Potential Evaluation of CO2 Sequestration and Enhanced Oil

Article pubs.acs.org/EF

Potential Evaluation of CO2 Sequestration and Enhanced Oil Recovery of Low Permeability Reservoir in the Junggar Basin, China Huan Wang,* Xinwei Liao, Xiangji Dou, Baobing Shang, Heng Ye, Dongfeng Zhao, Changlin Liao, and Xiaoming Chen Petroleum Engineering Department, China University of Petroleum, Beijing 102249, People’s Republic of China ABSTRACT: The Chinese government is seeking CO2 gas emission reduction measures. CO2 capture and geological sequestration is one of the main measures. Injecting CO2 into oil reservoirs can not only achieve the environmental protection purpose of CO2 geological sequestration but also improve oil recovery and realize economic benefits, which helps to offset the cost of CO2 sequestration. Therefore, the oil reservoir is one of the best sites for CO2 sequestration. As for the reservoir of CO2 flooding after water flooding, there are two methods for evaluating the potential of CO2 enhanced oil recovery (EOR) and sequestration capacity, which are the mass balance method and analogy method. Through a combination of these two methods, this paper presents a new method, which can be reasonably used to evaluate these potentials. Besides, the screening criteria of CO2 sequestration and EOR in the Junggar Basin are also proposed. On the basis of the guidelines of CO2 source matching, reservoir characteristics, and fluid characteristic, four typical low permeability reservoirs (Caiman oil reservoir, Karamay oil reservoir, Beisantai oil reservoir, and Luliang oil reservoir) of the Junggar Basin are selected to study their potential of CO2 EOR and sequestration. And then the potential of CO2 EOR and sequestration capacity for the Junggar Basin oil reservoirs of CO2 flooding after water flooding is studied by applying the method mentioned above. For 275 development blocks of 24 oil fields in the Junggar Basin, 139 development blocks are suitable for CO2 miscible flooding EOR and sequestration, whereas 136 development blocks are suitable for CO2 immiscible flooding EOR and sequestration. The total EOR potential could be 18 407.76 × 104 t and the CO2 sequestration potential could amount to 47 486.0 × 104 t. The evaluation results show that the Junggar Basin’s oil reservoirs are suitable sites for CO2 EOR and sequestration and have great potentials. It can provide the decision basis for the future implementation of CO2 emission reduction projects in Western China.

1. INTRODUCTION There is a broad scientific consensus that global warming results primarily from increased concentrations of atmospheric greenhouse gases.1 The main greenhouse gases that cause climate change are CO2, CH4, N2O, HFCs, PFCs, and SF6. Among these gases, CO2 is the main culprit for the earth warming.2 Along with economic development and all-round social progress, China has become the world’s second largest economy and second largest CO2 gas emitter.3,4 As a result, China is seeking CO2 gas emission reduction measures. CO2 capture and geological storage is one of the main measures, and reservoirs, deep saline aquifers, and coal beds are the main places of CO2 geological storage.5−14 Oil reservoirs can provide safe geologic traps for CO2 storage. Injecting CO2 into oil reservoirs can not only achieve the goal of emission reduction, but also improve oil recovery and ensure the satisfactory economic benefits, which can reduce the cost of CO2 storage. Therefore, storing CO2 in a reservoir is a preferred method. The Chinese government attaches great importance to CO2 capture and storage research and supports many national basic research programs of China (973 Program) and the Chinese national major science and technology (863 Program), which have conducted a lot of mechanism and laboratory experiments and carried out field tests in Jilin, Shengli, Daqing, and other oil fields. Hao et al. used slim tubule tests and PVT experiments to investigate the mechanism of CO2 flooding.15 Zhang et al. used a mass balance method, which considers different trapping states of CO2 in oil reservoirs and aquifers, to assess the © 2014 American Chemical Society

potential of CO2 storage in oil reservoirs associated with large aquifers.16 Zhou et al. conducted preliminary assessments on the effective CO2 storage capacity in the Pearl River Mouth Basin (PRMB) offshore Guangdong.17 Vincent et al. used published methods to evaluate the CO2 storage potential for Dagang oil field and Shengli oil field.18 Su et al. introduced China’s first field-scale reservoir demonstration project in Jilin oil field and evaluated its performance with respect to both EOR and carbon storage.19 The content of this study is supported by the 973 Program “Carbon Dioxide Emission Reduction, Storage and Resource Utilization”. The Junggar Basin is an important oil-bearing basin in China. Good geologic trap and complete injection-production facilities enable it to be a promising location for CO2 geological storage in China. Thus, evaluating CO2 effective storage and EOR capacity in the Junggar Basin is of practical significance. On the basis of current capacity calculation methods for CO2 storage in oil reservoir, this paper proposed a new method, which is appropriate for evaluating CO2 effective storage capacity and EOR in China. The calculation method used in the paper is different from the mass balance method and analogy method. Through a combination of the two methods, this paper presents a new method to evaluate the EOR effects and sequestration potential of CO2. The new method is elaborated, Received: December 20, 2013 Revised: April 24, 2014 Published: April 24, 2014 3281

