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Article

Distribution of Remaining Oil and Enhanced Oil Recovery Strategy for Carboniferous Buried-Hill Reservoirs in Junggar Basin

by
Qijun Lv
1,2,
Zhaowen Shi
3,*,
Linsong Cheng
1 and
Chunjing Zan
1
1
College of Geosciences, China University of Petroleum, Beijing, Beijing 102249, China
2
Xinjiang Oilfield Heavy Oil Company, Karamay 834000, China
3
Chengdu Branch of Daqing Oilfield Exploration and Development Research Institute, Chengdu 610051, China
*
Author to whom correspondence should be addressed.
Energies 2025, 18(10), 2474; https://doi.org/10.3390/en18102474
Submission received: 11 April 2025 / Revised: 30 April 2025 / Accepted: 3 May 2025 / Published: 12 May 2025
(This article belongs to the Collection Flow and Transport in Porous Media)
Browse Figures
Versions Notes

Abstract

:
The Carboniferous reservoirs in the northwestern margin of the Junggar Basin exhibit complex lithological assemblages, primarily composed of siltstone, sandy conglomerate, tuff, and igneous rocks. These reservoirs are rich in oil and gas resources but have entered the middle to late stages of development. The reservoir spaces in the Carboniferous system are mainly composed of pores and fractures, resulting in a complex storage system. To provide effective strategies for stabilizing and enhancing production during the middle to late development stages, it is essential to establish a dual-porosity and dual-permeability model based on a clear understanding of lithological distribution patterns. This will facilitate the identification of favorable zones and the proposal of effective development strategies through numerical simulation. The present study systematically identified the lithology of the study area through microscopic lithological identification combined with logging data, conducted reservoir matrix property research under facies constraints, and established a three-dimensional geological model of lithology and physical properties. To more reasonably study the reservoir development process and establish an optimal development plan, a machine learning model for fracture density was trained using imaging logging interpretation results and conventional logging curve data. The model was then utilized to calculate single-well fracture density. Finally, a fracture model of the study area was established based on the collaborative constraints of fracture density and three-dimensional seismic attributes. Using the results of the established dual-porosity and dual-permeability model and production data, reservoir production evaluation and residual oil distribution research were conducted. The results indicate that the southwestern part of the study area features thick sandy conglomerate reservoirs with good physical properties, continuous lateral distribution, and high residual oil content, making it a dominant area favorable for horizontal well development and production. Additionally, reservoir numerical simulation was employed to study enhanced production development strategies. It is recommended to adopt gas–water alternating injection to improve production, with the optimal gas–water injection ratio of 4:1 yielding the maximum reservoir recovery factor. This study provides theoretical and technical support for the development of complex lithologic buried-hill reservoirs in the Carboniferous system of the western margin of the Junggar Basin.

1. Introduction

The efficient development and effective utilization of unconventional oil and gas are important components for ensuring energy security in the future [1,2]. With the increasing intensity of oil and gas exploration and development, as well as the continuous advancement of engineering technology, volcanic rock oil and gas reservoirs have been discovered and successfully explored [3,4,5]. The characteristics and distribution of volcanic rocks are closely related to the formation of oil and gas reservoirs. They can act as both cap rocks and reservoir rocks, playing an important role in the formation and migration of oil and gas, and are currently a key direction in oil and gas exploration and development [6,7,8,9]. The Carboniferous volcanic rock oil reservoir in the Kebai fault zone on the northwest margin of the Junggar Basin, as the earliest discovered volcanic rock oil reservoir in China, is a buried-hill volcanic rock oil reservoir with dual media of fractures and pores, controlled jointly by faults and weathering crusts. For reservoirs controlled by weathering crusts, some studies believe that the area 300 m below the weathering crust interface is an unfavorable area for the exploration and development of volcanic rock oil reservoirs [10,11,12]. However, the Kebai fault oil reservoir is mainly concentrated in the shallow to medium-depth zones close to the top of the Carboniferous weathering crust, which is an advantageous area for volcanic rock oil and gas exploration and development.
The Carboniferous system in the study area has complex lithology and well-developed faults, resulting in stronger reservoir heterogeneity [13,14,15]. Despite recent advancements and improvements in technologies and methods for volcanic rock reservoir description, there are still many challenges and uncertainties in characterizing fractured reservoirs, and the detailed characterization of complex lithology reservoirs remains a key challenge limiting the improvement of exploration and development efficiency for such reservoirs. Since the development of the Kebai fault Carboniferous reservoir, the output of oil and gas has remained consistently high [16]. However, in recent years, the oil production rate in most blocks has declined significantly. Analysis has revealed that the reservoir primarily faces three issues. First, there is an unclear understanding of the reservoir characteristics and spatial distribution, making it difficult to identify favorable reservoirs and implement targeted development strategies. Second, the reservoir has been developed using a depletion method for years [17], relying on natural formation production without any pressure maintenance measures, leading to a rapid decline in formation pressure, a significant drop in initial well production, and a swift transition to a low-production stage, resulting in low overall oil recovery efficiency. Third, as the reservoir enters the middle to late development stage, the distribution pattern of the remaining oil in the study area is unclear. In recent years, measures such as large-scale layer addition and well pattern densification have been implemented, but with poor results, a high proportion of low-production wells, and insufficient research on fracture development patterns, the effect of alternating gas and water injection is also not significant. Therefore, it is necessary at this stage to study the reservoir characteristics and spatial seepage characteristics of the study area, clarify the distribution patterns of lithology, fractures, and remaining oil, and formulate targeted remaining oil potential tapping strategies and reservoir development plans.
To address the aforementioned challenges and research objectives, this study conducts a detailed reservoir characterization and numerical simulation of the Carboniferous buried-hill reservoir in the northeastern Kebai fault zone of the Junggar Basin. By integrating 3D geological modeling with fundamental geological data, we aim to accurately characterize the Carboniferous reservoir. Furthermore, reservoir numerical simulation technology will be employed to calibrate and establish a model that aligns with the actual reservoir seepage conditions. This model will then be used to simulate dynamic development scenarios, thereby optimizing the reservoir development plan and enhancing recovery efficiency. The findings of this study will provide valuable insights and strategic guidance for the exploration and development of volcanic rock oil reservoirs in the Junggar Basin. Additionally, the results may serve as a reference for the efficient development of other tight and fractured oil and gas reservoirs with similar characteristics.

