Abstract
Carbonate reservoir has complex pore structures. At present, the influence of pore structure on water flooding mechanism of carbonate reservoirs is insufficient. In this article, a systematic workflow was designed in combination with scanning electron microscope, particle size, physical properties, and water flooding experiments to study the effect of pore structure on water flooding mechanism of fine-grained carbonate rocks. Due to the small particle size and strong heterogeneity, the acid fracturing operations, rather than hydraulic fracturing alone, are necessary to achieve increased production and reservoir reconstruction of carbonate reservoirs. Through this study, the mathematical model of reservoir physical parameters (permeability and porosity) was proposed, and the accuracy of the model was verified by comparing the simulated oil recovery with the experimental results. According to the comparison results, the experimental results are consistent with the simulation results, and their oil recovery efficiency is 33.62 and 31.87%, respectively. Finally, the effect of injection rate on oil production was discussed. It is shown that with the increase in injection rate, the output of displaced oil increases significantly. The cumulative oil production increases from 62.5 to 256.31 mL when the injection rate increases from 100 to 400 mL/min. The findings of this study can help for better understanding of the influencing factors and mechanisms of the development efficiency of carbonate reservoirs.
1 Introduction
With the rapid development of the global economy, the energy crisis has become increasingly prominent. However, at present, new energy such as solar energy cannot completely replace fossil fuels [1,2,3]. Development and utilization of conventional fossil fuels are still the focus of future energy-related investigations [4]. Conventional oil and gas resources are often in a state of continuous production reduction after long-term of production cycle [5,6]. Therefore, the exploration and development of unconventional oil and gas resources are of great significance in maintaining the security of global energy [7,8]. Among them, carbonate reservoirs are important unconventional resources, whose estimated resource reserves are 1.6 × 1012 tons [9]. Therefore, effective development of oil and gas resources from carbonate reservoirs can help ease the tense situation of global energy.
Nevertheless, the bedrock minerals of carbonate rocks are extremely vulnerable to corrosion and become carbonate solutions, resulting in complex pore structures that differ from other reservoirs [10]. It is dominated by short and wide pores, and there may be small karst caves [11,12,13]. Figure 1 shows a comparison of the pore structures of carbonate and sandstone reservoirs [14]. As observed in Figure 1a, distribution of pores within carbonate reservoir is discrete, and the existence of some microfissures communicates different pore clusters. However, the pores in sandstone distribute uniformly (Figure 1b). [15] Therefore, the heterogeneity of pore structure in carbonate reservoir is stronger than that of sandstone. Therefore, it is very necessary to simulate the process of water flooding in carbonate reservoirs.
![Figure 1
Schematic diagram of the pore structure of carbonate (a) and sandstone (b) reservoirs (modified after ref. [14]); (c) difference in fluid flow in carbonate and sandstone reservoirs.](/document/doi/10.1515/geo-2022-0477/asset/graphic/j_geo-2022-0477_fig_001.jpg)
Schematic diagram of the pore structure of carbonate (a) and sandstone (b) reservoirs (modified after ref. [14]); (c) difference in fluid flow in carbonate and sandstone reservoirs.
At present, some researchers have carried out relevant studies on the pore structures of carbonate reservoir, and some progress has been made. Li et al. [16] systematically studied the characteristics of pore structures in four types of carbonate reservoirs, and the main influencing factors of difference in pore structures were also investigated. It was found that the random distribution of various pores is an important factor affecting the pore structures of reservoirs. He et al. [17] studied the relationship between permeability and porosity in carbonate reservoirs. It was found that high permeability of the reservoir is related to the development of microfractures and caves. Wang and Fan [18] evaluated the variation characteristics of porosity and permeability of carbonate reservoirs caused by diagenesis. Rezaee et al. [19] established the relationship between permeability, porosity, and pore size through an artificial neural network approach. This study indirectly proved that the artificial neural network is effective in predicting the permeability of carbonate reservoirs.
Undoubtedly, these studies are of significance for the efficient development of oil and gas resources in carbonate reservoirs. Nevertheless, there are still two defects that exist in these investigations. First of all, most of the research studies on pore structures of carbonate reservoirs are qualitative studies, and it is difficult to establish relationships with development indexes. There are few research studies on water flooding mechanism based on a systematic workflow construction and microperspective. Second, there are few reports about the effect of pore structures of carbonate rocks on oil and gas production behavior. In this study, a systematic experimental workflow was established to characterize the microscopic pore structures (including mathematical modeling) of carbonate reservoirs and their effect on productivity. This study can facilitate subsequent productivity calculation and reservoir simulation of carbonate reservoirs.