dx.doi.org/10.1021/ef4025068 | Energy Fuels 2014, 28, 3281−3291

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storage capacity is close to the practical storage capacity, and it is meaningful for the primary evaluation of low permeability oil reservoirs in a whole basin. This paper focuses on the evaluation of CO2 effective storage capacity in the Junggar Basin, thus the effective storage capacity evaluation models are mainly discussed. Many researchers in different countries and organizations have studied the calculation method for CO2 effective storage capacity. All the research methods can be divided into two categories, the mass balance method and the analogy method. On the basis of the calculation method of theoretical storage capacity, the Carbon Sequestration Leadership Forum (CSLF) proposed a calculation method based on mass balance, which considers buoyancy, gravity override, mobility contrast, heterogeneity, water saturation, and aquifer strength.21

and the background of the proposal is presented. Furthermore, the paper conducts a completed case study, which is based on a lot of laboratory experiments, geological data, geologic modeling, and numerical simulation. Considering low permeability oil reservoirs in the Junggar Basin are mainly developed by water flooding, the development effects of flooding would become worse in the high water cut stage. CO2 flooding is an effective method to slow down the decline of development effects, so it would be used to improve oil recovery after water flooding. On the basis of the factors mentioned above, this paper focuses on the potential evaluation of CO2 storage and EOR of low permeability oil reservoirs after water flooding in the Junggar Basin.

2. CALCULATION MODELS FOR CO2 EFFECTIVE STORAGE CAPACITY IN OIL RESERVOIRS Classification for CO2 storage capacity can be described by the resource pyramid proposed by McCabe in 1998. The CO2 storage capacity includes theoretical storage capacity, effective storage capacity, practical storage capacity, and matched storage capacity (see Figure 1).20 Theoretical storage capacity, which

MCO2e = Cm × C b × C h × Cw × Ca × MCO2t ≡ Ce × MCO2t

(1)

Where MCO2e is the CO2 effective storage capacity in an oil reservoir (t), MCO2t is the CO2 theoretical storage capacity in an oil reservoir (t), Ce is the effective storage coefficient influenced by comprehensive factors, subscripts m, b, h, w and a stand for mobility contrast, buoyancy, heterogeneity, water saturation, and aquifer strength, respectively. The advantage of this method is to consider relative comprehensive influence factors of CO2 storage, whereas the disadvantage is that each effective storage coefficient cannot be easily obtained. Each of the coefficients can be calculated by numerical simulation, which is heavy and complicated work. Equation 1 can be used to calculate the CO2 effective storage capacity in both depleted oil reservoirs and CO2 flooding oil reservoirs. The analogy method is another calculation method based on the actual data of CO2 EOR projects, thus this method is mainly used for CO2 flooding reservoirs. The United States and European Union have conducted in-depth research for this method. This method calculates the effective storage capacity by introducing a CO2 utilization factor.22

Figure 1. Techno-economic resource-reserve pyramid for CO2 sequestration capacity (CSLF, 2005, Bradshaw et al., 2007).

occupies the whole of resource pyramid, represents the physical limit of what the geological system can accept. The effective storage capacity, which is a subset of theoretical capacity, is obtained by applying a range of technical cutoff limits to a storage capacity assessment, including the consideration of the part of theoretical storage capacity that can actually be physically assessed. Effective storage capacity is the CO2 storage capacity considering reservoir properties, sealing of reservoir, storage depth, pressure system of reservoir, and pore volume. The capacity, which is influenced by fluidity, gravitational differentiation effect, reservoir heterogeneity, and formation water body, is always smaller than the theoretical one. Practical storage capacity, a subset of the effective capacity, is determined by considering technical, legal, and regulatory conditions of a certain country or area, infrastructure, and general economic limit to CO2 geological storage. Matched storage capacity is the subset of practical capacity, the value is obtained by the detailed matching of large stationary CO2 sources with geological storage sites that are adequate in terms of capacity, injectivity, and supply rate. When calculating the effective storage capacity, some factors are taken into account, which are buoyancy, gravity override, mobility contrast, heterogeneity, water saturation, and so on. The effective

MCO2e = NP × R CO2

(2)

Here, MCO2e is the CO2 effective storage capacity in an oil reservoir (t), NP is the extra oil obtained by CO2 injection (m3); RCO2 is the CO2 utilization factor, which equals to the ratio of the net CO2 injection amount to oil production amount (t/m3). The method has the advantage of a simple calculation. Although, the disadvantage is that the CO2 utilization coefficient has little correlation with crude oil and reservoir properties and cannot form the corresponding laws. By applying the CO2 flooding data of seven Permian sedimentary basins, Stevens proposes an empirical relationship between oil gravity and extra oil recovery due to the injection of CO2.23 The empirical relationship shows that original oil in place (OOIP) can be determined with the following formula if the final recoverable reserves and oil gravity are known. OOIP =

URR × 100 API + 5

(3)

Here, URR is the final recoverable reserves (109 m3) and API is the crude oil gravity (°API). 3282