2. Geological Setting

The study area is located on the northwestern margin of the Junggar Basin, within the upthrown block of the Kebai fault zone(Figure 1). Its southern boundary connects to the Ke-Wu Fault and the Baijiantan South Fault, adjacent to the Mahu Sag, while its northern side links to the Zaire Mountains via the Jiuqian-25 Well Fault and the Xibaibai Boundary Fault. The area is delineated by the locations of single wells and oil-bearing boundaries, confined to the northeastern block bounded by four boundary faults. The study area can be divided into three sub-regions: the B905 block in the north, the Gu-16 Well block in the center, and the J-451 Well block in the south. The northern and central blocks are characterized by two thrust faults—the J22 Fault and the G40 Fault—whereas the southern block shows no fault development. The total area of the study region covers approximately 34 km2.

3. Data and Methodology

3.1. Reservoir Rock Characterization Study

This study characterizes the reservoir rock features of the Carboniferous system in the research area by integrating core samples, thin sections, scanning electron microscopy (SEM), and well-log data. Building on previous research, we classify the carbonate reservoir rock types, analyze their key characteristics, and establish lithostratigraphic well-log identification charts and classification schemes. These results provide a robust foundation for three-dimensional geological modeling.

3.2. Reservoir Properties and Oil-Bearing Characteristic Study

This study employed core samples, thin-section preparation, SEM analyses, and physical property measurements to investigate the storage space characteristics and fracture development features of carbonate reservoirs. Variations in physical properties among different rock types were systematically analyzed. By integrating core analysis data, well-log interpretation results, and the reservoir’s “four-characteristics” relationships (lithology, petrophysics, fluid flow, and geomechanics), we determined the oil-bearing properties of distinct reservoir units and established precise identification criteria for the Carboniferous oil-bearing layers in the study area. Building on a comprehensive understanding of the reservoir’s fundamental characteristics and fluid flow behavior—along with regional geological settings and production data—this study analyzed the key controlling factors of favorable carbonate reservoirs. The findings facilitate the prediction of productive zones within the Carboniferous system, providing valuable guidance for future oil and gas exploration and development in the region.

3.3. Fracture Study

In this study, the types, quantities, and characteristics of fractures were identified based on the observation of core and imaging logging, and the distribution of fractures in different lithologies was statistically analyzed. However, there are few core and imaging logging data in the study area, and only 11 wells are available, which limited the subsequent fracture modeling research. However, due to the complex lithology and rapid spatial change in the study area, lithological change is often misunderstood as fracture development, so the fracture response characteristics of the other five conventional logs are added while retaining the lateral logging, which is used for the subsequent fine fracture identification of the whole area. The identification of fractures is transformed into the classification of fracture density curves, and the study of fracture density and its quantitative characterization in the study area is conducted using the random forest machine learning method. The random forest algorithm, proposed by Breiman et al., is an ensemble learning technique based on multiple decision trees. This algorithm generates numerous decision tree models by training on a known dataset, enabling accurate sample prediction. The final prediction results are determined through a voting mechanism among all decision trees [18,19]. This method has achieved significant success in lithology and fracture identification [20,21,22,23,24]. Previous studies have extensively explored intelligent fracture identification techniques. For example, the GA-SVM method was used to predict fractures in carbonate reservoirs in the Ordos Basin [25], and BP neural networks were applied to construct a fracture-filling interpretation model based on the logging response characteristics of fracture cavity fillings, both yielding notable results [26,27]. In this study, we used single-well fracture density derived from imaging logging as the prediction target and conventional logging curves as feature variables. A random forest simulation was implemented in Python 3.12 to achieve accurate fracture prediction.