In this study, the pore structures of carbonate reservoirs were analyzed by macroscopic and microscopic experiments. The effect of water injection operation on oil and gas development was explored through oil displacement experiments. At the same time, the effect of injection rate on oil production was also investigated. This work can provide reference for the efficient extraction and production of oil and gas resources in carbonate reservoirs.
The workflow of the investigation is shown in Figure 2. In Section 2, both the self-developed experimental system and experimental method were introduced. In Section 3, the rock properties were investigated, and modeling of porosity and permeability was carried out. Furthermore, the applicability of the proposed model was verified by the oil displacement experiments, and the influence of injection rate on oil displacement effect was analyzed.
Workflow of the study.
2 Experiments and methods
The particle size distribution of mineral particles was analyzed using a BT-9300ST laser particle size analyzer (purchased from Dandong Baxter Instruments Co.). This particle size analyzer can accurately measure the size of particle in the range of 0.1–1,000 μm. Moreover, the accuracy of the particle size analyzer is 1%, which can fully meet the requirements of particle size distribution measurement of most minerals.
In addition, the experiment system for simultaneous measurement of porosity and permeability was performed as shown in Figure 3.
(a) Experimental equipment for simultaneous measurement of porosity and permeability of rock samples and (b) diagram of the applied stress states.
Step-by-step measurements of porosity and permeability can lead to the deviation in experimental results. This is because the permeability and porosity measurement process will pose a threat to the integrity of the pore structures of rock samples. For this reason, the permeability and porosity should be measured simultaneously by placing the rock samples directly into the autoclave. The porosity and permeability were measured with the gas method to avoid pollution caused by the mercury-pressure method [20]. During the experiment, the airtightness of the device needs to be tested first. Then, the samples were placed into the autoclave and sealed with a rubber sleeve. At the same time, the porosity and permeability measurement system was activated, and the nitrogen was filled into the experiment system. The permeability and porosity can be measured simultaneously. Notably, the measurement accuracy of porosity and permeability is as high as 0.25% and 0.20 mD, respectively.
The principle of this experimental system for porosity measurement can be expressed as follows:
where φ is the porosity, %; V p is the pore volume of rock sample, m3; V t is the total apparent volume of rock sample, m3.
Similarly, the gas percolation during the measurement of permeability follows Darcy’s law exactly, and the Klinkenberg effect is neglected. Darcy’s law, which is the principle of the experimental system for permeability measurement, can be expressed as:
where Q is the infiltration rate per unit time; K is the permeability, mD; A is the effective cross-sectional area of rock sample, m2; ΔP is the pressure difference, MPa; l is the length of the rock sample, m.
3 Results
3.1 Experimental materials and mineral fractions
The six samples used in the experiments were taken from the AH-1 well in the AH Oil Field, and the sampling depth interval was between 1,325 and 1,342 m. Notably, particle size analysis of mineral grains needs to be performed after the simultaneous permeability and porosity measurement experiments. This is because particle size analysis will destroy the physical structure of the rock samples. Meanwhile, the nitrogen gas (99.9% purity) used in the experiments was purchased from Qingdao Southwest Drainage Gas Co. The physical characteristics of the six samples are shown in Table 1.
Rock sample characteristics and results of mineral analysis
| No. | Diameter (mm) | Density (g/cm3) | Height (mm) | Mineral composition (%) | |||||
|---|---|---|---|---|---|---|---|---|---|
| Quartz | Potassium feldspar | Plagioclase | Calcite | Dolomite | Clay | ||||
| 1 | 48.84 | 2.54 | 98.37 | 39 | 10 | 7 | 25 | 2 | 14 |
| 2 | 49.16 | 2.58 | 100.39 | 36 | 14 | 6 | 24 | 2 | 16 |
| 3 | 50.12 | 2.61 | 101.62 | 25 | 2 | 5 | 48 | 2 | 14 |
| 4 | 49.29 | 2.55 | 99.68 | 32 | 23 | 4 | 27 | 4 | 7 |
| 5 | 50.67 | 2.60 | 101.03 | 47 | 26 | 4 | 11 | 1 | 7 |
| 6 | 50.32 | 2.59 | 98.92 | 34 | 20 | 5 | 31 | — | 8 |
In addition, the experimental results of mineral analysis are given in Table 1. Only the percent contents of the main mineral components are given in Table 1. From Table 1, it is found that quartz, calcite, and potassium feldspar make up the major part of the mineral composition of the six rock samples. The proportion of the above three minerals is distributed between 70 and 90%. Plagioclase, dolomite, and clay are less in the six rock samples. The content of these three minerals only accounts for about 10% of the total mineral composition. It can be inferred from the experimental results that the cementation of the carbonate reservoir is achieved by the compaction of the surrounding rocks.