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3. EVALUATION METHOD FOR CO2 EFFECTIVE STORAGE CAPACITY IN OIL RESERVOIRS Great differences of oil fields exist between China and other countries due to specific sedimentary environments, reservoir forming conditions, and oil properties. The low permeability reservoirs of China are continental deposits, which is different from marine deposits of abroad.24 Generally speaking, they have complicated structures and strong anisotropy. The physical property of low permeability reservoirs is poor with low-porosity and extra-low-permeability. In China, the low permeability reservoirs are usually developed by water flooding, most of them have reached at the high water cut stage, and the crude oil is close to the black oil. So, calculation models that are suitable for oil reservoirs in some literature cannot be applied to the Junggar Basin directly.21,22 Collection difficulty and accuracy of key parameters for capacity calculations are very critical to determine the reasonable evaluating model. OOIP, a key parameter, can be obtained easily and accurately for each reservoir in Chinese oil fields. Then on the basis of the mass balance method and analogy method, a new method to calculate the CO2 effective storage capacity is proposed in this paper. On the basis of eq 2 (MCO2e = NP × RCO2), where NP = N × Roil. N is the OOIP and Roil is the oil recovery by CO2 injection, so MCO2e = N × Roil × RCO2. As mentioned above, considering the weakness of eq 2, the oil recovery factor and CO2 utilization factor are calculated, respectively, which is in a complicated manner. Furthermore, in this study, we found that merging the oil recovery factor with the CO2 geological storage coefficient can show the good relationship with oil properties and reservoir properties.25 By introducing CO2 storage coefficient Rs, which considers some influence factors (such as buoyancy, gravity override, mobility contrast, heterogeneity, and water saturation) by using the idea of mass balance as the same as eq 1, the model is as follows.26,27 MCO2e = N × R s

Figure 2. Selecting schematic diagram of the typical reservoir in a basin.

recovery factor can be obtained. Then, by using the analogy method, the capacity of other reservoirs can be determined by “borrowing” these important parameters from the typical reservoir based on the law of similarity. Finally, the potential evaluation of CO2 effective storage capacity and EOR for the whole basin can be completed.

4. CO2 EOR AND STORAGE POTENTIAL EVALUATION OF TYPICAL RESERVOIRS 4.1. Geological Properties of the Junggar Basin. The Junggar Basin is located in north of the Xinjiang Uygur Autonomous Region, China, shown in Figure 3.28 The area of the basin is 13.487 × 104 km2. The Junggar Basin is a large superposition basin, which is mainly composed of later Paleozoic, Mesozoic, and Cenozoic continental deposits based on a Precambrian crystal basement, late Proterozoic era, and Paleozoic platform sedimentary face. Then, a composite and superimposed basin is formed by tectonic activities. The basin has experienced Hercynian, Indo-Chinese, Yanshan, and Himalayan orogeny periods. The multiple types of structural combination and sedimentary system are the results of the polycyclic sedimentary tectonic evolution. Sedimentary lithofacies of this basin include diluvia facies, fluvial facies, delta facies, lake facies, and volcanic facies. Integrated sedimentary layers of the Junggar Basin are developed from middle-late period of the Permian to Quaternary, and the maximum thickness of which could be 1.6 × 104 m. Currently, six oilbearing series and formations including Carboniferous, Permian, Triassic, Jurassic, Cretaceous, and Tertiary have been found. The main oil-bearing series are Triassic, Jurassic, Cretaceous Permian, and Carboniferous. Tectonics of some oil fields are monoclines controlled by faults (e.g., Karamay and Baikouquan oil fields), anticlines (Cainan, Huoshaoshan, Beisantai, and Dushanzi oil fields) that are intact or simply cut by faults, complex fault blocks (Hongshanzui and Xiazijie oil fields), etc. The reservoir buried depth varies from about 300 to 4500 m (Shinan oil field). The pore structure of the reservoir is complicated, and the porosity and permeability are low, most of which are less than 20% and 50 mD, respectively. Besides, the plane and vertical heterogeneities of the reservoirs are strong. In the Junggar basin, the oil and water distribution of a handful of reservoirs (Huoshaoshan, Xiazijie, etc.) is relatively complex due to the influence of structure, lithology, and fracture. And these reservoirs have edge-bottom waters and gas caps. Although, in most of the oil fields, the oil and water distributions are simple and the fluid properties are much better (Karamay,

(4)

Where MCO2e is the CO2 effective storage coefficient in the oil reservoir (t), N is the OOIP for the oil reservoir (t), Rs is the CO2 geological storage coefficient, which equals to the dimensionless ratio of CO2 storage amount to oil production amount. How to determine Rs is the key to calculate the CO2 effective storage coefficient. Generally, this coefficient can be determined empirically from completed CO2 EOR and storage projects or numerical simulations. Considering few of those projects have been carried out in China as well as large differences between the geological characteristics and fluid properties of China and abroad, this paper mainly determines Rs with numerical simulations based on some experiments. This method can be used for a small reservoir as well as a whole basin to evaluate the CO2 effective storage capacity and EOR. When a certain reservoir is evaluated, an average storage coefficient can be obtained from numerical simulation on several typical well groups in different locations of the reservoir, and then the average value can be applied to the whole reservoir. When the capacity for a whole basin is calculated, it is impossible to build geological models and conduct numerical simulations for every reservoir in this basin, thus several typical reservoirs should be selected (see Figure 2). By conducting simulations on models of these typical reservoirs, important parameters such as the CO2 effective storage coefficient and oil 3283


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Figure 3. Location of the Junggar Basin (modified from Jia et al., 2012).

