown or brown mudstone with a thickness of 158–228 m. The Lower Urho Formation is mainly composed of gray or greenish gray mudstone, with a thickness of approximately 100–432 m.
Geosciences 15 00215 g001
Figure 1. Comprehensive geological profile of Mosuowan–Mobei Bulge in the Central Depression of the Junggar Basin (modified from [55,56]). (a) Division of structural units in the Junggar Basin; (b) geological overview; (c) stratigraphic bar chart.
Figure 1. Comprehensive geological profile of Mosuowan–Mobei Bulge in the Central Depression of the Junggar Basin (modified from [55,56]). (a) Division of structural units in the Junggar Basin; (b) geological overview; (c) stratigraphic bar chart.
Geosciences 15 00215 g001

3. Sampling and Methods

This study measured mudstone samples from the Upper Urho Formation (No. P1–P8) of well PD1 and the Lower Urho Formation (No. M1–M26) of well MS1. The measurement methods included X-ray diffraction analysis, porosity, permeability, TOC, Rock-Eval, SEM, and fluorescence parameters (Table 1). The above tests were completed at the State Key Laboratory of Deep Oil and Gas in China University of Petroleum (East China). The sample processing steps and experimental methods followed the standard procedures of this laboratory.

3.1. Mineralogy

The samples P1–P3 and P8 come from the Upper Urho Formation in the well PD1, with sampling depths of 5130.83 m, 5133.07 m, 5262.55 m, and 5261.9 m, respectively. The samples M1–M3 and P8 come from the Lower Urho Formation in the well MS1, with sampling depths of 6543.21 m, 6544.23 m, 6544.53 m, and 6542.26 m, respectively. The above sample composition was measured using a polycrystalline X-ray diffractometer with a scanning speed of 2°/min, a sampling step angle of 0.02°, a scanning range of 5–45°, and an accuracy of less than 0.02°. During the experiment, 30 g of the sample were mechanically crushed into powder and divided into two parts: one part was 25 g, with a grain size of 74 μm (200 mesh); another was 5 g, with a grain size of 47 μm (325 mesh). One-to-two grams of sample powder with a grain size of 47 μm were placed in the groove of the sample slide, which was then placed in the instrument to test the mineral content of the sample. The testing of clay mineral components can be divided into the following steps. (i) Take 20 g of a sample with a grain size of 74 μm and place it in a large beaker (2000 mL), soak it in distilled water, and disperse it using ultrasound. Let it stand for 5 min. (ii) Suck the clay grain suspension with a dropper and evaporate it into a small beaker (500 mL) for concentration. (iii) Use a dropper to aspirate 0.7 mL to 0.8 mL of the concentrated solution and dry it on a glass slide. (iv) After drying, send the glass slide into the instrument for testing.

3.2. TOC and Rock-Eval Pyrolysis

In this study, the rock pyrolysis parameters and TOC of the M4–M21 samples were measured; for the M22–M26 and P4–P8 samples, only the TOC was measured (Table 1). Use the CS744 carbon and sulfur analyzer to test the TOC (total organic carbon) of the sample, with an analysis range of 2 ppm–6% and an analysis accuracy of 1 ppm. During the experiment, 5 g of the sample were mechanically crushed to 100 mesh, and 140 mg were weighed and washed in a crucible with hydrochloric acid (concentration 3.6%) 15 times, followed by rinsing with clean water 15 times. Finally, the sample is dried for testing. S1, S2, and Tmax were tested using the Rock-Eval 7 rock pyrolysis instrument, with a maximum temperature of 850 °C and an FID (flame ionization detector) detector sensitivity of 100 pA–1 μA. One hundred milligrams of the sample are weighed into a small crucible and placed in a rock pyrolysis analyzer to test S1, S2, and Tmax. Then, a heating rate of 30 °C/min between 20 and 300 °C is used to release free hydrocarbons (S1 peak), and a heating rate of 30 °C/min in the range of 300–650 °C is used to cause the cracking of kerogen in the source rock and record the S2 peak. The pyrolysis temperature corresponding to the maximum yield of the S2 peak is recorded as Tmax.

3.3. Imaging Methods

The samples were shaped into cubes of 10 × 10 × 3 dimension, using a diamond wire cutting. Manual polishing in the same direction was conducted through a sequence of decreasing grit sizes. After manual polishing, the sample was placed in the Leica EM TIC 3X instrument for argon ion profiling (Leica Microsystems, Wetzlar, Germany). The sample is dissected under a vacuum environment (0.01 Pa) for 20 h, with the instrument angle set at 3°, the voltage set at 5 kV, and the muzzle current set at 10–11 μA. Finally, place the sample in the sample compartment of the FEI 450 scanning electron microscope for observation (FELMI, Graz, Austria).

3.4. Quantitative Fluorescence Techniques

Quantitative fluorescence technology utilizes a standardized procedure, detection method, and parameter settings to quantitatively detect the fluorescence intensity and spectral characteristics of adsorbed hydrocarbons on the surface of grains and oil inclusions inside grains, which includes a series of technologies such as QGF, QGF E, and TSF [39,57].
Mudstone has a smaller grain size than sandstone in Udden Wentworth’s clastic sedimentary classification. In other words, the grain size range of 60–80 mesh is suitable for testing most sandstone samples [58], while it may not be suitable for mudstone in the study area. In this experiment, P8 and M23 samples with different grain sizes were first tested to determine the suitable grain-size range for detecting mudstone samples. Next, P7 and M23 samples were ground into grains of different sizes. Then, samples were taken within the grain size ranges of 60–80 mesh, 80–100 mesh, 100–120 mesh, 120–140 mesh, 140–180 mesh, and 180–200 mesh. The 12 sets of fluorescence parameters have been tested. These samples undergo a standard cleaning procedure prior to detection to ensure that the fluorescence signal only comes from the fluid inclusions and hydrocarbons adsorbed on the surface of mineral grains [39].
The specific experimental procedure is as follows (Table 2). (i) Mechanical crushing of mudstone samples to 100–120 mesh (see Section 5.2 for details), mechanical crushing of reservoir samples to 60–80 mesh, and 2–3 g of the sample are weighed and placed in a 40 mL beaker for later use (Figure 2a). (ii) Twenty milliliters of dichloromethane (DCM) were added to the beaker, then placed in an ultrasonic shaker for 10 min to remove soluble hydrocarbons and drilling fluid pollutants. (iii) The waste liquid is poured out, the sample is cleaned with distilled water, and it is dried in a 60 °C oven. (iv) Forty milliliters of 10% hydrogen peroxide are added at room temperature and left to stand for 40 min. Then, it is placed in an ultrasonic machine and oscillated for 20 min to make the clay minerals attached to the surface of the rigid grains fall off. (v) The waste liquid is poured out, and the sample is rinsed with distilled water. Then, 40 mL of 3.6% hydrochloric acid were added to remove carbonate minerals that may produce mineral fluorescence and residual hydrogen peroxide. (vi) The sample is rinsed with distilled water and dried in a 60 °C oven. At this time, the remaining grains are mainly feldspar and quartz (Figure 2b). (vii) Twenty milliliters of DCM were added to the dried sample and shaken in an ultrasonic instrument for 10 min to extract the hydrocarbons attached to the surface of the feldspar and quartz. (viii) The extract in step 7 is used for QGF and TSF analysis, while feldspar and quartz grains are used for QGF analysis (Figure 2c). The fluorescence parameters of the sample are measured using Cary Eclipse grain fluorescence meter, which uses ultra ultraviolet laser with a wavelength of 220–340 nm.
The QGF spectrum mainly reflects the fluorescence characteristics of oil inclusions inside the grains [39]. The fluorescence parameters, such as QGF index, QGF ratio, and λmax, can be used to characterize this spectrum. While these fluorescence parameters are defined as follows:
QGF index is the ratio of QGF intensity to the corresponding fluorescence intensity at 300 nm (Equation (1)).
QGF index = QGF intensity/I300 nm
QGF ratio is the ratio of QGF intensity to the corresponding fluorescence intensity at 350 nm (Equation (2)).
QGF ratio = QGF intensity/I350 nm
QGF λmax refers to the wavelength (nm) corresponding to the maximum fluorescence intensity in the QGF fluorescence spectrum (Figure 3).
QGF E represents the fluorescence characteristics of the hydrocarbon extraction solution adsorbed on the grain surface and is commonly characterized by the QGF E intensity parameter.
QGF E intensity is the value obtained by normalizing the maximum intensity of the QGF E fluorescence spectrum to 1 g of grain and 20 mL of extraction solution (Equation (3)).
QGF E intensity = Imax/m
m represents the mass of the sample, g.
QGF λ refers to the wavelength (nm) corresponding to the maximum fluorescence intensity in the QGF E fluorescence spectrum.
TSF is an emission spectrum obtained by scanning a sample with excitation light with continuously changing wavelengths, and its results can be displayed in three-dimensional or contour form (Figure 4). The spectral characteristics of TSF can be characterized by parameters such as TSF R1 and TSF R2 [39]. The specific parameters are defined as follows.
TSF R1 is the ratio of the fluorescence intensity at the emission wavelength of 360 nm to the fluorescence intensity at the emission wavelength of 320 nm under 270 nm excitation light (Equation (4)). TSF R2 is the ratio of the fluorescence intensity corresponding to the emission wavelength at 360 nm to the fluorescence intensity at the emission wavelength of 320 nm under 260 nm excitation light (Equation (5)).
TSF R1 = I360 nm/I320 nm (270 nm)
TSF R2 = I360 nm/I320 nm (260 nm)

4. Results

4.1. Mineral Compositions

The mineral composition is primarily feldspar, quartz, calcite, and clay, according to X-ray diffraction analysis (Figure 5). Feldspar is the main mineral, with a content of 14.81–70.39 wt%. The quartz content ranges from 7.98 wt% to 38.98 wt%. Calcite is the main source of carbonate rocks in the sample, with a content of 0–21.54 wt%. The clay mineral content ranges from 6.97 wt% to 51.02 wt%. There is a significant difference in mineral composition between the samples from well PD1 and well MS1. The content of feldspar and quartz in well PD1 is high, while the content of clay is low. The mudstone of well MS1 has high quartz and clay content and low feldspar content.