3.2 Characterization of particle size distribution and pore structure
Fine characterization of rock pore structure is a prerequisite for studying reservoir seepage mechanism [21,22,23]. The D50 and D90 data of the particle size distribution of the six rock samples are given in Table 2. From Table 2, we can find that the median particle sizes are between 30 and 50 μm, and the mineral particle is extremely fine. This is mainly due to the serious compaction of this layer during its geological formation process. Moreover, there were no violent plate tectonic movement and metamorphism in this process, and the mineral grains did not recrystallize [24]. The D90 of the six rock samples is mostly between 80 and 100 μm, which is also at a fine level.
Results of particle size analysis
| No. | D50 (μm) | D90 (μm) | Porosity (%) | Permeability (mD) |
|---|---|---|---|---|
| 1 | 49.94 | 98.59 | 13.2 | 8.56 |
| 2 | 34.55 | 87.75 | 12.6 | 7.98 |
| 3 | 33.83 | 84.65 | 11.1 | 7.52 |
| 4 | 34.91 | 95.57 | 12.8 | 8.84 |
| 5 | 34.88 | 88.84 | 16.3 | 10.61 |
| 6 | 33.11 | 91.70 | 15.2 | 10.39 |
Notes: D50: Particle size when the cumulative particle size distribution percentage reaches 50%. D90: Particle size when the cumulative particle size distribution percentage reaches 90%.
Finer carbonate mineral grains are not conducive to the formation of pores with high hydraulic conductivity, resulting in the poor property of carbonate reservoirs. The scanning electron microscope images of rock samples No. 3 and No. 5 are shown in Figure 4. From Figure 4(a), it can be found that the poor physical properties of rock sample No. 3 can be attributed to the fact that the pores are filled with dense cements. The porosity of the rock sample No. 3 is only 11.1%, which is the worst among the six rock samples. As a comparison, the clay content of the rock sample No. 5 in Figure 4(b) is only 7%, while the quartz content reaches 47%. The weak cement filling leads to the development of pores in this rock sample, which can be used as the seepage channel and storage space for oil and gas [25].
Microscopic pore structures of rock samples No. 3 (a) and No. 5 (b).
In a word, stimulation measures such as hydraulic fracturing are still required for the efficient development of carbonate reservoirs. To be precise, acid-fracturing technology is more suitable for reservoir stimulation of carbonate reservoirs [13].
3.3 Physical parameter modeling
It is of engineering significance to propose a mathematical model of the porosity and permeability of rock samples obtained from carbonate reservoirs. Based on the experimental results, the porosity of carbonate reservoir can be fitted as:
where φ 0 is the porosity in reservoir conditions, %. C Q and C c are the quartz and clay content in rock samples, respectively, %. a and b are the fitting constants, which are 0.324 and 2.631, respectively.
Similarly, rock permeability can be expressed as:
where c, d, and e are all the fitting coefficients (c is 0.023, d is 0.387, and e is 1.075). From equation (4), we can find that permeability can be expressed as a function of porosity. In this way, it was indirectly expressed as a function of the clay and quartz contents. The models shown in equations (3) and (4) can be used in the productivity analysis of carbonate reservoirs.
4 Discussion
Water injection and oil displacement experiments for carbonate reservoirs were carried out to verify the accuracy of the aforementioned fitted models (equations (3) and (4)). At the same time, the effect of injection rate on oil and gas production in carbonate reservoirs was explored in this section.