Figure 4. Typical reservoirs and CO2 emission sources map in the Junggar Basin.

Baikouquan, and Cainan oil fields, etc.), which have no edgebottom waters and gas caps. Therefore, in this paper, only the amount of CO2 that is buried into the reservoir, including the CO2 that exists as free gas and dissolves into the remaining oil and reservoir water during CO2 flooding, is taken into consideration to evaluate the potential of CO2 effective storage capacity, while the paper does not consider the CO2 that dissolves into the edge-bottom water of the reservoir. 4.2. Typical Oil Reservoirs Selection in the Junggar Basin. The exploration and development of the Junggar basin date back to the 1930s. By the end of 2013, there was a total of 25 proven oil fields in the basin (see Figure 4), including 9 oil fields in the northwest (Karamay, Honshanzui, Baikouquan, Wuerhe, Fengcheng, Xiazijie, Mabei, Xiaoguai, and Chepaizi), 7 oil fields in the east (Huoshaoshan, Beisantai, Santai, Ganhezi, Shanan, Shabei, and Dishuiquan), 3 oil fields in the south

(Dushanzi, Qigu, and Kayindike), and 6 oil fields in the central basin (Shixi, Cainan, Luliang, Mobei, Mosuowan, and Shinan). On the basis of the deposition, reservoir and fluid characteristics of the Junggar basin, and the matching relation of the location between the target oil reservoir and the source gas, the selection standards of typical reservoirs are determined. (1) When CO2 flooding is conducted in an oil reservoir, fewer heavy components of crude oil that means a higher possible EOR. So, when the candidate oil reservoir is selected, the amount of heavy components, especially resin and asphaltene of the crude oil, should be as little as possible. (2) The candidate oil reservoir should be near the gas source to reduce the CO2 transport costs. (3) The candidate oil reservoir should have good sealing with no fracture development, or else the CO2 will leak to cause greater environmental pollution. 3284


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Table 1. Statistics of CO2 Emission Sources Surrounding a Typical Reservoir in the Junggar Basin distance (km)

oil field

company category

25

Cainan Karamay Beisantai Luliang Cainan Karamay Beisantai Luliang Cainan Karamay Beisantai Luliang

coal industrial park methanol plant, refinery power plant, industrial park, ethylene plant, coal chemical industry

141 136

power plant, ethylene plant, coal chemical industry methanol plant, refinery power plant, industrial park, ethylene plant, coal chemical industry

1870 141 136

power plant, ethylene plant, coal chemical industry methanol plant, refinery power plant, industrial park, ethylene plant, coal chemical industry

230/1870 141 1774

50

100

CO2 emission (104 t)

remarks under construction

without statistics predict

without statistics have/predict

without statistics

Table 2. Stratigraphic Sequence of Typical Reservoirs typical reservoir

erathem

system

series

formation

section

age (millions of years)

Cai9 Xishanyao Lu9 Xishanyao Ke8 Upper Karamay Bei16 Wutonggou

Mesozoic Mesozoic Mesozoic Palaeozoic

Jurassic Jurassic Triassic Triassic

Middle Middle Middle Upper

Xishanyao Xishanyao Upper Karamay Wutonggou

J2x1, J2x2 J2x4 T2k2 P3wt

166.1−178.0 166.1−178.0 235.0−241.1 245.0−256.0

1.527 and 20.83 μm with an average of 4.78 μm. The horizontal heterogeneity of the reservoir is controlled by the degree of sand body development and its distribution direction. The planar permeability contrast is 50−150 and the average vertical permeability contrast is 13.43. 4.3.2. Upper Karamay Reservoir of the Karamay Oil Field. The Upper Karamay reservoir is a uniclinal structure inclining to the southeast with the dip angle of 5°−7°. The Nanbaijiantan fault that traverses the north part of the district is an overthrust fault and the fault surface inclines to the northwest. The Jurassic Sangonghe formation and Triassic Baijiantan formation are at the top of the fault. The dip angles of these two formations are about 70°−80° and 25°−30°, respectively. The fault throw of the Upper Karamay reservoir is 400−500 m, and the horizontal fault throw is about 200−450 m. The Upper Karamay reservoir conformably deposits on the Lower Karamay reservoir with the average deposition thickness of 218 m. Fan delta deposition formed when the braided river flowed into the lake. The average porosity and permeability of the reservoir are 14.9% and 14.2 mD, respectively. 4.3.3. Wutonggou Reservoir of the Beisantai Oil Field. The north edge of the Beisantai embossment was cut by the north fault of the Beisantai formation, and thus formed the Bei 16 well block, which is a nose structure. The top of the Bei 16 well block is a nose uplift with gentle slope. Its east and west wings become steeper. And the nose uplift pitches to the north. The tectonic axis lies exactly on the connecting line of well B1041 and well B1079. The east and west parts of the fault nose are constrained by a nearly north−south trending fault. In this area, the Wutonggou reservoir is a alluvial fan-fan delta facies deposition. P3wt12−1-P3wt13−2 is the main oil-bearing sand layer, and it belongs to fan delta front subfacies depositon. Its deposition thickness is about 110 m. The main lithology of the reservoir is small conglomerate, pebbly medium-coarse sandstone and medium sandstone. The average porosity of the reservoir is 17% and the average permeability is 28.5 mD. 4.3.4. Xishanyao Reservoir of the Luliang Oil Field. The Lu 9 well block is located on three fountain bugles and it is a secondary structural unit of the Luliang uplift. The three