4.2. TOC and Pyrolysis Parameters

The TOC value of the P2w mudstone ranges from 0.05 wt% to 2.18 wt%, with an average value of 0.72 wt% (Table 3); the S1 + S2 value ranges from 0.19 to 4.88 mg/g rock, with an average value of 1.00 mg/g. The hydrogen index (HI) value ranges from 35.09–400 mg/g TOC, with an average value of 92.15 mg/g TOC. The peak temperature of pyrolysis ranges from 298 to 471 °C, with an average value of 416 °C. The TOC value of the P3w mudstone ranges from 0.06 wt% to 0.98 wt%, with an average value of 0.33 wt%.

4.3. Backscatter Image

Scanning electron microscopy observation shows that the pores between the feldspar grains in the P2 sample are filled with authigenic feldspar, with the development of chlorite lining edges at the edge of the pores, and only a small amount of residual primary intergranular pores (Figure 6a,b). The dissolution effect on the edges of the feldspar grains in the P3 sample is significant, and the dissolution pores are filled with organic matter (Figure 6c). The M2 and M3 samples have relatively clear edges, with some feldspar and quartz grains visible. There are many microcracks visible in the M3 sample, including adherent cracks and intergranular microcracks of clay minerals, and there is no cement in the microcracks (Figure 6d–f). A small number of dissolution pores are observed on the surfaces of feldspar grains and clay minerals (Figure 6e,f). It is worth noting that most of the microcracks in the well MS1 sample are distributed along mineral grains.

4.4. Fluorescence Parameters

After cleaning in accordance with the standard experimental procedure (Table 2), the weight-loss rate (subtract the weight before sample cleaning from the weight after sample cleaning, divide by the weight before sample cleaning, and multiply by 100) of P8 control samples is 3.68–34.6% (Table 4). In order to better compare the difference between weight loss rate and sample clay content, the parameter of content deviation (its definition can be found in the annotations of Table 4) is introduced. The content deviation of P8 control samples is 5.77–396.85%. The weight loss rate for M23 control samples is 21.75–58.21%, with a content variation of 0.89–51.2%. In terms of fluorescence parameters, the QGF λ of P8 control samples ranges from 374 to 378 nm, and the QGF E intensity ranges from 184.33 to 286.16 pc (photometer count). The QGF λ of M23 control samples ranges from 370 to 457 nm, and the QGF E intensity ranges from 76.13 to 161.59 pc. It is easy to see that the weight-loss rate and QGF E intensity of the P8 and M23 control samples show a synchronous increasing trend as the sample grain size increases, while the content deviation first shows a trend of decreasing and then increasing.
P4–P8, M22–M26 are mudstone caprock samples; P9–P12, M27–M30 are reservoir samples. Samples P4–P12 have QGF ratio values ranging from 0.6 to 11.9, TSF R1 values ranging from 1.18 to 3.4, and TSF R2 values ranging from 1.64 to 4.87 (Table 5). They do not show regularity with depth variation. Samples P4–P12 have QGF index values ranging from 0.6 to 16.1, and QGF E intensity values ranging from 7 to 323.5 pc. These two parameters show a decreasing trend from the top of the reservoir to the top of the caprock. Samples P4–P12 have QGF λmax values ranging from 395.5 to 474.5 nm, and the data points are concentrated between 410–430 nm. Samples P4–P12 have QGF λ values ranging from 357 to 375 nm, and the data points are concentrated between 360–375 nm.
Samples M22–M30 have QGF ratio values ranging from 0.4 to 8.7, TSF R1 values ranging from 0.88 to 3.14, and TSF R2 values ranging from 1.43 to 5.36 (Table 5). These parameters do not show regularity with depth variation. Samples M22–M30 have QGF index values ranging from 2 to 57.5, and this parameter shows a decreasing trend from the top of the reservoir to the top of the caprock. The QGF E intensity value of samples M22–M30 ranges from 11.1–145.4 pc, and this parameter shows an increasing trend from the bottom of the caprock to the top. The QGF λmax values of samples M22–M30 are between 436.3–507 nm, and the data points are concentrated between 450–510 nm. The QGF λ values of samples M22–M30 are between 356–375 nm, and the data points are concentrated between 360–375 nm.
The QGF λmax values of these samples mainly range from 410 to 510 nm, which is similar to the fluorescence spectrum characteristics of crude oil; most of QGF λ is located near 370 nm, similar to the fluorescence spectra of tetracyclic aromatic hydrocarbons contained in crude oil in solvents [59]. On the other hand, in previous tests on sandstone samples, TSF R1 and TSF R2 usually showed a good correlation [60]. The results also show a good positive correlation (Figure 7). In summary, the data obtained from this testing of mudstone samples should be considered reliable.

5. Discussion

5.1. Fluorescence Parameter Characteristics of Reservoirs

Samples P9–P12 have QGF index values ranging from 3.9–16.1, while samples M27–M30 have QGF index values ranging from 2 to 57.5 (Table 4). The QGF spectra of the reservoir samples from wells PD1 and MS1 show clear, distinctive peaks in the 400–600 nm range, as shown in Figure 8. Palaeo-oil zones often have a QGF index value larger than 4, whereas water zones typically have a value less than 4 [57]. The QGF spectrum of the palaeo-oil zones exhibits prominent peaks between 400 and 600 nm, while the curve of the water zone is rather flat and close to the baseline [32]. Therefore, it is believed that both the Permian of the well PD1 and the Carboniferous of the well MS1 have palaeo-oil zones. Samples M27–M30 have an average QGF index value of 21.45, while samples P9–P12 have an average QGF index value of 7.67. Liu and Eadington (2005) found a positive correlation between the sample’s QGF index value and its oil inclusion abundance (GOI, grains containing oil inclusions) [32]. Additionally, the palaeo-oil zones’ oil saturation increases with GOI value of the reservoir sample. As a result, it is possible that well MS1’s palaeo-oil zones have a higher oil saturation than well PD1.
The QGF λmax value shows a negative correlation with the crude oil API value, while the TSF R1 has an excellent negative correlation with the geochemical parameter Ts/(Ts + Tm), which can be used to determine the maturity of crude oil (Table 6). Samples P9–P12 have QGF λmax values ranging from 395.5 to 429.7 nm, and the TSF R1 values are between 2.91 and 6.28, suggesting that the palaeo-oil zone of well PD1 has been charged with condensate, normal–light crude oil, and medium–heavy crude oil. Samples M27–M30 have QGF λmax values ranging from 479.7 to 507 nm, and the TSF R1 values are 0.88 and 3.18, respectively, suggesting that the palaeo-oil zones of well MS1 have been charged with condensate and medium–heavy crude oil. Furthermore, prior studies have indicated that the Carboniferous palaeo-oil zones of well MS1 were once charged with normal–light crude oil [61]. It can be seen that the maturity of crude oil in the palaeo-oil zones of well MS1 and PD1 is the same. In summary, the fluorescence parameter indicates that there is a difference in the oil filling intensity of the palaeo-oil zones between well PD1 and MS1, while the maturity of the crude oil is consistent.