4.1 Experimental system for water injection and repulsion
As shown in Figure 5, the experimental system used for oil displacement in carbonate reservoirs mainly consists of a core holder, an air compressor, a water injection system, and a waste water treatment system. Among them, the core holder is a cylinder made of 316 L stainless steel, whose wall thickness is 5.0 mm. The core holder can withstand pressure up to 100 MPa, which fully meets the experimental requirements herein. In addition, the role of the air compressor is to restore the subsurface environment in reservoir. The air compressor can achieve a maximum confining pressure of 30 MPa on the sample with an accuracy of 0.10 MPa. The water injection system is realized by an advection pump, and the injection medium is clear water with a certain amount of surface activator added. Notably, the surface activator used in the experiment is sodium dodecyl sulfate (SDS). The advection pump is capable of injecting the displacing fluid at a constant flow rate of up to 2 L/min with an adjustment accuracy of 10 mL/min. The waste water treatment system consists of a beaker and an oil–water separator. Among them, the beaker is used to receive the waste displacing fluid in experiment. However, the oil–water separator is a hydrocyclone used in the separation of oil–water mixture.
Experimental system of carbonate injection and replacement.
The natural carbonate cores are first prepared as the standard samples (50 mm in diameter and 100 mm in height). Then, the core samples are placed in a core holder and loaded with surrounding pressure. At the same time, water with a certain concentration of surface activator is injected into the system. Finally, the waste displacement fluid is collected, and oil–water separation is performed for production (capacity) calculation. It should be noted that the injection rate is 200 mL/min, and the SDS concentration in the displacement fluid is 50 ppm. These are only the default data, and they should be adjusted when sensitivity analysis is performed.
4.2 Displacement effect of water injection and productivity
Figure 6 displays the evolution curves of oil production and oil–water ratio in the experiments. As observed in Figure 6, the oil production gradually increases as the displacement experiment continues. However, the production rate of oil is gradually decreasing. The oil production in the first 12 h was 75.25 mL. However, it was only about 20 mL in the last 12 h, which was about one-fourth of the oil production in the first 12 h. This is mainly due to the fact that the oil saturation in the core decreases as the experiment continues [12,26,27]. Moreover, the oil displaced by water at the beginning of the experiment was in well-flowing pores and throats, and it was easily displaced. However, as the experiment continues, the oil and gas in the core gradually become some crude oil sealed in pores with poor conductivity. It can also be seen in Figure 6 that the oil–water ratio decreases as the experiment continues. This indicates that the proportion of crude oil in produced mixture is gradually decreasing during the experiment, while this proportion of water is gradually increasing. At the beginning of the experiment, the ratio between oil and water was as high as 17.9, indicating that the majority of the produced fluid was crude oil. However, as the experiment continues, oil saturation in pore space within the reservoir gradually decreases. The Klinkenberg effect appears, and the fingering phenomenon becomes prominent. By the end of the experiment, the ratio between oil and water in the produced fluid was only 4.81, which was 26.87% of that at the beginning of the experiment.
Evolution curves of oil production and oil–water ratio during water injection and replacement experiments.
Another purpose of this experiment is to verify the applicability and accuracy of the porosity and permeability models proposed herein (see equations (3) and (4)). The recovery value of crude oil was then obtained from the productivity simulation by taking equations (3) and (4) into account. The comparison with the experimental results (oil recovery) is shown in Table 3. Notably, all simulation conditions are all same with the experimental results.
Comparison of simulation and experimental results
| Category | Time (h) | |||
|---|---|---|---|---|
| 4.0 | 8.0 | 12.0 | 24.0 | |
| Simulation | 12.23 | 22.31 | 30.22 | 33.62 |
| Experiment | 11.34 | 21.08 | 29.11 | 31.87 |
We can see from Table 3 that although the oil recovery obtained from the simulations at any moment is all slightly higher than that of the experiments, the difference is small. All these results indicate that the influence of mineral composition on pore structures should be considered when the productivity for carbonate reservoir was calculated. As a comparison, the productivity of carbonate reservoirs was also analyzed without considering the influence of mineral composition on pore structures. It was found that the final crude oil recovery was only about 27%, which was much lower than the experimental results. The comparison results shown in Table 3 indicate that the influence of mineral composition on pore structures needs to be considered when the productivity of carbonate reservoir was calculated.
4.3 Influence of injection flow rate on capacity
Based on the proposed pore structure (i.e. porosity and permeability) model, an analysis was conducted for the effect of injection rate on the productivity calculation of carbonate reservoirs. The results are shown in Figure 7. In this section, the injection rate was assumed as 100, 200, 300, and 400 mL/min, respectively. As observed in Figure 6, both the cumulative oil production and production rate increase significantly with the increasing injection rate. The cumulative oil production is only 62.71 mL when the injection flow rate is 100 mL/min. However, the final cumulative oil production increased to 75.25, 136.96, and 256.31 mL when the injection rate was 200, 300, and 400 mL/min, respectively. Therefore, it can be concluded that the oil recovery of water flooding in carbonate reservoir can be improved by properly increasing the injection rate.