According to the above selection standards, the Cai9 Xishanyao reservoir of the Cainan oil field, the Ke8 Upper Karamay reservoir of the Karamay oil field, the Bei16 Wutonggou reservoir of the Beisantai oil field, and the Lu9 Xishanyao reservoir of the Luliang oil field are chosen as the typical oil reservoirs in the Junggar basin (see Figure 4). The four candidate reservoirs are typical low permeability reservoirs. The oil and gas have been sealed in these reservoirs for more than hundreds of millions of years, which have demonstrated that they have good sealing cap rocks. They have relative uniform distributions in the basin. Also, they can represent the characteristics of low permeability reservoirs located in different places of the basin. Source-sink matching is indispensable for CO2 EOR and storage, so the selection also considers the matching relation of the location between the target oil reservoir and the CO2 emission source. The statistical data of CO2 emission sources near the typical oil reservoirs is shown in Table 1. For the Cainan, Karamay, and Beisantai oil fields, their distances to the CO2 emission source are all within 25 km. But the Luliang oil field is relatively far from the CO2 emission source, whose distance is about 100 km. 4.3. Geological Characteristics of the Typical Oil Reservoirs. 4.3.1. Xishanyao Reservoir of the Cainan Oil Field. The Xishanyao reservoir of the Cainan oil field belongs to lacustrine delta front subfacies deposition, including the J2x1 and J2x2 sections (see Table 2). The main sand body belongs to the J2x2 section, which is a sandy distributary channel deposition. This sand body is an overlay to a depositional massive reservoir body, and it stably distributes in the Cai9 block. The sedimentary thickness of the sand body is 33−60 m with an average of 47.2 m. The lithology of the reservoir is mainly sandstone and fine sandstone, and the interstitial material is mainly composed of argillaceous and kaolinite. According to the core petrophysical analysis data, the average porosity and permeability of the Cai9 Xishanyao reservoir are 16% and 10.05 mD, respectively. The pore development degree of the reservoir is medium. The dominated pore type is intergranular dissolved pores. The pore throat radius is not very big and the maximum connected throat radius lies between 3285


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Figure 5. Geological models of four typical reservoirs.

Figure 6. ST-PVT experimental setup.

fountain bugles and Luliang uplift have undergone the whole process of three uplift movements, which happened at the end of Early Permian, the end of Jurassic and the end of Cretaceous to Tertiary, respectively. The Xishanyao reservoir J2x4 section of the Lu 9 well block is a sedimentary association, which is composed of thick sandstones and thin mudstones. The reservoir thickness lies between 27.4 to 39.7 m with the average of 32.4 m. The Xishanyao reservoir J2x4 section is vertically divided into three subsections J2x41, J2x42, and J2x43. Laterally, the reservoir of the whole area distributes stably. The lithology of the upside of the reservoir is mudstone and argillaceous siltstone, whereas the lithology of middle and bottom parts is mainly medium sandstone. The average porosity and permeability of the reservoir are 18.9% and 18.2 mD, respectively. 4.4. Models of the Typical Oil Reservoirs. 4.4.1. Geological Models of the Typical Oil Reservoirs. To build the geological models of the four typical reservoirs, different kinds of data were collected, including the coordinate data of the

production and injection wells, stratified data, logging data, fault data, deposition facies diagrams, core analysis data, producing test, and various kinds of well test data. Then, facies-controlled models of the four typical oil reservoirs were built based on the collected data. When the geological models were built, first of all, the deposition facies and formation structure models were established. Then, interwell interpolations and stochastic simulations for different facies were accomplished based on the distribution law of the formation parameters. As a result, the formation parameter distribution models were built. The geological models of the four typical reservoirs are shown in Figure 5. 4.4.2. Oil Pressure, Volume, Temperature (PVT) Test of the Typical Oil Reservoirs. To study the influence of the injected gas on the physicochemical properties of the crude oil, PVT tests were conducted. First, bottom hole oil samples of typical wells were taken from the four typical oil reservoirs. Then these oil samples’ components were analyzed and several PVT tests were conducted, including the saturation pressure test, flash 3286


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Table 3. Pseudocomponents of Crude Oil Sample pseudocomponent

CO2

N 2 + C1

C2−nC4

C5−C6

C7−C10

C11−C17

C18−C27

C28+

mole fraction (%)

0.1

30.35

4.23

3.15

14.49

21.67

13.98

12.03

Table 4. Fluid Critical Parameters of EOS components

MW(g/mol)

Ωa

Ωb

Pc (bar)

Tc (K)

Vc (m3 kg−1 mol−1)