5.2. Applicability of Quantitative Fluorescence Techniques

Based on the analysis of the mineral composition of the samples (Figure 5), it is speculated that P8 and M23 should contain almost no carbonate. Thus, the clay minerals are mostly responsible for their weight reduction throughout the cleaning procedure (Section 3.4). The weight-loss rate of the P8 sample after cleaning increased from 3.68% to 34.63% as the grain size decreased, and the weight-loss rate of the M23 sample after cleaning increased from 21.75% to 58.21% (Table 3). As the grain size of P8 and M23 samples decreases in the 60–120 mesh range, the content deviation decreases synchronously (Figure 9a). This indicates that as the degree of sample grain size decreases, the proportion of feldspar and quartz minerals in the cleaned sample increases. As the grain size of P8 and M23 samples decreases in the range of 120–200 mesh, the content deviation gradually increases, which should be related to the enrichment of clay minerals. The strength of clay minerals is weaker than that of rigid grains such as quartz, and it is easier to be stripped from the mineral grain aggregates by the grinder blade so as to gradually enrich in samples.
The QGF E intensity value of the P8 sample with a grain-size range of 80–100 mesh is 184.33 pc, while the other grain sizes are around 233 pc (Figure 9b). This may be due to a systematic error during the experimental procedure, resulting in a significantly lower measurement value. Actually, the measurement value of a sample with a grain size range of 80–100 mesh is similar to that of other samples. When the sample grain size is between 180 and 200 mesh, the content deviation is close to 400%, and the QGF E intensity value is about 50 pc higher than the average value. It can be seen that for samples with low clay content, even if their content deviation is close to 50%, it will not have a significant impact on fluorescence parameters. This indicates that the P8 sample in these grain-size ranges can meet the experimental requirements after cleaning. When the grain size of the M23 sample is 60–80 and 80–100 mesh, the content deviation is higher than 30%. There are still many residual clay minerals in the sample. The QGF λ values of these samples are around 450 nm instead of 370 nm (Table 3), further indicating that their fluorescence parameter measurements are unreliable [59]. When the grain size of the M23 sample is 100–120 and 120–140 mesh, the content deviation (0.89%, 6.27%, respectively) is at a lower level, and the weight loss rate of the sample is extremely close to the clay content (Figure 9a). Meanwhile, the QGF E intensity of M23 samples is relatively stable when the grain size is 100–120 and 120–140 mesh, ranging from 104.79 to 109.31 pc (Figure 9b). This indicates that the M23 sample in these grain size ranges can meet the experimental requirements after cleaning. The content deviation of samples in the grain size range of 140–180 and 180–200 mesh is about 30%, and the QGF E intensity value exceeds 130 pc. This indicates that for samples with high clay content, a content deviation exceeding 30% will significantly affect the fluorescence parameter measurement values.
In this study, the 60–180 mesh grain-size range is suitable for testing mudstones with a low clay content, and the 100–140 mesh grain-size range is suitable for testing mudstones rich in clay. Although samples with a grain size range of 100–140 mesh still retain some clay minerals after cleaning, the impact on the test results is relatively small (Figure 9b). Furthermore, the author speculates that 100–140 mesh would be a suitable grain size range for mudstone samples with unknown clay content.

5.3. The Primary Hydrocarbons Implications for Fluorescence Parameter

Primary hydrocarbons refer to the liquid and gaseous hydrocarbons generated by organic matter that remain in the mudstone without undergoing initial migration [62]. The impact of mudstone primary hydrocarbons on test results (such as acidolysis hydrocarbon) is typically disregarded when utilizing oil and gas geochemical exploration technology to investigate the source, migration form, and migration direction of hydrocarbons in oil and gas reservoirs [34]. In this section, we briefly discussed the influence of primary hydrocarbons on the fluorescence parameters of mudstone samples.
According to the classification standard of continental source rock (SYT5735-1995), P2w in the study area is classified as medium-to-poor source rock, while P3w is classified as non-source rock (Table 3). The organic matter type of P2w is primarily type III (Figure 10a). From the perspective of effective hydrocarbon expulsion thickness, even for thin source rocks, the hydrocarbon expulsion efficiency is only 10% when the organic matter type is type III [63]. It can be seen that the TOC and S1 values of the M4–M21 sample are almost below the gray line, indicating that the free hydrocarbons currently trapped in the source rock are mostly primary hydrocarbons (Figure 10b). If the hydrocarbon generation of organic matter has a significant impact on the fluorescence parameters of mudstone samples, then there should be a positive correlation between primary hydrocarbons and fluorescence parameters. Furthermore, due to the synchronous increase trend between the TOC and S1 value (R2 = 0.6605), it is speculated that there should also be a synchronous increase trend between the TOC value and its fluorescence parameters of mudstone samples.
The acidic compounds in crude oil entering the caprock can cause the water film on the mineral surface to rupture, thereby changing the wettability of the mineral [65]. Usually, the bottom of the caprock experiences this shift in wettability first [66]. The P8 sample is very close to the top of the reservoir (indicated by the black arrow in Figure 11a), and the fluorescence parameters may exhibit anomalies due to long-term immersion of crude oil. There is a positive correlation between organic matter maturity and burial depth [67]. The TSF R1 value of the P4–P8 samples should exhibit a decreasing trend with increasing depth rather than an increasing trend if the primary hydrocarbon is the principal factor influencing the QGF index value of the P8 sample (Table 5). Therefore, this data point was excluded when analyzing the correlation between TOC and QGF index values.
When the TOC value increased from 0.089 wt% to 1.83 wt%, the QGF index value decreased from 5.2 to 2.1 (Figure 11a). When TOC increased from 0.064 wt% to 1.83 wt%, the QGF ratio value decreased from 11.9 to 1.3 (Figure 11b). There is no positive correlation between the TOC and fluorescence parameters value of the mudstone samples in the study area. This may indicate that the influence of primary hydrocarbons in the mudstone on the quantitative fluorescence technology test results is relatively weak. In other words, the geochemical information extracted from mudstone samples by quantitative fluorescence technology may mainly come from hydrocarbons leaked into the caprock from palaeo-oil zones. Therefore, in the next section, we further discuss the response of the fluorescence parameters of mudstone samples to the leakage of palaeo-oil zones.

5.4. Response of Fluorescence Parameters of Caprocks to the Leakage of Palaeo–Oil Zones

5.4.1. Natural Hydraulic Fracturing of Mudstone Caprock

The pressure coefficient of the P2w on the well MS1 is 2.0, and the pressure coefficient of the P3w on the well PD1 is 1.53, calculated by the DC index method. The lithostatic pressure gradient of Mobei and Mosuowan Bulges is approximately 24.5 MPa/km; the pore pressure of the caprock in well PD1 and well MS1 is 62.45% and 81.63% of the lithostatic pressure, respectively. Hydraulic fracturing will occur in the stratum when the pore pressure reaches 82–85% of the lithostatic pressure, leaving a number of vertical high-angle fractures [68,69,70]. As a result, hydraulic fracturing may have occurred in the upper caprock of the palaeo-oil zones in the well MS1. This hypothesis was further supported by the examination of microscopic images, micro-resistivity logging images, and biomarker compounds (Figure 6a and Figure 12).
Microscopic image observation shows that the pore types of well PD1 mudstone are mainly dissolution pores, with a small amount of residual intergranular pores and no microcracks (Figure 6a–c); the pore types of mudstone in well MS1 are mainly dissolution pores and microcracks (Figure 6d–f). Some microcracks can be seen in the figure surrounding the distribution of mineral grains, which should be a type of tensile fracture caused by excessive pore pressure [71]. There are 98 fractures in the 6642–6740 m section of the well MS1 micro resistivity imaging logging image. Drilling-induced fractures often appear symmetrically along the wellbore, arranged in a feather-like pattern with a similar dip angle. However, in Figure 12a, the fractures did not exhibit such characteristics. Further, 67.01% of these fractures have a dip angle exceeding 60° and exhibit hydraulic fracturing characteristics. C29 Sterane ββ/(ββ + αα) and C29 Sterane ααα 20S/(20S + 20R) are commonly used parameters to characterize the maturity of crude oil [72]. The former is influenced by the degree of thermal evolution of the source rock and the geochromatography effect during hydrocarbon migration, showing an increasing trend along the migration direction; the latter increases with the degree of thermal evolution of the source rock. The C29 Sterane ββ/(ββ + αα) ratio in the well MS1 cuttings extract increases with decreasing depth, while the C29 Sterane ααα 20S/(20S + 20R) ratio remains almost unchanged vertically (Figure 12b,c). The distribution range of crude oil maturity is similar from bottom to top, suggesting that there has been a large amount of vertical fluid migration in well MS1 [61]. This type of fluid migrates vertically over a range of more than 3000 m, and it is nearly impossible for rock pores to serve as its migration pathways [73,74]. It is worth noting that the faults in the north and south of well MS1 have ceased activity since the Triassic, while the following analysis indicates that vertical fluid migration has occurred recently. So, the vertical migration path of fluid is not a fault, but rather a hydraulic fracturing.