Histogram of productivity analysis when injection rate is different.
However, it is not always possible to achieve a better displacement effect by simply increasing the injection rate. The schematic diagram of oil washing effect in porous media when injection rate is different is displayed in Figure 8. From Figure 8(a), we can see that the crude oil droplets in pores between the sediment particles are less impacted by the injected water when the injection rate is small. The oil droplets cannot be effectively stripped from the surface of the matrix particles. In this case, the recovery of crude oil is certainly lower and the cumulative crude oil production is not high. However, when the injection rate is high, the drag force of injected water on crude oil is extremely strong. In this case, the crude oil in the pores of porous media will be effectively flushed, which is manifested as higher crude oil production and recovery. Of course, the fingering effect will gradually appear and be strengthened with the increase in injection rate, and it will result in a poor oil displacement [28]. Therefore, there must be a limit injection rate value, which maximizes the water flooding efficiency. For this purpose, we expanded the injection rate to three groups: 500, 600, and 700 mL/min. Furthermore, it was found that when the water injection rate reached 400 mL/min under experimental conditions, the oil displacement effect reached the limit value.
Schematic diagram of oil washing effect under different water injection rates: (a) low water injection rate and (b) high water injection rate.
Through this study, the mathematical models of permeability and porosity are constructed on the basis of quantitative analysis. Moreover, a systematic experimental system of oil displacement in carbonate reservoirs associated with development dynamics has been established. However, this study also has some disadvantages: (1) The core represents a one-dimensional condition, which cannot restore the true reservoir state (3D), and (2) the factors affecting the water flooding effect of carbonate reservoir are complex, but due to the limitation of experimental conditions, the factors have not been fully considered. These shortcomings also need to be improved in the follow-up study.
5 Summary and conclusions
The main conclusions in this work are as follows:
In this study, the main mineral components in the carbonate samples are quartz, calcite, and potassium feldspar, the total content of which is more than 70%. However, the proportion of clay minerals is about 10%. The strong cementation of the carbonate reservoirs is related to the strong compaction of the surrounding rocks. Carbonate reservoir has poor percolation characteristics, and acidizing fracturing is an effective reservoir reconstruction measure to effectively extract oil and gas from carbonate reservoir.
Through this study, a model of reservoir physical parameters (permeability and porosity) was proposed, and the accuracy of the model was verified by comparing the simulated oil recovery with the experimental results. According to the comparison results, the experimental results are consistent with the simulation results, and the oil recovery efficiency is 33.62 and 31.87%, respectively.
The effect of injection rate on oil production was discussed. It is shown that with the increase in injection rate, the output of displaced oil increases significantly. The cumulative oil production increases from 62.5 to 256.31 mL when the injection rate increases from 100 to 400 mL/min.
In order to provide further support for efficient oil recovery in carbonate reservoirs, further studies will need to be carried out, such as the influence of other factors, such as displacement fluid properties, on oil displacement.
Nomenclature
- SDS
-
sodium dodecyl sulfate
- P z
-
axial pressure
- P c
-
confining pressure
- P p
-
pore pressure
- φ
-
porosity
- V p
-
pore volume of rock sample
- V t
-
total apparent volume of rock sample
- Q
-
infiltration rate per unit time
- K
-
permeability
- A
-
effective cross-sectional area of rock sample
- P
-
pressure
- ΔP
-
pressure difference
- l
-
length of the rock sample
- D50
-
particle size when the cumulative particle size distribution percentage reaches 50%
- D90
-
particle size when the cumulative particle size distribution percentage reaches 90%
- φ 0
-
porosity in reservoir conditions
- C Q
-
quartz content
- C C
-
clay content
- a–e
-
constants
Acknowledgement
This study was supported by the National Major Oil and Gas Projects of China “Key technologies for the development of large carbonate reservoirs in the Silk Road Economic Belt” (Item No.: 2017ZX05030).
-
Conflict of interest: This study was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
-
Data availability statement: The data that support the findings of this study are available from the corresponding author upon a reasonable request.
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