Zc

AF

CO2 CH4 + N2 C2H6 C5+ C7+ C11+ C18+ C28+

44.010 16.185 33.877 71.988 114.288 187.666 298.750 518.632

0.457 0.515 0.547 0.457 0.457 0.457 0.457 0.910

0.078 0.020 0.037 0.078 0.078 0.078 0.078 0.109

73.866 12.598 30.859 12.014 27.269 18.998 13.762 2.763

304.700 94.405 280.513 468.983 587.887 700.025 730.429 787.548

0.094 0.098 0.162 0.308 0.460 0.721 1.052 1.999

0.225 0.013 0.113 0.247 0.335 0.510 0.731 4.511

0.225 0.013 0.113 0.247 0.335 0.510 0.731 4.511

separation experiment, constant composition expansion experiment, differential liberation experiment, and formation oil viscosity test. The main equipment used for the experiments is the mercury-free transparent piston high-pressure PVT device produced by French ST Company (see Figure 6). This device is mainly composed of PVT container, constant temperature air bath, pressure and temperature sensors, sample bucket, highpressure metric pump, operation control system, and the observation and recording system. The autoclave is a piston device and its volume is variable. The volumetric change of the autoclave can be controlled by the piston that is driven by the precise motor. On the basis of the above experiments, the PVTi module of the numerical simulation software Eclipse was applied to match the experimental data. The critical parameters of the heavy components and equation of state (EOS) parameters were corrected to obtain the PVT data that can be used in the numerical simulation. PR3 was chosen as the appropriate EOS. When the pseudocomponents of the reservoir fluid were determined (see Table 3), the data of the above experiments was regressed to obtain the characteristic parameters of the reservoir fluid, such as saturation pressure, gas−oil ratio, surface oil density, formation oil density, viscosity, and the change of the saturation pressure after gas injection. The data calculated by EOS are applied to match the experimental data. When the curves of calculated data are approximate to the curves of experimental data, the good matching results could be obtained. The matching results, which are the critical parameters of the oil sample from the Cai9 Xishanyao reservoir, are shown in Table 4 (these critical parameters of the oil samples from the other three typical oil reservoirs are not shown here). 4.4.3. CO2-crude Oil Expansion Experiment of Typical Oil Reservoirs. A gas-injection expansion experiment was conducted to study the effect of different proportions of injected gas on the fluid phase. This experiment could also help to determine the oil-displacement mechanism by injecting gas and to provide fundamental parameters for phase regression. Under the current formation pressure, a 10% mole fraction of CO2 was added into the oil. After gas was injected, the pressure of the system increased gradually until all of the injected gas dissolved into the oil. Then the saturation pressure, PV relation, and viscosity of the new system were tested. After that, above procedure was repeated several times until the percentage of injected gas reached the desired amount (see Table 5). In this paper, the crude oil of the Bei16 Wutonggou oil reservoir was used to conduct the CO2-injection expansion

Table 5. Influence of Fluid Phase by Injecting CO2 mole ratio of CO2 (mol/mol) (%)

saturation pressure (MPa)

oil density (g/cm3)

volume factor (Bo)

solution gas-oil ratio (m3/t)

swell factor (V/V)

0 10 20.0 30.0 40.0 50.0 60.0

11.79 14.75 17.8 20.3 23.65 30.52 42.84

0.8150 0.8109 0.8060 0.8026 0.8014 0.8026 0.8079

1.1220 1.1968 1.2521 1.3190 1.4035 1.5166 1.6782

40.6 56 76 101 135 182 252

1.0000 1.0379 1.0858 1.1438 1.2171 1.3151 1.4553

experiment. The main physical characteristic changes of the crude oil under bubble point pressure after injecting CO2 are shown in Table 5. The saturation pressure of the crude oil increases greatly when the molar volume ratio of injected CO2 increases. When the ratio reaches to 60%, the oil saturation pressure rises to 42.84 MPa, however, the state of the CO2 and crude oil has not yet reached a critical point, which indicates that the first contact minimum miscible pressure (MMP) of them is higher than 42.84 MPa. The crude oil volume and its volume factor are increasing with the increased amount of CO2, and the effects of swell and gas flooding are better. The solution gas−oil ratio is also increasing with the increase of injected CO2, but crude oil density decreases at first and then increases with the increase of injected CO2. 4.4.4. MMP Experiments of CO2 and Crude Oil of Typical Reservoirs. For the technology of gas flooding to improve oil recovery, it can be divided into miscible flooding and immiscible flooding. The miscible displacement mechanism is that, under reservoir conditions, the oil and gas can be mixed without the presence of interfacial tension due to the diffusion and mass transfer between two fluids. As a result, the oil displacement efficiency could be greatly improved and the residual oil saturation could be minimized. The flowchart of the slim tube experiment is shown in Figure 7, and the main parameters of the slim tube model are shown in Table 6. The miscible flooding efficiency is much higher than that of immiscible flooding proved by theory and practice.29−31 The oil displacement efficiency of CO2 injection depends largely on the displacement pressure. It can achieve miscible flooding when the displacement pressure is higher than the MMP, whereas it cannot reach mixed phase and achieve high oil recovery when the displacement pressure is less than the MMP. But the displacement pressure should not be too high, because achieving high pressure conditions requires greater investment 3287