5.4.2. Fluorescence Parameter Characteristics of Caprock

When the caprock has capillary sealing ability, it allows hydrocarbons to slowly leak out of the palaeo-oil zone in a diffusion form driven by concentration differences; when hydraulic fracturing occurs, the palaeo-oil zones will experience significant leakage [75]. Diffusion migration features have been seen in the natural gas beneath the Permian caprock in nearby areas of well PD1 [55]. This suggests that the well PD1 caprock has a great capillary sealing ability, which effectively blocks the hydrocarbons in the Permian palaeo-oil zones. The hydrocarbons will leak through the caprock in the form of diffusion. From the current oil testing results of the Permian reservoir, it can be concluded that the palaeo-oil zones of well PD1 are only partially lost (Figure 13a). From the bottom to the top of the caprock, there is a decreasing tendency in the concentration of hydrocarbons since concentration differences are what drive the diffusion of chemicals. Additionally, the polar component of crude oil, aromatic hydrocarbons, will be preferentially adsorbed by rock mineral grains under the influence of the geochromatography effect [76]. This will result in a decrease in the relative content of aromatic hydrocarbons in the crude oil component along the migration direction. Accordingly, from the bottom of the caprock to the top, the QGF E intensity and QGF index of well PD1 exhibit a substantial decreasing trend (Figure 13b,c). The QGF E intensity value decreased from 219.65 pc to 9 pc, and the QGF index value decreased from 8.4 to 1.6. The trend of the fluorescence parameter variation is consistent with the results of Krooss et al. (1988) [77], which demonstrates that the hydrocarbon content inside the caprock is positively associated with depth when diffusion takes place in palaeo-oil zones.
During the accumulation of pore pressure, the well MS1 caprock should have capillary sealing ability; otherwise, it will not form the Carboniferous palaeo-oil zones. At this stage, the palaeo-oil zones of well MS1 mainly leak hydrocarbons in the form of diffusion, and the vertical variation pattern of fluorescence parameters QGF E intensity and QGF index inside the caprock is the same as that of well PD1. After hydraulic fracturing occurs, hydrocarbons leak through the fracture channels of the caprock, with an average rate of 2 × 108–2.5 × 109 m3/(km2·a−1) [78]. According to this rate, even if the episodic fluid discharge only lasts for 20–50 years [79], it is still sufficient for the palaeo-oil zones to completely leak. This should be due to the existence of a palaeo-oil zone in the well MS1, which currently only produces water (Figure 14a). The QGF E intensity value of the M27–M30 sample is higher than 10 pc, indicating the presence of relatively high residual oil in the reservoir of well MS1, indicating that hydrocarbon leakage has occurred recently [57]. However, the procedure of mineral crystallization growth is slow, and when the caprock undergoes hydraulic fracturing, mineral lattice defects do not have enough time to capture hydrocarbons and form inclusions [80]. Therefore, the QGF index value of well MS1 still shows a decreasing trend from the bottom of the caprock to the top, from 4.5 to 2.1 (Figure 14c). The procedure of hydrocarbons leaking through the cracks at the top of the trap can be regarded as the displacement of hydrocarbons by the edge water and bottom water of the palaeo-oil zones. Components that are more soluble in crude oil will be removed preferentially by water-washing, which will enrich aromatic hydrocarbons in the direction of migration [81,82]. With the enrichment of aromatic hydrocarbons, the QGF E intensity value will show a trend of increasing from the bottom of the caprock to the top, from 11.1 pc to 145.4 pc (Figure 14b). The average QGF index value of well PD1 (excluding P8 samples) is less than that of well MS1, despite the fact that the QGF index values of both wells show the same trend of change from the bottom to the top of the caprock. This may be due to the lower charging intensity of the Permian palaeo-oil zones in well PD1 compared to the Carboniferous palaeo-oil zones in well MS1, resulting in less hydrocarbon diffusion loss.
P4–P8 samples have QGF λmax values between 413.5 and 474.5 nm and TSF R1 values between 1.18 and 3.4, suggesting that normal–light oil and condensate make up the majority of the hydrocarbons that leaked from palaeo-oil zones into the caprock. The QGF λmax values of the M22–M27 samples fall between 436.3 and 504.9 nm, whereas the TSF R1 values range between 1.55 and 3.14. These results suggest that the hydrocarbons leaked from palaeo-oil zones into the caprock are medium–heavy oil, normal–light oil, and condensate. Comparison shows that 80% of the data points in the well PD1 caprock fall in the condensate and normal–light oil intervals (Figure 13d), while 80% of the data points in the well MS1 caprock fall in the normal–light oil and medium–heavy oil intervals (Figure 14d). These hydrocarbons’ varying maturities ought to be a reaction to variations in caprock effectiveness. As the maturity of crude oil increases, its average relative molecular weight will decrease, and its molecular radius will decrease. The Stokes–Einstein equation states that the diffusion coefficient increases with decreasing molecular radius of a substance. Thus, in well PD1, medium–heavy crude oil is found at the bottom of the caprock, and condensate is found at the top. Hydraulic fracturing occurred in the well MS1 caprock, and the fractures may also result in the loss of low-maturity crude oil (Figure 14d). As a result, compared to well PD1, medium–heavy crude oil will be found in the upper portion of the well MS1 caprock, with more data points between the medium–heavy crude oil zones. Overall, the different escape pathways not only result in differences in fluorescence parameters’ characteristics but also lead to variations in the maturity of hydrocarbons leaked from the reservoir into the caprock.

6. Conclusions

Based on X-ray diffraction analysis, Rock-Eval, TOC, and SEM measurements, this study discusses the response of fluorescence parameters of the caprock samples to hydrocarbon leakage from the underlying palaeo-oil zone.
Two control experiments showed that the mudstone in the study area is suitable for fragmentation to 100–140 mesh before fluorescence parameter testing. The intersection diagram of TOC and fluorescence parameter shows that the implication of primary hydrocarbon in mudstone on the test results of quantitative fluorescence technology is relatively weak.
When hydrocarbons are efficiently sealed by the caprock and depth decreases, the QGF index value and QGF E intensity value drop. While the caprock fails due to hydraulic fracturing, the QGF E intensity value increases with shallower depth under water-washing conditions. If the hydraulic fracturing of the caprock has recently occurred, the QGF index value still retains its early characteristics and decreases with shallower depth.
In the well MS1 caprock, the proportion of data points is high between normal–heavy crude oil areas, whereas the proportion of data points in the well PD1 caprock is high between condensate–normal crude oil areas. The varying maturity of hydrocarbons migrated from the paleo-oil zone into the caprock interior likely reflects differences in the effectiveness of the caprock. The caprock effectiveness can be indirectly reflected by evaluating QGF λmax, QGF E, and TSF R1 values. Therefore, quantitative fluorescence technology may be a new technique for evaluating the effectiveness of caprocks.

Author Contributions

Conceptualization, K.L.; Methodology, J.Q., K.L. and X.D.; Software, K.L. and M.Z. (Minghui Zhou); Validation, M.Z. (Minghui Zhou); Formal analysis, K.L.; Resources, H.L. and M.Z. (Ming Zha); Data curation, H.L.; Writing—original draft, K.L.; Writing—review and editing, J.Q., K.L., X.D. and M.Z. (Ming Zha); Visualization, M.Z. (Minghui Zhou); Supervision, J.Q. and M.Z. (Ming Zha); Project administration, X.D.; Funding acquisition, H.L. and X.D. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a grant from the National Natural Science Foundation of Shandong Province (ZR2022MD066), grants from the National Natural Science Foundation of China (Grant No. 41702143; No. 42272191), and major projects of PetroChina science and technology (2021DJ0206).

Data Availability Statement

Data are contained within the article.

Acknowledgments

We thank the editors and anonymous reviewers for their constructive and helpful comments.