3.4. Distribution of Remaining Oil

Based on the well coordinates in the study area, a computational program was developed to calculate the single-well control area. The well coordinates were directly converted into polygon files representing the drainage range of each well, allowing for the generation of a planar map of single-well control areas in Petrel 2018. After determining the single-well control areas, the model was used to estimate geological reserves for each well. By analyzing both the cumulative production of individual wells and the geological reserves under their control, the reservoir utilization efficiency was calculated. This analysis provided a planar distribution map of remaining oil reserve abundance in the Carboniferous reservoirs of the study area.

3.5. Three-Dimensional Geological Model and Reservoir Numerical Model

Using a comprehensive dataset—including seismic data, well-log information, and geological stratification—we employed Petrel 2018 software to construct three-dimensional geological and fracture development models for the study area. These models elucidate the fluid flow characteristics and spatial distribution patterns of fractures within the carbonate reservoirs. By integrating insights from reservoir control factor analysis, we identified favorable reservoir fluid flow zones and characterized their lateral distribution. To optimize reservoir management and enhance hydrocarbon recovery, this study focused on determining the spatial distribution of reservoir fluids while incorporating current development status and remaining oil distribution data. Suitable areas were selected for numerical simulation to validate model accuracy and simulate optimal development strategies. This approach provides valuable insights for improving recovery efficiency and guiding field development strategies in the study area’s Carboniferous reservoirs. The research methodology is illustrated in Figure 2.

4. Results

4.1. Lithology

The Carboniferous buried-hill reservoir in the study area forms the basement of the basin, having undergone prolonged deep burial followed by multiple episodes of intense tectonic compression and uplift. Subsequent weathering and leaching alteration, combined with multi-stage intermittent volcanic eruptions in the region, have resulted in a complex lithofacies distribution with significant spatial heterogeneity. Previous studies indicate that the volcanic rocks in the Junggar Basin were primarily formed during the Carboniferous period, with mafic varieties being dominant [28].
The volcanic rocks in the Carboniferous strata of the study area include andesite, basalt, dolerite, and medium-basic volcanic tuff (Figure 3a,b), among which andesite and basalt are the predominant types (Figure 4a,b). Andesite is typically gray or gray–green in color and features a porphyritic texture, sometimes with vesicular or amygdaloidal structures partially filled with calcite. Its mineral composition is dominated by plagioclase feldspar, with subordinate amphibole and biotite (Figure 4c,e). In terms of geophysical properties, andesite exhibits moderate-to-low natural gamma ray values (25–44 API), medium resistivity, and a relatively low acoustic time difference (56–75 μs/ft). Basalt, on the other hand, is primarily dark gray or brownish in color and typically displays fine-grained to cryptocrystalline textures. It is composed mainly of plagioclase and pyroxene (Figure 4d). Basalt shows extremely low natural gamma ray values (11–35 API), moderate-to-high resistivity, and a relatively small acoustic time difference (47–69 μs/ft).
In the Carboniferous system of the study area, the tuffaceous rocks are primarily composed of tuff and mud-dominated volcanogenic deposits, the latter of which includes fine-grained lithologies such as tuffaceous siltstone and tuffaceous shale (Figure 4f,g). The tuff exhibits a yellow–gray to gray-brown color and is characterized by volcanic debris fragments (<2 mm) constituting over 50% of its composition. Mineralogically, it consists of quartz, feldspar, and clay minerals with subordinate volcanic debris, confirming its classification as tuff. Thin-section analysis reveals load structures (Figure 4h), suggesting rapid deposition under volcaniclastic and sedimentary influences. For this study, fine-grained deposits (e.g., tuffaceous mudstone and tuffaceous siltstone) are grouped under the broader tuff category (Figure 4i,j). These rocks display moderate fracture intensity and typically exhibit intermediate natural gamma ray values (44–65 API) coupled with high resistivity (>80 Ω·m). In contrast, the sand conglomerates in the study area predominantly comprise breccia, sandstone, and coarse-grained tuffaceous lithologies such as tuffaceous sandstone and tuffaceous breccia (Figure 4k,l). These deposits show moderate rounding and sorting, with mineral grains predominantly in linear contact. Their high rock fragment content—mainly volcaniclastic debris and minor chert—reflects low compositional maturity, classifying them as lithic sandstone (Figure 4m,n). Locally, significant carbonate cementation is observed, along with evidence of dissolution and mineral precipitation attributed to deep burial diagenesis under subsurface fluid influence (Figure 4o). Among the studied rock types, sand conglomerates exhibit the lowest fracture intensity, with most fractures being calcite-filled joints. Their natural gamma ray values range from moderate to high (>44 API), while resistivity is notably low (<80 Ω·m).