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The numerical simulation models were established by applying the static field parameters of the geological model, the PVT parameters, oil−water and oil−gas relative permeability data, the basic parameters of the rock, and the historical production data of wells. After the four numerical simulation models were established, then history matching was conducted to revise those geological models and to obtain the current residual oil saturation distribution and the reservoir parameters, whose aim is to provide accurate models for the following study of CO2 EOR and storage. 4.5. CO2 EOR and Storage Capacity Calculation of Typical Reservoirs. Currently, the reservoirs in the Junggar Basin are usually developed by water flooding, so the potential evaluation of CO2 EOR and storage focuses on the reservoirs after water flooding. The basic parameters of four typical reservoirs are shown in Table 7. To obtain the correct numerical simulation models, the history matching was conducted. When the history matching is carried out, the production system is set to constant a single well production rate. To judge the accuracy of history matching, main indicators are original oil in place (OOIP), reservoir pressure, water cut, gas−oil ratio, and the production index. According to these indicators, finally good history matching results of the four models are realized, and their qualified rates of matching are all more than 90%. Thus, the four models meet the accuracy requirements of CO2 EOR and storage potential evaluation. It is necessary to determine a reasonable gas injection volume before applying CO2 EOR and geological sequestration in a reservoir. From the numerical simulation result of Figure 9, we can see that there is an inflection point near 0.7 HCPV of CO2. The oil recovery factor increases greatly with the increasing of HCPV of CO2 before the inflection point. But after that point, the oil recovery factor increases slightly with the increasing of CO2 HCPV. Therefore, the 0.7 HCPV of CO2 is a reasonable value.

Figure 7. Flowchart of slim tube experiment.

Table 6. Main Parameters of Slim Tube Experiment main parameters

value

maximum temperature (°C) maximum pressure (MPa) length (m) inner diameter (mm) outside diameter (mm) filler (quartz sand) (mesh) porosity (%) air permeability (μm2)

150 50 20 3.86 6.35 170−325 39 5.43

and costs. MMP is a key parameter to identify if those four typical reservoirs can achieve miscible flooding, to identify CO2 miscible/immiscible displacement design and prediction, as well as to identify feasible studies, development plan designs and economic evaluations of CO2 miscible flooding. So, the MMP experiments are an indispensable part of the study of CO2 flooding and storage of typical reservoirs in the Junggar Basin. Figure 8 shows the MMP of CO2 and crude oil system of the four typical reservoirs. 4.4.5. Numerical Simulation Models of Typical Reservoirs. The oil−water relative permeability curve was obtained through core displacement experiments and oil−gas relative permeability curve was obtained through Corey model calculation.

Figure 8. MMP of CO2 and crude oil system of the four typical reservoirs. 3288


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ratio to 1000 m3/m3. Then the recovery factor and CO2 storage coefficient is calculated. The evaluation results of the four typical reservoirs are shown in Table 8.

Table 7. Parameters of Four Typical Reservoirs parameters geologic reserves (104t) temperature (°C) reservoir depth (m) effective thickness (m) reservoir type porosity (%) permeability (mD) initial oil saturation (%) pressure factor initial reservoir pressure (Mpa) saturation pressure (Mpa) initial gas/oil ratio (m3/m3) oil volume factor density of surface oil, (kg/m3) density of formation oil, (kg/m3) viscosity of surface oil (cp) viscosity of formation oil (cp) formation water salinity (mg/L) MMP (Mpa) miscible or immiscible