Conflicts of Interest

Authors Hailei Liu and Minghui Zhou were employed by the company PetroChina Xinjiang Oilfield Company and PetroChina Xinjiang Oilfield Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Aplin, A.C.; Macquaker, J.H.S. Mudstone diversity: Origin and implications for source, seal, and reservoir properties in petroleum systems. AAPG Bull. 2011, 95, 2031–2059. [Google Scholar] [CrossRef]
  2. Davy, C.; Skoczylas, F.; Lebon, P.; Dubois, T. Gas migration properties through a bentonite/argillite interface. Appl. Clay Sci. 2009, 42, 639–648. [Google Scholar] [CrossRef]
  3. Liu, X.Y.; Chen, H.H.; Huang, Y.H.; Safaei-Farouji, M.; Ostadhassan, M. An indication of salt caprock failed integrity in inclusion records: Dongpu depression, bohai bay basin, China. Mar. Pet. Geol. 2023, 148, 106016. [Google Scholar] [CrossRef]
  4. Skerbisch, L.; Misch, D.; Drews, M.; Stollhofen, H.; Sachsenhofer, R.F.; Arnberger, K.; Schuller, V.; Zamolyi, A. Regional mudstone compaction trends in the Vienna Basin: Top seal assessment and implications for uplift history. Int. J. Earth Sci. 2023, 112, 1901–1921. [Google Scholar] [CrossRef]
  5. Wang, Y.; Gang, W.; Zhu, C.; Jiang, W.; Liao, W. Source rock evaluation and hydrocarbon generation and expulsion characteristics in Es3 in Qikou Sag. Pet. Geol. Recovery Effic. 2023, 03, 11–27. [Google Scholar]
  6. Bennani, R.; Wang, M.; Wang, X.; Li, T.Y. Porosity estimation using machine learning approaches for shale reservoirs: A case study of the Lianggaoshan Formation, Sichuan Basin, Western China. J. Appl. Geophys. 2025, 237, 105702. [Google Scholar] [CrossRef]
  7. Wu, S.T.; Guan, M.D.; Wang, X.H.; Zhang, J.; Zhou, Y.H.; Huang, X.; Pan, B. Machine learning-based shale-alkane-brine contact angle prediction at in-situ reservoir conditions. Fuel 2025, 395, 135106. [Google Scholar] [CrossRef]
  8. Cushing, L.J.; Vavra-Musser, K.; Chau, K.; Franklin, M.; Johnston, J.E. Flaring from Unconventional Oil and Gas Development and Birth Outcomes in the Eagle Ford Shale in South Texas. Environ. Health Perspect. 2020, 128, 077003. [Google Scholar] [CrossRef]
  9. Norris, S. Clays in Natural and Engineered Barriers for Radioactive Waste Confinement: An introduction. Geol. Soc. Lond. Spec. Publ. 2014, 400, 1–5. [Google Scholar] [CrossRef]
  10. Espinoza, D.N.; Santamarina, J.C. CO2 breakthrough—Caprock sealing efficiency and integrity for carbon geological storage. Int. J. Greenh. Gas Control. 2017, 66, 218–229. [Google Scholar] [CrossRef]
  11. Tarkowski, R. Underground hydrogen storage: Characteristics and prospects. Renew. Sustain. Energy Rev. 2019, 105, 86–94. [Google Scholar] [CrossRef]
  12. Downey, M.W. Evaluating Seals for Hydrocarbon Accumulations. AAPG Bull. 1984, 68, 1760. [Google Scholar]
  13. Dewhurst, D.N.; Piane, C.D.; Esteban, L.; Sarout, J.; Josh, M.; Pervukhina, M.; Clennell, M.B. Geological Carbon Storage. In Geological Carbon Storage: Subsurface Seals and Caprock Integrity; Geophysical Monograph Series; American Geophysical Union: Washington, DC, USA, 2018; pp. 1–30. [Google Scholar]
  14. Rahman, M.J.; Fawad, M.; Mondol, N.H. Organic-rich shale caprock properties of potential CO2 storage sites in the northern North Sea, offshore Norway. Mar. Pet. Geol. 2020, 122, 104665. [Google Scholar] [CrossRef]
  15. Lloyd, C.; Huuse, M.; Barrett, B.J.; Stewart, M.A.; Newton, A.M.W. A regional CO2 containment assessment of the northern Utsira Formation seal and overburden, northern North Sea. Basin Res. 2021, 33, 1985–2017. [Google Scholar] [CrossRef]
  16. Wang, F.; Chen, D.; Wang, Q.; Du, W.; Chang, S.; Wang, C.; Tian, Z.; Cheng, M.; Yao, D. Quantitative evaluation of caprock sealing controlled by fault activity and hydrocarbon accumulation response: K gasfield in the Xihu Depression, East China Sea Basin. Mar. Pet. Geol. 2021, 134, 105352. [Google Scholar] [CrossRef]
  17. An, F.; Jia, H.; Feng, Y. Effect of stress, concentration and temperature on gas diffusion coefficient of coal measured through a direct method and its model application. Fuel 2022, 312, 122991. [Google Scholar] [CrossRef]
  18. Zhang, S.Q. Evaluation on the Dynamic Evolution of Ultra-Deep Caprock in Sichuan Basin. Master’s Thesis, China University of Petroleum, Beijing, China, 2021; p. 5. (In Chinese). [Google Scholar]
  19. Thyberg, B.; Jahren, J. Quartz cementation in mudstones: Sheet-like quartz cement from clay mineral reactions during burial. Pet. Geosci. 2011, 17, 53–63. [Google Scholar] [CrossRef]
  20. Bjørlykke, K. Relationships between depositional environments, burial history and rock properties. Some principal aspects of diagenetic process in sedimentary basins. Sediment. Geol. 2014, 301, 1–14. [Google Scholar] [CrossRef]
  21. Li, C.; Luo, X.; Likuan, Z.; Yuhong, L.; Ming, C.; Lan, Y. Chemical compaction of deep buried mudstone and its influence on pressure prediction. In Proceedings of the IOP Conference Series: Earth and Environmental Science, Changsha, China, 18–20 September 2020; Institute of Physics: London, UK, 2020; Volume 600, p. 012012. [Google Scholar]
  22. Nie, H.K.; Jin, Z.J.; Li, P.; Katz, B.J.; Dang, W.; Liu, Q.; Ding, J.; Jiang, S.; Li, D. Deep shale gas in the Ordovician-Silurian Wufeng–Longmaxi formations of the Sichuan Basin, SW China: Insights from reservoir characteristics, preservation conditions and development strategies. J. Asian Earth Sci. 2023, 244, 105521. [Google Scholar] [CrossRef]
  23. Liu, Y.; Tian, Y.; Yang, C.; Xiao, D.; Liao, Q.; Shen, C.; Fan, Y.; Tang, P.; Pu, X.; Jiang, W. Hydrocarbon phase limit and conversion process in the deep formation from Qikou Sag of Bohai Bay rift lacustrine basin, China. J. Nat. Gas Geosci. 2017, 2, 229–238. [Google Scholar] [CrossRef]
  24. Ghaderi, S.M.; Nourozieh, H. Estimation of bitumen and bitumen-liquid solvent volumetric properties from analysis of extensive experimental data with application in numerical simulation. Fuel 2019, 244, 227–239. [Google Scholar] [CrossRef]
  25. Hu, W.X.; Kang, X.; Cao, J.; Wang, X.L.; Fu, B.; Wu, H.G. Thermochemical oxidation of methane induced by high-valence metal oxides in a sedimentary basin. Nat. Commun. 2018, 9, 5131. [Google Scholar] [CrossRef] [PubMed]
  26. Guo, H.; Liu, M.; Wang, Y.; Wang, Q.; Liu, J.; Peng, P. The effect of thermochemical sulfate reduction of n-pentane and condensate on the chemical and isotopic compositions of gases: Implications for the organic reactant and reaction extent. Mar. Pet. Geol. 2022, 145, 105916. [Google Scholar] [CrossRef]
  27. Fan, Z.Q.; Jin, Z.J.; Johnson, S.E. Oil-Gas Transformation Induced Subcritical Crack Propagation and Coalescence in Petroleum Source Rocks. Int. J. Fract. 2014, 185, 187–194. [Google Scholar] [CrossRef]
  28. Jin, Z.J.; Yuan, Y.S.; Sun, D.; Liu, Q.Y.; Li, S. Models for dynamic evaluation of mudstone/shale caprocks and their applications in the Lower Paleozoic sequences, Sichuan Basin, SW China. Mar. Pet. Geol. 2014, 49, 121–128. [Google Scholar] [CrossRef]
  29. Yang, Y.; Aplin, A.C. Permeability and petrophysical properties of 30 natural mudstones. J. Geophys. Res. Solid Earth 2007, 112, B03206. [Google Scholar] [CrossRef]
  30. Yang, Y.; Aplin, A.C. A permeability–porosity relationship for mudstones. Mar. Pet. Geol. 2010, 27, 1692–1697. [Google Scholar] [CrossRef]
  31. Schumacher, D.D. Surface geochemical exploration for oil and gas new life for an old technology. Lead. Edge 2000, 19, 258–261. [Google Scholar] [CrossRef]
  32. Liu, K.; Eadington, P. Quantitative fluorescence techniques for detecting residual oils and reconstructing hydrocarbon charge history. Org. Geochem. 2005, 36, 1023–1036. [Google Scholar] [CrossRef]