4.2. Reservoir Properties and Oil-Bearing Characteristics

The porosity distribution of volcanic rock reservoirs ranges from 0.1% to 18.73%, with a median of 1.74% and an average of 3.21%. The permeability distribution ranges from 0.01 mD to 486.96 mD, with a median of 0.17 mD and an average of 6.39 mD (Figure 5). Approximately 20% of the reservoirs have a porosity greater than 5%, and about 25% have a permeability greater than 1 mD. Volcanic rock reservoirs are characterized as ultra-low porosity and ultra-low permeability reservoirs.
The porosity distribution of tuff reservoirs ranges from 0.06% to 15.12%, with a median of 1.47% and an average of 2.85%. The permeability distribution ranges from 0.02 mD to 230.34 mD, with a median of 0.53 mD and an average of 2.77 mD (Figure 5). Approximately 15% of the reservoirs have a porosity greater than 5%, and about 35% have a permeability greater than 1 mD. Tuff reservoirs are also classified as ultra-low porosity and ultra-low permeability reservoirs.
The porosity distribution of sandy conglomerate reservoirs ranges from 0.15% to 20.98%, with a median of 3.3% and an average of 4.47%. The permeability distribution ranges from 0.02 mD to 200.41 mD, with a median of 0.76 mD and an average of 4.25 mD (Figure 5). Approximately 30% of the reservoirs have a porosity greater than 5%, and about 40% have a permeability greater than 1 mD. Sandy conglomerate reservoirs are also classified as ultra-low porosity and ultra-low permeability reservoirs.
Based on the statistical analysis of physical properties in the study area, the overall porosity distribution of the Carboniferous reservoirs ranges from 0.06% to 20.98%, with an average of 4.13%. The permeability distribution ranges from 0.01 mD to 486.96 mD, with an average of 3.79 mD. The reservoirs are classified as ultra-low porosity and ultra-low permeability. According to the analysis of different lithologies, sandy conglomerate reservoirs have the best physical properties, followed by volcanic rock reservoirs, while tuff reservoirs have the poorest physical properties. Therefore, sandy conglomerate and volcanic rock reservoirs are the dominant lithologies in the Carboniferous formation of the study area.
The oil-bearing grade of the core is an important index to evaluate the oil-bearing part of the reservoir, and the larger the proportion and the higher the grade of the oil-bearing part of the core, the better the oil-bearing part of the reservoir. According to the core oil-bearing statistics in the study area, all three major lithologies in the Carboniferous reservoirs of the Kebai fault zone show signs of oil and gas. For volcanic rock reservoirs, the lengths of cores with rich oil, oil stain, oil trace, and fluorescence account for 4.81%, 40.28%, 13.30%, and 41.61% of the total core length, respectively. For tuff reservoirs, the corresponding lengths account for 2.02%, 59.46%, 7.22%, and 31.30% of the total core length. For sandy conglomerate reservoirs, the lengths account for 4.00%, 36.50%, 14.36%, and 45.14% of the total core length. The total core length in the study area is 386.4 m, with an oil-bearing core length of 233.9 m (Table 1). Among them, the oil-bearing core length of the conglomerate accounted for 60.6% of the total core length, the volcanic rock accounted for 66.4%, the tuff accounted for 54.5%, and the oil-rich section accounted for 4%, 4.9%, and 2.1%, respectively. Therefore, the proportion of oil-bearing core sections and high oil-bearing grades of conglomerate and volcanic rocks is high, and they have better oil-bearing properties than tuff reservoirs.
This study employs Archie’s equation to estimate the oil saturation of the Carboniferous oil reservoirs in the target area. The equation establishes an empirical relationship between the formation resistivity factor (F), porosity (Φ), water saturation (Sw), and formation resistivity (Rt), serving as the foundation for quantitatively evaluating oil saturation based on well-logging data. Using well-logging data from 60 wells in the study area, cross-plots of the formation resistivity factor (F) versus porosity (Φ) and the resistivity index (I) versus water saturation (Sw) were generated. Through the analysis of these plots, the lithology-dependent coefficients, cementation exponent (m), and saturation exponent (n) were determined as follows (Table 2).
The formation water in the Carboniferous formation of the study area is relatively inactive. Analysis of the data shows that the total salinity of the formation water ranges from 376.1 mg/L to 22,208.4 mg/L, with a water type of CaCl2. However, the water production is minimal and not representative. Therefore, this study refers to the properties of the Carboniferous formation water in adjacent blocks and, considering the reservoir’s mid-depth temperature of 26.96 °C, calculates the formation water resistivity as 0.18 Ω·m. Consequently, Archie’s equation can be used to calculate the water saturation of the reservoirs in the study area, describe the oil-bearing properties, and lay the groundwork for establishing a three-dimensional model of attribute parameters. Based on the well-test results of the Carboniferous reservoirs in the study area, cross-plots of resistivity (Rt) versus porosity (Φ) were created to analyze and calculate the oil layer classification standards for different lithologies. The well-test conclusions classify oil layers and oil–water layers as oil layers, while dry layers and water layers are classified as non-oil layers. This classification determines the oil layer identification standards for different lithologies in the Carboniferous formation of the study area (Table 3). The lower limit of porosity for oil layers in volcanic rock and tuff reservoirs is 5%, while for sandy conglomerate reservoirs, it is 6%. The lower limit of oil saturation for all three lithologies is set at 40%.