Cai9 Xishanyao reservoir

Ke8 Upper Karamay reservoir

Bei16 Wutonggou reservoir

Lu9 Xishanyao reservoir

2655

5711

714

9873.9

62.2 2230

53 2060

65.1 2235

65.6 2175

30.2

19.5

15.7

16.7

structure 16 10.05

monoclinic 14.9 14.2

structure 17 28.5

structure 18.9 18.2

56

60

52

50

0.93 21.27

1.25 25.65

1.12 25.3

0.926 20.6

8.4

14.35

19.05

4.19

40

141

67

17

1.235 825.2

1.281 854.1

1.185 898.8

1.128 853

776.2

739

833.7

829

2.78

25.3

15.4

10.85

1.18

2.01

6.61

4.9

10 210

18 392

14 589

13 380

21.6 immiscible

19.1 miscible

42.5 immiscible

20.2 miscible

5. CO2 EOR AND STORAGE POTENTIAL EVALUATION OF THE JUNGGAR BASIN 5.1. Screening Criteria for CO2 EOR and Storage. In recent years, the reservoir screening criteria that are suitable for CO2 miscible flooding are proposed by domestic and foreign experts. Although each experts’ screening criteria are different at various periods, the value of their screening criteria have the same trend and have similar value boundaries. The screening criteria for CO2 miscible flooding that are suitable for light oil reservoirs in the Junggar Basin are proposed by taking into account other experts’ screening criteria and reservoir characteristics of the Junggar Basin (see Table 9).32,33 According to the reservoir screening criteria suitable for CO2 immiscible flooding of foreign heavy oil reservoirs, for the heavy oil reservoirs whose crude oil density is greater than 920 kg/m3 in the Junggar Basin, the screening criteria for CO2 immiscible flooding (or near miscible flooding) is shown in Table 10. 5.2. CO2 EOR and Storage Potential Evaluation. By the end of 2013, there were 25 proven oil fields in the Junggar Basin. According to the screening criteria for CO2 EOR and storage, which are suitable for Chinese geological features and fluids characteristics (see Tables 9 and 10), the reservoirs in the Junggar Basin were screened, of which there are 275 development blocks of 24 oil fields suitable for CO2 EOR and storage. There are 139 development blocks with 87 495.24 × 104 t OOIP that are suitable for CO2 miscible flooding, and 136 development blocks with 75 649.78 × 104 t OOIP that are suitable for CO2 immiscible flooding. The oil recovery factors of two typical CO2 miscible flooding reservoirs (Ke8 Upper Karamay reservoir and Lu9 Xishanyao reservoir) are 14.14% and 14.38%, respectively, whereas the CO2 storage coefficients are 0.329 and 0.357, respectively. The oil recovery factors of two typical CO2 immiscible flooding reservoirs (Cai9 Xishanyao reservoir and Bei16 Wutonggou reservoir) are 8.21% and 7.47%, respectively, whereas the CO2 storage coefficients are 0.237 and 0.225, respectively. The CO2 EOR and storage potential evaluation of water flooding oil reservoirs in the Junggar Basin are conducted by “borrowing” the oil recovery factor and storage coefficients of typical reservoirs. For two typical CO2 miscible flooding reservoirs, the average additional oil recovery factor is 14.26% and the average CO 2 storage coefficient is 0.343. For two typical CO 2 immiscible flooding reservoirs, the average additional oil recovery factor is 7.84% and the average CO2 storage coefficient is 0.231. For the 275 development blocks in the Junggar Basin, the CO2 miscible flooding reservoirs can improve additional oil for 12 476.82 × 104 t, and the CO2 immiscible flooding reservoirs can improve additional oil for 5930.94 × 104 t. The total improved additional oil is 18 407.76

Figure 9. Oil recovery factor with different volumes of CO2 injection.

To obtain the oil recovery factor and CO2 storage coefficient, we injected 0.7 HCPV of CO2 after the water cut rose to 95%. The gas injection rate is 2 × 104 m3/d. The production well was controlled by constant flowing bottom hole pressure (FBHP), and the economic limit indicator is set for the produced gas−oil

Table 8. EOR and CO2 Storage Evaluation Results of the Four Typical Reservoirs reservoir

geologic reserves (104 t)

EOR (104 t)

CO2 storage (104 t)

enhance recovery factor (%)

CO2 storage coefficient (104 t)

miscible or immiscible

Cai9 Ke8 Bei16 Lu9

2148.000 1445.000 714.300 6755.000

176.351 204.323 53.358 971.369

508.345 474.784 187.861 2411.535

8.210 14.140 7.470 14.380

0.237 0.329 0.225 0.357

immiscible miscible immiscible miscible

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Table 9. Screening Criteria for Application of CO2 Miscible Flooding reservoir parameter crude oil density (g/cm3) oil gravity (°API) depth (m) original pressure (MPa) temperature (°C) viscosity (mPa·s) permeability (mD) oil saturation

Carcoana, 1982

Taber, 1983

Klins, 1984

Ren S., 2008

Zhao F., 2001

S. Bach, 2004

“Ninth five-year plan”, 1998

The paper, 2010

10.3

22−55 600−3500

>22 >762

27−48

>25

>7.5

≥MMP

>22 >600 >7.5

0.30

0.25

550 0.25

32−120 1 >0.20

× 104 t. The CO2 storage capacity of miscible flooding reservoirs is 30 010.87 × 104 t, and the CO2 storage capacity of immiscible flooding reservoirs is 17 475.1 × 104 t. The total CO2 storage capacity is 47 486.0 × 104 t (see Figure 10).

6. CONCLUSION (1) The CO2 EOR and storage calculation method proposed in the paper has combined the advantages of the mass balance method and analogy method. By introducing the CO2 storage coefficient to calculate CO2 storage capacity, it can not only be calculated easily but also can consider many influence factors, such as buoyancy, gravity override, mobility contrast, heterogeneity, and water saturation, when using the numerical method to calculate the CO2 storage coefficient. However, the weakness of the method is that it needs plenty of geological and fluid data to build geological and numerical models. And the influence factors of the CO2 storage coefficient are composed in one integrated parameter, thus it is difficult to clearly recognize which factor is important. (2) For two typical miscible flooding reservoirs in the Junggar Basin, the average additional oil recovery factor is 14.26% and the average CO2 storage coefficient is 0.343. For two typical immiscible flooding reservoirs in the Junggar Basin, the average additional oil recovery factor is 7.84% and the CO2 storage coefficient is 0.231.

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AUTHOR INFORMATION

Corresponding Author

*H. Wang. Tel.: 86-18101361250. Fax: +86-10-89733223. Email: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Basic Research Program of China “Carbon Dioxide Emission Reduction, Storage and Resource Utilization (973 Program, Grant 2011CB707302)”, the Chinese National Major Science and Technology (Projects 2011ZX05016-006 and 2011ZX05009004-001), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20120007120007).

Figure 10. Results of CO2 EOR and storage. 3290


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