  33. Przyjalgowski, M.A.; Ryder, A.G.; Feely, M.; Glynn, T.J. Analysis of Hydrocarbon-bearing Fluid Inclusions (HCFI) using time-resolved fluorescence spectroscopy. In Proceedings of the Conference on Optical Sensing and Spectroscopy, Seattle, WA, USA, 17–24 August 2025; Department of Physics, National University of Irel: Galway, Ireland, 2005; Volume 5826, pp. 173–184. [Google Scholar]
  34. Chen, W.M.; Li, Q.X.; Xiao, X.L.; Zhang, Q.; Deng, T.L. Determination of Acidolysis Hydrocarbons in Oil/Gas Geochemical Exploration Sample by Gas Chromatograph. Acta Geol. Sin. 2014, 88, 128–129. [Google Scholar] [CrossRef]
  35. Leith, T.L.; Kaarstad, I.; Connan, J.; Pierron, J.; Caillet, G. Recognition of caprock leakage in the Snorre Field, Norwegian North Sea. Mar. Pet. Geol. 1993, 10, 29–41. [Google Scholar] [CrossRef]
  36. Kalio, V.G.; Sunday, A.; Beka, F.T. Application of molecular markers for the determination of leakage through the caprock of asgard field. Int. J. Sci. Invent. Today 2015, 4, 140–150. [Google Scholar]
  37. Li, W.; Zhu, H. Borehole Oil and Gas Geochemical Exploration Technique and Its Application. Mar. Geol. Quat. Geol. 2012, 32, 159–166. [Google Scholar] [CrossRef]
  38. Liu, K.; Eadington, P.; Coghlan, D. Fluorescence evidence of polar hydrocarbon interaction on mineral surfaces and implications to alteration of reservoir wettability. J. Pet. Sci. Eng. 2003, 39, 275–285. [Google Scholar] [CrossRef]
  39. Liu, K.; George, S.C.; Lu, X.; Gong, S.; Tian, H.; Gui, L. Innovative fluorescence spectroscopic techniques for rapidly characterising oil inclusions. Org. Geochem. 2014, 72, 34–45. [Google Scholar] [CrossRef]
  40. Liu, N.; Qiu, N.; Chang, J.; Shen, F.; Ma, X.; Liu, C.; Feng, Q. Quantitative fluorescence techniques for investigating hydrocarbon charge history in carbonate reservoir. Energy Explor. Exploit. 2017, 35, 356–375. [Google Scholar] [CrossRef]
  41. Guo, X.; Chen, J.; Huang, Z.; Jin, Y.; Chen, C.; Li, T.; Ding, C.; Wang, R. Geochemical characteristics and accumulation of hydrocarbon in Lower Jurassic tight sandstone in Wenjisang area, Turpan Basin, NW China. Pet. Sci. Technol. 2019, 37, 853–861. [Google Scholar] [CrossRef]
  42. Liu, B.; Bai, L.; Chi, Y.; Jia, R.; Fu, X.F.; Yang, L. Geochemical characterization and quantitative evaluation of shale oil reservoir by two-dimensional nuclear magnetic resonance and quantitative grain fluorescence on extract: A case study from the Qingshankou Formation in Southern Songliao Basin, northeast China. Mar. Pet. Geol. 2019, 109, 561–573. [Google Scholar]
  43. Hao, J.; Taylor, K.G.; Hollis, C. Mineral diagenesis and inferred fluids in basinal mudstones: The Carboniferous Morridge Formation, Widmerpool Gulf, UK. Mar. Pet. Geol. 2021, 134, 105373. [Google Scholar] [CrossRef]
  44. Sun, N.; He, W.; Zhong, J.; Gao, J.; Chen, T.; Swennen, R. Widespread development of bedding-parallel calcite veins in medium–high maturity organic-rich lacustrine shales (Upper Cretaceous Qingshankou Formation, Northern Songliao Basin, NE China): Implications for hydrocarbon generation and horizontal compression. Mar. Pet. Geol. 2023, 158, 106544. [Google Scholar] [CrossRef]
  45. Luo, T.; Guo, X.; He, Z.; Dong, T.; Tao, Z.; Yang, R.; Wang, K. Fluid evolution and gas enrichment in the Wufeng-Longmaxi shale reservoirs of the eastern Sichuan Basin, SW China. J. Asian Earth Sci. 2024, 259, 105905. [Google Scholar] [CrossRef]
  46. Schowalter, T.T.; Hess, P.D. Interpretation of Subsurface Hydrocarbon Shows. AAPG Bull. 1982, 66, 1302–1327. [Google Scholar]
  47. Ablimiti, Y.; He, W.; Li, N.; Ma, D.; Liu, H.; Bian, B.; Ding, X.; Jiang, M.; Wang, J.; Cao, J. Mechanisms of deep oil–gas accumulation: New insights from the Carboniferous Central Depression, Junggar Basin, China. Front. Earth Sci. 2022, 10, 987822. [Google Scholar] [CrossRef]
  48. Hu, H.Y.; Zhao, Z.; Lu, S.; Xiao, H.Z. Regional Seal as Key Controlled Elements of Petroleum Accumulation in the Rift Basin. Adv. Mater. Res. 2012, 524, 190–193. [Google Scholar] [CrossRef]
  49. Jahn, B.; Wu, F.; Chen, B. Massive granitoid generation in Central Asia: Nd isotope evidence and implication for continental growth in the Phanerozoic. Episodes 2000, 23, 82–92. [Google Scholar] [CrossRef]
  50. Windley, B.F.; Alexeiev, D.; Xiao, W.; Krooner, A.; Badarch, G. Tectonic models for accretion of the Central Asian Orogenic Belt. J. Geol. Soc. 2007, 164, 31–47. [Google Scholar] [CrossRef]
  51. Ji, D.; Xu, Y.; Pang, Z.; Yuan, B.; Zhao, K. Classification of Typical Tectonic Styles and Exploration of Hydrocarbon Accumulation Patterns in Southern Junggar Basin, China. Energies 2022, 15, 6715. [Google Scholar] [CrossRef]
  52. Zhu, Z.; Li, Q.; Chen, H.; Li, J.; Zhang, W.; Liu, Y.; Yan, Z. Tectonic-geomorphological evolution and provenance-sedimentary response: Insights from the Middle Jurassic–Lower Cretaceous, Junggar Basin, China. Mar. Pet. Geol. 2023, 158, 106514. [Google Scholar] [CrossRef]
  53. Shen, Z.; Yu, F.; Wang, Q.; Zhang, J.; Xue, Y. Discrete element method simulation of the fold-and-thrust belts along strike various compression in the southern margin of the Junggar Basin, China. Mar. Pet. Geol. 2022, 145, 105849. [Google Scholar] [CrossRef]
  54. Wang, Y.; Jia, D.; Pan, J.; Wei, D.; Tang, Y.; Wang, G.; Wei, C.; Ma, D. Multiple-phase tectonic superposition and reworking in the Junggar Basin of northwestern China-Implications for deep-seated petroleum exploration. AAPG Bull. 2018, 102, 1489–1521. [Google Scholar] [CrossRef]
  55. Liu, K.; Qu, J.; Zha, M.; Liu, H.; Ding, X.; Zhou, M.; Gao, T. Genesis Types and Migration of Middle and Lower Assemblages of Natural Gas in the Eastern Belt around the Penyijingxi Sag of the Junggar Basin, NW China. Processes 2023, 11, 689. [Google Scholar] [CrossRef]
  56. Tao, K.Y.; Cao, J.; Chen, X.; Hu, W.X.; Shi, C.H. Deep hydrocarbons in the northwestern Junggar Basin (NW China): Geochemistry, origin, and implications for the oil vs. gas generation potential of post-mature saline lacustrine source rocks? Mar. Pet. Geol. 2019, 109, 623–640. [Google Scholar] [CrossRef]
  57. Liu, K.; Eadington, P.; Middleton, H.; Fenton, S.; Cable, T. Applying quantitative fluorescence techniques to investigate petroleum charge history of sedimentary basins in Australia and Papuan New Guinea. J. Pet. Sci. Eng. 2007, 57, 139–151. [Google Scholar] [CrossRef]
  58. Li, Z.; Jiang, Z.; Li, F. Quantitative Grain Fluorescence Responds to Residual Oil Zones and Paleo-Oil Zones. Spectrosc. Spectr. Anal. 2012, 32, 3073–3077. [Google Scholar]
  59. Liu, K.; Lu, X.; Gui, L.; Fan, J.; Gong, Y.; Li, X. Quantitative Fluorescence Techniques and Their Applications in Hydrocarbon Accumulation Studies. Earth Sci. 2016, 41, 373–384. [Google Scholar]
  60. Ma, W.J.; Wei, Y.Z.; Li, X. Accumulation process and control factors of Jurassic-Cretaceous distant-source and secondary-filled reservoirs in the hinterland of Junggur Basin. Acta Sci. Nat. Univ. Pekin. 2018, 54, 1195–1204. [Google Scholar]
  61. Hou, M. Phase Origin and Evolution of Deep-Seated Oil and Gas in the Western Central Depress of Junggar Basin. Ph.D. Thesis, China University of Petroleum (East China), Qingdao, China, 2022; p. 145. (In Chinese). [Google Scholar]
  62. Meng, Y.; Wang, J.; Ma, W. Evaluation of Hydrocarbon Source Rock Characteristics of Lower Permian Fengcheng Formation in Mahu Sag Based on Fine Grained Sedimentary Rock Type. J. Jilin Univ. (Earth Sci. Ed.) 2022, 52, 1735–1746. [Google Scholar]
  63. Mackenzie, A.S.; Leythaeuser, D.; Schaefer, R.G.; Bjorøy, M. Expulsion of petroleum hydrocarbons from shale source rocks. Nature 1983, 301, 506–509. [Google Scholar] [CrossRef]
  64. Nádudvari, Á.; Fabiańska, M.J. The impact of water-washing, biodegradation and self-heating processes on coal waste dumps in the Rybnik Industrial Region (Poland). Int. J. Coal Geol. 2016, 154, 286–299. [Google Scholar] [CrossRef]
  65. Teige, G.M.G.; Hermanrud, C.; Thomas, W.H.; Wilson, O.B.; Bolås, H.M.N. Capillary resistance and trapping of hydrocarbons: A laboratory experiment. Pet. Geosci. 2005, 11, 125–129. [Google Scholar] [CrossRef]