4.3. Fracture Characteristics and Identification

According to the imaging logging data of the Carboniferous system in the study area, the fractures observed in the study area are mainly filled and unfilled fractures. Among the unfilled fractures, oblique, network, and high-angle fractures are predominant. In the fracture imaging logging data, the different types of fractures exhibit distinct characteristics. The oblique fractures typically appear as black sinusoidal curves, where a smaller height difference indicates a smaller dip angle (Figure 6a). The network fractures are characterized by irregular black lines intersecting each other, generally covering a larger area (Figure 6b). The filled fractures are characterized by a color consistent with the formation but with obvious discontinuities (Figure 6c). The high-angle fractures, which are oblique fractures with dip angles greater than 70 degrees, are characterized by a larger height difference in the black sinusoidal curves (Figure 6d). Additionally, there are a small number of straight fractures, which appear as nearly vertical black lines in the imaging logs and are less distributed in the study area. Furthermore, the observations of the fracture types from the core data of the Carboniferous system in the study area are consistent with the imaging logging results. These include the high-angle fractures (Figure 6e,f), network fractures (Figure 6g,h), oblique fractures (Figure 6i,j), filled fractures (Figure 6k,l), and straight fractures (Figure 6m,n). The Carboniferous reservoir in the study area contains a significant number of filled fractures, with calcite identified as the filling material through core observations. Fractures in the Carboniferous reservoir can be classified into two main categories: filled and unfilled fractures. Unfilled fractures serve as effective storage spaces in the reservoir. Therefore, unfilled fractures are further categorized into oblique, network, high-angle, and straight fractures, which are studied as effective storage spaces in the study area. Filled fractures are not the focus of this study.

4.4. Three-Dimensional Geological Model and Remaining Oil Distribution

4.4.1. Three-Dimensional Geological Model

Based on the analysis of the lithological modeling results, volcanic rocks account for 16.2% of the total lithology in the study area, while conglomerates and tuffaceous rocks account for 26.1% and 57.7%, respectively, with tuffaceous rocks being the most widely distributed. Spatially, the volcanic rocks are predominantly concentrated in the central area near the Gu-16 well, the conglomerates are mainly distributed in the southern area near the Jian-451 well, and other regions also exhibit small areas with significant thicknesses. In contrast, the tuffaceous rocks are extensively developed across the entire study area, with both substantial thickness and widespread distribution (Figure 7).
According to the single-well imaging logging image, the location and number of Carboniferous fractures in the target layer are identified, the identified data are converted into a continuous single-well fracture density curve through Petrel, and then the database of the random forest simulation algorithm is constructed according to the selected fracture logging response curve as the eigenvalue and the fracture density curve as the variable value; the basic parameter results of the simulation are shown in Table 4, which indicates that the fracture simulation results have a certain accuracy, so the results of the fracture density simulation in the whole area are used as the basic data of fracture modeling. The crack development intensity model in the study area can be obtained. According to the analysis of the fracture development intensity model, the average fracture density is 6.23 fractures per meter in the C1 layer, 6.05 fractures per meter in the C2 layer, 5.98 fractures per meter in the C3 layer, and 5.95 fractures per meter in the C4 layer. This indicates that the closer to the top of the Carboniferous system, the better the fracture development, suggesting a greater degree of reservoir modification (Figure 8).
The porosity of the Carboniferous oil reservoirs in the study area ranges from 5.3% to 16.1%, with an average of 9.2%. Vertically, the average porosity of the oil reservoirs is 10.8% in the C1 layer, 9.04% in the C2 layer, 8.78% in the C3 layer, and 8.68% in the C4 layer (Figure 9). This indicates that the closer to the top of the Carboniferous system, the greater the oil reservoir porosity, suggesting better reservoir storage performance. The spatial distribution of permeability in the Carboniferous system of the study area follows a similar trend to porosity. Specifically, the southern area near the Jian-451 well exhibits the highest permeability, and the permeability increases with proximity to the top of the Carboniferous system. The average permeability across the study area ranges from 0.2 mD to 5.56 mD, with an average of 0.95 mD (Figure 10).

4.4.2. Remaining Oil Distribution

Based on the cumulative production data of individual wells and the geological reserves controlled by each well in the study area, the degree of reservoir utilization was analyzed and calculated. This analysis yielded the planar distribution of the remaining oil reserve abundance in the Carboniferous reservoir (Figure 11). The results indicate that the remaining oil is primarily concentrated in the southern region near the J451 well, with a secondary concentration in the central area near the G16 well. These regions are thus identified as key targets for the subsequent development of the Carboniferous reservoir. The average abundance of the remaining oil reserves in the study area is 1.74 × 10⁶ t/km2 (or 174 × 10⁴ t/km2, if this unit is standard in your field).

5. Discussion

Based on the well distribution and production performance of the individual wells in the study area, this study selected the HW95110 well group in the central Gu-16 well block for reservoir numerical simulation. The three-dimensional attribute parameter model and fracture parameter model, previously established through geological modeling, were clipped and upscaled to generate the reservoir numerical simulation model for the HW95110 well group (Figure 12). Using the numerical simulation capabilities of Petrel RE, an integrated geological engineering reservoir development plan was simulated and analyzed. The grid step size of the numerical model is 50 m × 50 m × 1 m, the number of grids is 10 × 25 × 200 = 50,000, the area on the plane is about 0.5 km × 1.25 km = 0.625 km2, and the longitudinal thickness is about 200 m (divided into four small layers from C1 to C4); the model infiltration data are shown in Table 5.
Before conducting reservoir numerical simulation, it is essential to validate and adjust the established numerical simulation model. This involves performing production history matching for the Carboniferous reservoir in the study area. By iteratively adjusting and refining reservoir parameters based on the matching results, the dynamic simulation process is calibrated to closely approximate the actual reservoir development, thereby improving the accuracy and reliability of the numerical simulation. The initial geological reserves of the simulation area were estimated at 264.7 × 104 m3. After applying Net-to-Gross (NTG) model constraints, the geological reserves were revised to 115 × 104 m3. Prior to simulation, the cumulative production from wells within the area was 10.4 × 104 m3 (Table 6). Subsequently, production history matching was conducted for the simulation area. Both the cumulative oil production for the entire area and that of individual wells exhibited high matching rates (Figure 13 and Figure 14), with an overall matching rate exceeding 90%. These results demonstrate that the numerical simulation model accurately captures the fluid flow behavior of the reservoir in the study area.
After verifying the accuracy of the numerical model, we designed two development scenarios—gas–water alternating injection (with varying gas–water ratios) and depletion development—to simulate total oil production over the next year. The recovery efficiency of the reservoir under these different development conditions was then compared. The results demonstrate that the gas–water alternating development scheme yields better performance than the depletion development scheme. Specifically, when the injected gas–water ratio is 4:1, the HW95110 production well group within the simulation area shows the highest and most stable predicted oil production rate. Based on the simulation results, the expected oil production for this well after one year was calculated (Table 7).

6. Conclusions

This study systematically investigates Carboniferous reservoirs in the Well 230 area of the Kebai fault zone, Junggar Basin, through integrated geological modeling and numerical simulation. Lithological analysis indicates that the reservoirs are predominantly composed of volcanic rocks, conglomerates, and tuffaceous rocks, with the conglomerates exhibiting the most favorable reservoir properties. Fracture characterization identifies four dominant fracture types—oblique, network, high-angle, and straight fractures—as the primary storage spaces, with an average fracture density of 6.09 fractures per meter. Spatial distributions of porosity and permeability reveal that the southern Jian-451 well area possesses the highest reservoir quality, marking it as a key target for future development. Numerical simulations demonstrate that a gas–water alternating injection scheme (4:1 ratio) significantly enhances oil recovery compared to depletion methods. The integration of 3D geological modeling and reservoir simulation provides insights into the remaining oil distribution and optimizes development strategies, offering a theoretical and technical framework for the efficient exploitation of complex Carboniferous lithologic reservoirs in the Junggar Basin.

Author Contributions

Methodology, Z.S.; Software, C.Z.; Resources, Q.L.; Data curation, C.Z.; Writing—original draft, Q.L.; Writing—review & editing, Z.S. and L.C.; Visualization, Q.L.; Supervision, L.C.; Funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by [Science Foundation of China University of Petroleum, Beijing] grant number [2462023BJR011].

Data Availability Statement

The research data comes from oilfield companies, there may be leakage or confidentiality issues, so we have decided not to disclose the use of data.

Conflicts of Interest

Author Qijun Lv was employed by the company Xinjiang Oilfield Heavy Oil Company and Author Zhaowen Shi was employed by the company Chengdu Branch of Daqing Oilfield Exploration and Development Research Institute. 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.

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Figure 1. The location of the study area structure.
Figure 1. The location of the study area structure.
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Figure 2. The study technology roadmap.
Figure 2. The study technology roadmap.
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Figure 3. Carboniferous lithology identification plate in the study area. (a) Natural gamma-resistivity; (b) natural gamma-sonic time difference.
Figure 3. Carboniferous lithology identification plate in the study area. (a) Natural gamma-resistivity; (b) natural gamma-sonic time difference.
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Figure 4. Carboniferous cores and micrographs in the study area. (a) Andesite, Gu 103 well, 691.59 m; (b) basalt, Gu 40 well, 841.45 m; (c) andesite, Gu 99 well, 934.13 m, single deviation, cast thin section; (d) basalt, Gu 40 well, 841.45 m, single deviation, cast thin section; (e) andesite, ancient 103 well, 691.59 m, single deviation, cast thin section; (f) tuff, well 95403, 399.15 m; (g) tuff, Ancient 99 well, 934.13 m; (h) tuffy mudstone, Ancient 99 well, 732.7 m, single deviation, cast thin section; (i) tuffy mudstone, Ancient 103 well, 657.21 m, single bias, cast thin section; (j) tuffaceous siltstone, 95,403 well, 399.15 m, single deviation, cast thin section; (k) coarse sandstone, Gu 98 well, 880.93 m; (l) conglomerate coarse sandstone, 802 well, 2102.16 m; (m) coarse-grained lithic sandstone, Gu 98 well, 897.04 m, single bias, cast thin sections; (n) rock chip coarse sandstone, Bai 017 well, 464.6 m, single bias, cast thin section; (o) rock chip coarse sandstone, Bai 017 well, 632.9 m, orthogonal, cast thin section.
Figure 4. Carboniferous cores and micrographs in the study area. (a) Andesite, Gu 103 well, 691.59 m; (b) basalt, Gu 40 well, 841.45 m; (c) andesite, Gu 99 well, 934.13 m, single deviation, cast thin section; (d) basalt, Gu 40 well, 841.45 m, single deviation, cast thin section; (e) andesite, ancient 103 well, 691.59 m, single deviation, cast thin section; (f) tuff, well 95403, 399.15 m; (g) tuff, Ancient 99 well, 934.13 m; (h) tuffy mudstone, Ancient 99 well, 732.7 m, single deviation, cast thin section; (i) tuffy mudstone, Ancient 103 well, 657.21 m, single bias, cast thin section; (j) tuffaceous siltstone, 95,403 well, 399.15 m, single deviation, cast thin section; (k) coarse sandstone, Gu 98 well, 880.93 m; (l) conglomerate coarse sandstone, 802 well, 2102.16 m; (m) coarse-grained lithic sandstone, Gu 98 well, 897.04 m, single bias, cast thin sections; (n) rock chip coarse sandstone, Bai 017 well, 464.6 m, single bias, cast thin section; (o) rock chip coarse sandstone, Bai 017 well, 632.9 m, orthogonal, cast thin section.
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Figure 5. Porosity and permeability distribution map of different lithologies in the study area.
Figure 5. Porosity and permeability distribution map of different lithologies in the study area.
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Figure 6. Carboniferous fracture imaging logging and core photos in the study area. (a) Diagonal seams; (b) mesh seams; (c) filling seams; (d) high-angle seams; (e) Gu 40 well, 840.85 m, high-angle fracture; (f) Gu 40 well, 840.45 m, high-angle fracture; (g) 95,403 well, 406.96 m, reticulated fracture; (h) Jiuqian 17 well, 682.85 m, reticulated fracture; (i) Gu 98 well, 913.31 m, oblique fracture; (j) 802 well, 1644.14 m, oblique fracture; (k) J512 well, 494.47 m, filling joint; (l) Bai 017 well, 571 m, filling joint; (m) Bai 017 well, 621.13 m, straight splitting seam; (n) Jiuqian 17 well, 561.6 m, straight splitting joint.
Figure 6. Carboniferous fracture imaging logging and core photos in the study area. (a) Diagonal seams; (b) mesh seams; (c) filling seams; (d) high-angle seams; (e) Gu 40 well, 840.85 m, high-angle fracture; (f) Gu 40 well, 840.45 m, high-angle fracture; (g) 95,403 well, 406.96 m, reticulated fracture; (h) Jiuqian 17 well, 682.85 m, reticulated fracture; (i) Gu 98 well, 913.31 m, oblique fracture; (j) 802 well, 1644.14 m, oblique fracture; (k) J512 well, 494.47 m, filling joint; (l) Bai 017 well, 571 m, filling joint; (m) Bai 017 well, 621.13 m, straight splitting seam; (n) Jiuqian 17 well, 561.6 m, straight splitting joint.
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Figure 7. Carboniferous lithological plane distribution map of the study area.
Figure 7. Carboniferous lithological plane distribution map of the study area.
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Figure 8. Plane distribution of fracture density in each small Carboniferous layer in the study area. (C1): Planar distribution of crack density in (C1) layer; (C2): Planar distribution of crack density in (C2) layer; (C3): Planar distribution of crack density in (C3) layer; (C4): Planar distribution of crack density in (C4) layer.
Figure 8. Plane distribution of fracture density in each small Carboniferous layer in the study area. (C1): Planar distribution of crack density in (C1) layer; (C2): Planar distribution of crack density in (C2) layer; (C3): Planar distribution of crack density in (C3) layer; (C4): Planar distribution of crack density in (C4) layer.
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Figure 9. Plane distribution of porosity of each Carboniferous layer in the study area. (C1): Plane distribution of porosity in the (C1) layer; (C2): Plane distribution of porosity in the (C2) layer; (C3): Plane distribution of porosity in the (C3) layer; (C4): Plane distribution of porosity in the (C4) layer.
Figure 9. Plane distribution of porosity of each Carboniferous layer in the study area. (C1): Plane distribution of porosity in the (C1) layer; (C2): Plane distribution of porosity in the (C2) layer; (C3): Plane distribution of porosity in the (C3) layer; (C4): Plane distribution of porosity in the (C4) layer.
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Figure 10. Plane distribution of permeability of each Carboniferous layer in the study area. (C1): Planar distribution of permeability of (C1) layer; (C2): Planar distribution of permeability of (C2) layer; (C3): Planar distribution of permeability of (C3) layer; (C4): Planar distribution of permeability of (C4) layer.
Figure 10. Plane distribution of permeability of each Carboniferous layer in the study area. (C1): Planar distribution of permeability of (C1) layer; (C2): Planar distribution of permeability of (C2) layer; (C3): Planar distribution of permeability of (C3) layer; (C4): Planar distribution of permeability of (C4) layer.
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Figure 11. Plane distribution of abundance of remaining oil reserves.
Figure 11. Plane distribution of abundance of remaining oil reserves.
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Figure 12. Numerical simulation of the work area model of the study area.
Figure 12. Numerical simulation of the work area model of the study area.
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Figure 13. Fitting of the production history of the whole area of the digital model work area.
Figure 13. Fitting of the production history of the whole area of the digital model work area.
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Figure 14. HW95110 fitting of single-well production history.
Figure 14. HW95110 fitting of single-well production history.
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Table 1. Statistical table of core oil and gas display of Carboniferous reservoir at Kebai fault.
Table 1. Statistical table of core oil and gas display of Carboniferous reservoir at Kebai fault.
LithologyCore Length
(m)		Oil-Bearing Core Length (m)Rich in OilsOil SpotsOil StainsFluorescence
Core Length
(m)		Percentage (%)Core Length
(m)		Percentage (%)Core Length
(m)		Percentage (%)Core Length
(m)		Percentage (%)
Volcanic15.210.10.494.814.0740.281.3413.304.2041.61
Tuff17.89.70.202.025.7759.460.707.223.0431.30
Gravel353.4214.18.564.0078.1536.5030.7414.3696.6445.14
total386.4233.99.253.9587.9837.6232.7914.02103.8844.41
Table 2. Carboniferous reservoir with different lithologies and electrical parameters of Kebai fault.
Table 2. Carboniferous reservoir with different lithologies and electrical parameters of Kebai fault.
LithologyRelationalmnabNR
VolcanicF = 1.122φ−1.8051.805-1.122-320.91
I = 1.085SW−1.931-1.931-1.085600.91
TuffF = 1.057φ−1.8751.875-1.057-270.94
I = 1.041SW−1.937-1.937-1.0411440.98
GravelF = 0.907φ−1.8011.801-0.907-480.87
I = 1.027SW−1.845-1.845-1.0272770.99
Table 3. Identification criteria for different lithologic reservoirs of the Carboniferous system at the Kebai fault.
Table 3. Identification criteria for different lithologic reservoirs of the Carboniferous system at the Kebai fault.
LithologyΦ (%)So (%)Rt (Ω.m)
Volcanic5.04047
Tuff5.04080
Gravel6.04027
Table 4. Machine learning simulation error analysis.
Table 4. Machine learning simulation error analysis.
DatasetR2RMSEMSE
Training set0.821.181.39
Test set0.791.231.52
Table 5. Oil–water relative permeability data.
Table 5. Oil–water relative permeability data.
SWKrWKro
0.300.9
0.3500.68465
0.381250.000195310.56038
0.41250.00156250.4447
0.443750.00527340.33829
0.4750.01250.24206
0.506250.0244140.15722
0.53750.0421880.085582
0.568750.0669920.030258
0.60.10
110
Table 6. Numerical simulation of reservoir basic information.
Table 6. Numerical simulation of reservoir basic information.
Model Initial Reserves (104 × m3)NTG Constrained Reserves
(104 × m3)		Cumulative Production of Well Groups (104 × m3)
264.711510.4
Table 7. The expected oil production in the gas–water alternating digital model work area.
Table 7. The expected oil production in the gas–water alternating digital model work area.
Gas–Water RatioWater Injection Volume
(m3/d)		Injecting Nitrogen Gas
(m3/d)		Expected Oil Production (103 m3)
3:112.537508.9
4:11040009.42
5:1840008.87
Exhaustive mining8.01
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Lv, Q.; Shi, Z.; Cheng, L.; Zan, C. Distribution of Remaining Oil and Enhanced Oil Recovery Strategy for Carboniferous Buried-Hill Reservoirs in Junggar Basin. Energies 2025, 18, 2474. https://doi.org/10.3390/en18102474

AMA Style

Lv Q, Shi Z, Cheng L, Zan C. Distribution of Remaining Oil and Enhanced Oil Recovery Strategy for Carboniferous Buried-Hill Reservoirs in Junggar Basin. Energies. 2025; 18(10):2474. https://doi.org/10.3390/en18102474

Chicago/Turabian Style

Lv, Qijun, Zhaowen Shi, Linsong Cheng, and Chunjing Zan. 2025. "Distribution of Remaining Oil and Enhanced Oil Recovery Strategy for Carboniferous Buried-Hill Reservoirs in Junggar Basin" Energies 18, no. 10: 2474. https://doi.org/10.3390/en18102474

APA Style

Lv, Q., Shi, Z., Cheng, L., & Zan, C. (2025). Distribution of Remaining Oil and Enhanced Oil Recovery Strategy for Carboniferous Buried-Hill Reservoirs in Junggar Basin. Energies, 18(10), 2474. https://doi.org/10.3390/en18102474

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