  66. Bennett, B.; Buckman, J.O.; Bowler, B.F.J.; Larter, S.R. Wettability alteration in petroleum systems: The role of polar non-hydrocarbons. Pet. Geosci. 2004, 10, 271–277. [Google Scholar] [CrossRef]
  67. Wang, W.; Lin, C.; Zhang, X.; Dong, C.; Ren, L.; Lin, J. Diagenesis, Diagenetic Facies and Their Relationship with Reservoir Sweet Spot in Low-Permeability and Tight Sandstone: Jiaxing Area of the Xihu Sag, East China Sea Basin. Minerals 2023, 13, 404. [Google Scholar] [CrossRef]
  68. Anderson, R.N.; He, W.; Hobart, M.A.; Wilkinson, C.R.; Nelson, H.R. Active fluid flow in the Eugene Island area, offshore Louisiana. Lead. Edge 1991, 10, 12–17. [Google Scholar] [CrossRef]
  69. Caillet, G. The caprock of the Snorre Field, Norway: A possible leakage by hydraulic fracturing. Mar. Pet. Geol. 1993, 10, 42–50. [Google Scholar] [CrossRef]
  70. Hao, F.; Zhu, W.; Zou, H.; Li, P. Factors controlling petroleum accumulation and leakage in overpressured reservoirs. AAPG Bull. 2015, 99, 831–858. [Google Scholar] [CrossRef]
  71. Sibson, R.H. Structural permeability of fluid-driven fault-fracture meshes. J. Struct. Geol. 1996, 18, 1031–1042. [Google Scholar] [CrossRef]
  72. Moldowan, J.M.; Dahl, J.; Zinniker, D.; Barbanti, S.M. Underutilized advanced geochemical technologies for oil and gas exploration and production-1. The diamondoids. J. Pet. Sci. Eng. 2015, 126, 87–96. [Google Scholar] [CrossRef]
  73. Jeffrey, H.S.; Sassen, R. Evidence for Large-Scale Vertical and Lateral Migration of Formation Waters, Dissolved Salt, and Crude Oil in the Louisiana Gulf Coast; GeoScienceWorld: Alexandria, VA, USA, 1990; Volume 9, pp. 283–296. [Google Scholar]
  74. Cartwright, J.; Huuse, M.; Aplin, A. Seal bypass systems. AAPG Bull. 2007, 8, 1141–1166. [Google Scholar] [CrossRef]
  75. Ingram, G.M.; Urai, J.L.; Naylor, M.A. Sealing processes and top seal assessment. Nor. Pet. Soc. Spec. Publ. 1997, 7, 165–174. [Google Scholar]
  76. Wang, P.; Shen, Z.; Liu, S. The Geochemical Characteristics and Comparison of the Terrestrial Natural Gas in Sichuan Basin. Nat. Gas Geosci. 2013, 24, 1186–1195. [Google Scholar]
  77. Krooss, B.M.; Leythaeuser, D.; Schaefer, R.G. Light hydrocarbon diffusion in a caprock. Chem. Geol. 1988, 71, 65–76. [Google Scholar] [CrossRef]
  78. Løseth, H.; Gading, M.; Wensaas, L. Hydrocarbon leakage interpreted on seismic data. Mar. Pet. Geol. 2009, 26, 1304–1319. [Google Scholar] [CrossRef]
  79. Roberts, S.J.; Nunn, J.A. Episodic fluid expulsion from geopressured sediments. Mar. Pet. Geol. 1995, 12, 195–204. [Google Scholar] [CrossRef]
  80. Liu, Y.; Zeng, J.; Qiao, J.; Chen, D.; Li, S.; Dong, Y.; Liu, S.; Long, H. Integration of charging time, migration pathways and sealing analysis to understand hydrocarbon accumulation in complex fault blocks, the Pinghu Slope Belt of the Xihu Depression, East China Sea Basin. Mar. Pet. Geol. 2023, 152, 106241. [Google Scholar] [CrossRef]
  81. Lafargue, E.; Thiez, P.L. Effect of water washing on light ends compositional heterogeneity. Org. Geochem. 1996, 24, 1141–1150. [Google Scholar] [CrossRef]
  82. Zhang, M.; Zhang, J. Effect of water washing on hydrocarbon compositions of petroleum sandstone reservoir rocks in Tarim Basin, NW China. Chin. J. Geochem. 2000, 19, 167–175. [Google Scholar] [CrossRef]
Geosciences 15 00215 g002
Figure 2. Schematic diagram of experimental flow. (a) Mudstone samples; (b) disaggregation and cleaned samples; (c) Cary Eclipse grain fluorescence meter.
Figure 2. Schematic diagram of experimental flow. (a) Mudstone samples; (b) disaggregation and cleaned samples; (c) Cary Eclipse grain fluorescence meter.
Geosciences 15 00215 g002
Geosciences 15 00215 g003
Figure 3. Schematic diagram of QGF spectral parameters [32].
Figure 3. Schematic diagram of QGF spectral parameters [32].
Geosciences 15 00215 g003
Geosciences 15 00215 g004
Figure 4. Spectrograms of the mudstones by TSF analysis.
Figure 4. Spectrograms of the mudstones by TSF analysis.
Geosciences 15 00215 g004
Geosciences 15 00215 g005
Figure 5. Mineral composition of deep-seated mudstone in the Central Depression of the Junggar Basin. (a) Mineral composition; (b) clay composition.
Figure 5. Mineral composition of deep-seated mudstone in the Central Depression of the Junggar Basin. (a) Mineral composition; (b) clay composition.
Geosciences 15 00215 g005
Geosciences 15 00215 g006
Figure 6. SEM observation results of deep-seated mudstone in the Central Depression of the Junggar Basin. (a,b) P2, the interior of intergranular pores is filled with chlorite lining and authigenic feldspar; (c) P3, organic matter filling inside dissolution pores at the edge of feldspar; (d,e) M2, microcracks distributed around grains, intragranular dissolution pores are distributed on the surface of feldspar or muscovite; (f) M3, microcracks between muscovite crystals, microcracks between illite crystals. Por: intergranular porosity; Mic: microcracks; Fsp: feldspar; Sec: secondary dissolution porosity; OM: organic matter; Chl: chlorite; Ms: muscovite; iPro: intergranular porosity; Ill: illite.
Figure 6. SEM observation results of deep-seated mudstone in the Central Depression of the Junggar Basin. (a,b) P2, the interior of intergranular pores is filled with chlorite lining and authigenic feldspar; (c) P3, organic matter filling inside dissolution pores at the edge of feldspar; (d,e) M2, microcracks distributed around grains, intragranular dissolution pores are distributed on the surface of feldspar or muscovite; (f) M3, microcracks between muscovite crystals, microcracks between illite crystals. Por: intergranular porosity; Mic: microcracks; Fsp: feldspar; Sec: secondary dissolution porosity; OM: organic matter; Chl: chlorite; Ms: muscovite; iPro: intergranular porosity; Ill: illite.
Geosciences 15 00215 g006
Geosciences 15 00215 g007
Figure 7. Cross plot of TSF R1 and TSF R2 for mudstone samples.
Figure 7. Cross plot of TSF R1 and TSF R2 for mudstone samples.
Geosciences 15 00215 g007
Geosciences 15 00215 g008
Figure 8. QGF spectrum of reservoir samples in the Junggar Basin (water zone data from [32]. (a) Well PD1 in Mobei Bulge; (b) well MS1 in Mosuowan Bulge.
Figure 8. QGF spectrum of reservoir samples in the Junggar Basin (water zone data from [32]. (a) Well PD1 in Mobei Bulge; (b) well MS1 in Mosuowan Bulge.
Geosciences 15 00215 g008
Geosciences 15 00215 g009
Figure 9. Line chart of parameters for mudstone samples with different grain sizes. (a) Content deviation line chart; (b) QGF E intensity line chart.
Figure 9. Line chart of parameters for mudstone samples with different grain sizes. (a) Content deviation line chart; (b) QGF E intensity line chart.
Geosciences 15 00215 g009
Geosciences 15 00215 g010
Figure 10. Cross plot of pyrolysis parameters of mudstone in the Junggar Basin (Plate a from [64]; Plate b from [62]). (a) Tmax vs. HI; (b) TOC vs. S1.
Figure 10. Cross plot of pyrolysis parameters of mudstone in the Junggar Basin (Plate a from [64]; Plate b from [62]). (a) Tmax vs. HI; (b) TOC vs. S1.
Geosciences 15 00215 g010
Geosciences 15 00215 g011
Figure 11. Cross plot of TOC and fluorescence parameters of mudstone in the Central Depression of the Junggar Basin. (a) TOC vs. QGF index; (b) TOC vs. QGF ratio.
Figure 11. Cross plot of TOC and fluorescence parameters of mudstone in the Central Depression of the Junggar Basin. (a) TOC vs. QGF index; (b) TOC vs. QGF ratio.
Geosciences 15 00215 g011
Geosciences 15 00215 g012
Figure 12. Evidence of hydraulic fracturing in the Permian caprock of well MS1. (a) Micro-resistivity logging images of the caprock (part); (b,c) vertical variation of C29 Sterane ratio with different configurations (data from Hou, 2022, p. 145 [61]).
Figure 12. Evidence of hydraulic fracturing in the Permian caprock of well MS1. (a) Micro-resistivity logging images of the caprock (part); (b,c) vertical variation of C29 Sterane ratio with different configurations (data from Hou, 2022, p. 145 [61]).
Geosciences 15 00215 g012
Geosciences 15 00215 g013
Figure 13. Vertical variation of Permian fluorescence parameters of well PD1 in Mobei Bugle, the Junggar Basin. (a) The conclusion of oil testing; (b) QGF E intensity value of the sample; (c) QGF index value of the sample; (d) the maturity of the sample, with a circle representing QGF λmax and a triangle representing TSF R1; solid points are mudstone samples, while hollow points are reservoir samples; C: Condensate, N: Normal–light crude oil, M: Medium–heavy crude oil, H: Heavy crude oil.
Figure 13. Vertical variation of Permian fluorescence parameters of well PD1 in Mobei Bugle, the Junggar Basin. (a) The conclusion of oil testing; (b) QGF E intensity value of the sample; (c) QGF index value of the sample; (d) the maturity of the sample, with a circle representing QGF λmax and a triangle representing TSF R1; solid points are mudstone samples, while hollow points are reservoir samples; C: Condensate, N: Normal–light crude oil, M: Medium–heavy crude oil, H: Heavy crude oil.
Geosciences 15 00215 g013
Geosciences 15 00215 g014
Figure 14. Vertical variation of fluorescence parameters of well MS1 in Mosuowan Bugle, the Junggar Basin. (a) The conclusion of oil testing; (b) QGF E intensity value of the sample; (c) QGF index value of the sample; (d) the maturity of the sample, with a circle representing QGF λmax and a triangle representing TSF R1; solid points are mudstone samples, while hollow points are reservoir samples; C: Condensate, N: Normal–light crude oil, M: Medium–heavy crude oil, H: Heavy crude oil.
Figure 14. Vertical variation of fluorescence parameters of well MS1 in Mosuowan Bugle, the Junggar Basin. (a) The conclusion of oil testing; (b) QGF E intensity value of the sample; (c) QGF index value of the sample; (d) the maturity of the sample, with a circle representing QGF λmax and a triangle representing TSF R1; solid points are mudstone samples, while hollow points are reservoir samples; C: Condensate, N: Normal–light crude oil, M: Medium–heavy crude oil, H: Heavy crude oil.
Geosciences 15 00215 g014
Table 1. Statistics of test categories conducted on all samples in this study.
Table 1. Statistics of test categories conducted on all samples in this study.
Sample SourceWell PD1Well MS1
NumberP1–P8P9–P12M1–M26M27–M30
Sample depth4930.2–5261.9 m5266.59–5322 m6379–6726 m6999.4–7140 m
LithologyMudstoneSandstoneMudstoneSandstone and conglomerate
X-ray diffraction analysisP1–P3, P8–M1–M3, M23–
TOCP3–P8–M4–M26–
Rock-Eval––M4–M21–
SEMP2–M3–
Fluorescence parametersP1–P8P9–P12M1–M26M27–M30
– Means the test is not conducted.
Table 2. Standard cleaning procedure for samples [32].
Table 2. Standard cleaning procedure for samples [32].
DisaggregationSample Core or Cuttings
Disaggregate to Single Grains
Mineral separationElectromagnetic concentration of quartz grains from sample if required
DCM 10 min ultrasound bath in 20 mL of HPLC-grade DCM
H2O2 (10%)1 h digestion at room temperature in 40 mL of H2O2, including 20 min of ultrasound bathing at beginning and the end of the digestion
HCl (3.6%)20 min digestion in 40 mL of HCl at room temperature
DCM 10 min ultrasound bath in 20 mL of HPLC-grade DCM, with DCM extract used for QGF E analysis
Table 3. Pyrolysis parameters of mudstone in the Central Depression of the Junggar Basin.
Table 3. Pyrolysis parameters of mudstone in the Central Depression of the Junggar Basin.
No.Depth (m)TOC (wt%)Tmax (°C)S1 (mg/g)S2 (mg/g)HI (mg/g TOC)
M463790.374530.230.3491.89
M563930.464460.60.3371.74
M664320.552980.430.5192.73
M764570.433040.290.3990.7
M864760.914072.350.8189.01
M964862.184321.693.19146.33
M1064921.184250.40.8572.03
M1165200.823150.490.7793.9
M126543.860.694710.070.3144.93
M1365660.554270.250.2952.73
M1465760.523110.080.2446.15
M1565970.574890.070.235.09
M1666320.334110.460.55166.67
M1766460.44250.10.1947.5
M1866680.054450.060.2400
M1966800.554970.050.1425.45
M2067000.474610.050.1940.43
M2167900.884370.190.7787.5
M2264021.83————
M236542.260.74————
M246544.230.88————
M2565840.74————
M2667260.73————
P44930.20.06————
P550420.33————
P65130.80.19————
P75131.60.09————
P85261.90.98————
Notes: Integer depth indicates a rock cuttings sample; otherwise, it is the core sample; — Means no data.
Table 4. Statistics of fluorescence parameters of mudstone samples with different grain sizes in the Junggar Basin.
Table 4. Statistics of fluorescence parameters of mudstone samples with different grain sizes in the Junggar Basin.
No.MeshWeight (g)After Cleaning (g)Weight Loss Rate (%)Content Deviation (%)QGF E
λ (nm)		QGF E Intensity (pc)
P860–801.24861.20273.6847.26374230.66
80–1002.54252.43524.2239.45374184.33
100–1200.81150.75826.575.77375226.85
120–1400.64160.59217.7210.69374237.05
140–1800.41570.3837.8712.86376241.61
180–2001.61651.056734.63396.85378286.16
M2360–801.87421.466521.7551.2044976.13
80–1001.99741.422528.7835.4545777.06
100–1200.71760.394745.000.89372109.31
120–1401.38050.853438.186.27372104.79
140–1800.28030.118857.6229.17370161.59
180–2002.08290.870458.2130.51375131.88
Note: Content deviation = Abs ((weight-loss rate − clay content)/clay content × 100).
Table 5. Test results of fluorescence quantitative technology.
Table 5. Test results of fluorescence quantitative technology.
No.WellDepth
(m)		QGF ParameterQGF E ParameterTSF Parameter
QGF
Ratio		QGF IndexQGF λmax (nm)QGF E IntensityQGF λ (nm)R1R2
P4PD14930.211.91.6426.393751.271.64
P5PD1504210.6418.533.53622.083.55
P6PD15130.83.31.3474.511.83571.181.82
P7PD15131.62.25.2418.473611.562.04
P8PD15261.92.68.4413.5226.853703.44.87
P9PD15266.593.116.1420.7215.43752.914.15
P10PD152822.25.1426.7323.53746.289.14
P11PD153021.23.9429.7165.63684.97.37
P12PD153220.65.6395.5124.13685.218.71
M22MS164021.32.1436.3145.43681.552.47
M23MS16542.2612.2486.8109.313752.432.91
M24MS16544.230.13.245656.43703.143.68
M25MS165848.74.5504.9122.53652.423.45
M26MS167261.22.6454.479.43621.82.6
M27MS16999.42.88.9497.811.13560.881.43
M28MS170330.52499.320.13663.815.36
M29MS17094417.450787.5367——
M30MS171400.457.5479.761.8369——
Table 6. Correspondence between fluorescence parameters and crude oil maturity [39,60].
Table 6. Correspondence between fluorescence parameters and crude oil maturity [39,60].
MaturityCondensateNormal to LightMedium to HeavyHeavy
TSF R1<2.02.0–3.0>3.0/
QGF λmax350–400 nm400–450 nm450–550 nm>550 nm
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Qu, J.; Liu, K.; Liu, H.; Zhou, M.; Ding, X.; Zha, M. Applying Quantitative Fluorescence Techniques to Investigate the Effectiveness of Deep-Seated Mudstone Caprocks in the Junggar Basin, NW China. Geosciences 2025, 15, 215. https://doi.org/10.3390/geosciences15060215

AMA Style

Qu J, Liu K, Liu H, Zhou M, Ding X, Zha M. Applying Quantitative Fluorescence Techniques to Investigate the Effectiveness of Deep-Seated Mudstone Caprocks in the Junggar Basin, NW China. Geosciences. 2025; 15(6):215. https://doi.org/10.3390/geosciences15060215

Chicago/Turabian Style

Qu, Jiangxiu, Keshun Liu, Hailei Liu, Minghui Zhou, Xiujian Ding, and Ming Zha. 2025. "Applying Quantitative Fluorescence Techniques to Investigate the Effectiveness of Deep-Seated Mudstone Caprocks in the Junggar Basin, NW China" Geosciences 15, no. 6: 215. https://doi.org/10.3390/geosciences15060215

APA Style

Qu, J., Liu, K., Liu, H., Zhou, M., Ding, X., & Zha, M. (2025). Applying Quantitative Fluorescence Techniques to Investigate the Effectiveness of Deep-Seated Mudstone Caprocks in the Junggar Basin, NW China. Geosciences, 15(6), 215. https://doi.org/10.3390/geosciences15060215

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop