- Email: [email protected]
Laboratory experiment, modeling and field application of indigenous microbial flooding
Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
Contents lists available at SciVerse ScienceDirect
Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol
Laboratory experiment, modeling and field application of indigenous microbial flooding Chuanjin Yao a,n, Guanglun Lei a, Jiye Ma b, Fengmin Zhao c, Gongze Cao c a
School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong, China Research Institute of Exploration and Development, PetroChina Changqing Oilfield Company, Xi’an, Shanxi, China c Research Institute of Oil Production Technology, Sinopec Shengli Oilfield Company, Dongying, Shandong, China b
a r t i c l e i n f o
abstract
Article history: Received 2 January 2011 Accepted 2 April 2012 Available online 24 April 2012
In this study, the indigenous microorganisms in the produced water of Block Zhan-3 in Shengli Oilfield were activated successfully when corn steep liquor and (NH4)2HPO4 was used as carbon source and nitrogen and phosphorus source, respectively. The oil viscosity change after interaction with microorganisms was measured and the physical simulation experiment of indigenous microbial flooding was performed. Based on the main mechanisms of IMEOR (indigenous microbial enhanced oil recovery), a mathematical model of indigenous microbial flooding reflecting the migration, growth, metabolism and adsorption properties of indigenous microorganisms was established. Using the orthogonal design method, the concentration of activating nutrient, air injection quantity, injection PV and injection mode of indigenous microbial flooding were optimized. According to the actual situation of Block Zhan-3, an optimal program was proposed and field test in Block Zhan-3 was conducted. This study shows that the indigenous microbial flooding can achieve better water reduction and enhanced oil recovery effect; by using the mathematical model of indigenous microbial flooding, it is possible to achieve a reliable performance prediction. The results can effectively provide guidance for the popularization and application of the IMEOR technology. & 2012 Elsevier B.V. All rights reserved.
Keywords: indigenous microorganisms physical simulation mathematical model numerical simulation field test enhanced oil recovery
1. Introduction At present, many oilfields in the world have entered the high water-cut stage. However, the decline rate of oil production is quick and the ultimate oil recovery is low. Therefore, how to enhance oil recovery further becomes an urgent question to be solved currently. As a new tertiary recovery technology, the indigenous microbial flooding has many advantages in comparison with other technologies. The indigenous microorganisms have better adaptability to the environment of oil reservoirs. The equipment and the construction are simpler, and the investment is lower. Most important of all, this technology has almost no damage to the oil reservoirs and does not pollute the environment. Thus, the indigenous microbial flooding has a wide application prospect (Cheng et al., 2006; Maudgalya et al., 2007). In recent years, field applications of this technology have been reported in Russia, Norway and so on, and obtained better effect of enhanced oil recovery (Awan et al., 2008; Rrebecca, 1996; Thrasher et al., 2010). In China, this technology has been applied in Oilfields of Daqing, Shengli, Dagang, Xinjiang and so on, and achieved significant effect of water decreasing and oil increasing (Cheng et al., 2005; Chen et al., 2006; Zhang et al., 2008). However, the mechanisms of indigenous microbial enhanced oil recovery
n
Corresponding author. Tel.: þ86 15165283060. E-mail address: [email protected] (C. Yao).
0920-4105/$ - see front matter & 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.petrol.2012.04.001
(IMEOR) and mathematical model of indigenous microbial flooding need to be performed further. In this paper, the indigenous microorganisms in the produced water of Block Zhan-3 in Shengli Oilfield were activated under the laboratory simulation of high temperature high pressure condition (65 1C, 11 MPa) of Block Zhan-3. The growth of microorganisms and consumption of carbon source were investigated through the colony analysis. The oil viscosity change after interaction with microorganisms was measured and the physical simulation experiment of indigenous microbial flooding was performed. A mathematical model of indigenous microbial flooding reflecting the migration, growth, metabolism and adsorption properties of indigenous microorganisms was established and verified with the experimental data. Furthermore, an optimal program of indigenous microbial flooding for Block Zhan-3 was proposed using the mathematical model. At last, field test of indigenous microbial flooding in Block Zhan-3 was conducted.
2. Experimental 2.1. Materials Na2HPO4 (purity above 98.0%), K2HPO4 (purity above 98.0%) and (NH4)2HPO4 (purity above 98.0%) were all purchased from
40
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
Sinopharm Chemical Reagent Co., Ltd. (China). The glucose, sucrose, blend oil, molasses and corn steep liquor were supplied by Qingdao Yihai Food Marketing Company (China). All reagents were used without any further purification. The produced water with salinity of less than 10,000 mg/L, produced oil with viscosity of 324.0 mPa s at 50 1C and produced sand for sandpack models were all obtained from Block Zhan-3 of Shengli Oilfield in China. The initial concentration of microorganisms in the produced water was 103 cell/ml. The microorganisms included the hydrocarbon oxidation bacteria, methane producing bacteria, denitrifying bacteria and sulfate reducing bacteria (Bao et al., 2008).
2.4. Oil viscosity change after interaction with microorganisms The experiment was carried out in a 250 ml conical flask equipped with a breathable rubber plug. First, 50 ml of activating nutrient was introduced into the flask. After sterilization for 30 min in high pressure steam, the flask was vaccinated with 200 ml produced water and cultivated on shaking table with constant temperature of 65 1C for 10 days. Then the vaccine was disposed into different concentration and interacted with the produced oil for 10 days. At last, the viscosity of oil samples was determined by DV III Viscosimeter.
2.2. Equipments 2.5. Physical simulation of indigenous microbial flooding The main equipment was the experimental flow for indigenous microbial flooding, as shown in Fig. 1. The 2PB00C constant-flux pump was supplied by Beijing Satellite Factory, China. The three piston middle vessels, sandpack tubes, pressure sensors and thermostat were all purchased from Petroleum Research Instrument Co., Ltd., Jiangsu, China. Other equipments included the surface tension determinator, hand sling pressure pump, injector, conical flask, anaerobic jar and 1000-fold microscope, DV III viscosimeter (all supplied by Petroleum Research Instrument Co., Ltd., Jiangsu, China). 2.3. Activation of indigenous microorganisms In this experiment, the sandpacks were all sterilized in high pressure steam and saturated by the produced water. The pressure in the thermostat was kept at 11 MPa. Then the following experiments were performed. (1) Activation with different carbon source: different carbon sources (glucose, sucrose, blend oil, molasses and corn steep liquor) with same concentration and air (liquid–gas ratio 1:12) were injected into five sandpacks for 1.0 PV, respectively. (2) Activation with different phosphorus source: different phosphorus sources (Na2HPO4, K2HPO4 and (NH4)2HPO4) with same concentration and air (liquid–gas ratio 1:12) were injected into three sandpacks for 1.0 PV, respectively. (3) Activation with different concentration of optimal carbon source: the optimal carbon source with different concentration and air (fluid–gas ratio 1:12) were injected into six sandpacks for 1.0 PV, respectively. The sandpacks were all placed in the incubator with constant temperature of 65 1C and cultured for 10 days. Then the bacterium fluid samples were taken through nitrogen gas flooding and the colony was analyzed. (4) Growth of microorganisms and consumption of carbon source: the optimal carbon, nitrogen and phosphorus source at optimal concentration and air (liquid–gas ratio 1:12) were injected into a sandpack; then the sandpack was placed in the incubator with constant temperature of 65 1C; then the samples cultured for different time were taken through nitrogen gas flooding and the colony, surface tension and consumption of carbon source were analyzed.
Fig. 1. Experimental flow for indigenous microbial flooding.
In this experiment, the sandpacks were all sterilized in high pressure steam and saturated by the produced water. The sandpacks were then saturated by the produced oil and the original oil saturation was calculated. After aging in incubator with constant temperature 65 1C for 24 h, the produced water was injected into the sandpacks until the water-cut was up to 98% and the residual oil volume (Vr) was calculated. The microorganisms solution with different concentration was then injected with a rate of 1.0 ml/min for 0.5 PV. At last, the produced water was injected into the sandpacks again until the water-cut was up to 98%. Simultaneously, the oil volume output (DV) was recorded and the enhanced oil recovery (EOR) was calculated using the formula: EOR¼ DV/Vr 100%.
3. Mathematical model 3.1. Assumption The assumption conditions for the mathematical model are as follows: (1) the percolation of oil, water and gas obeys Darcy law; (2) the microorganisms, nutrient substances and metabolites are mainly in water phase; (3) the migration, growth, metabolism and adsorption properties of indigenous microorganisms in displacement process are taken into consideration; (4) the carbon source and oxygen are the nutrient substances which restrict the growth of microorganisms and other nutrient substances are sufficient; (5) the variation of rock porosity and permeability, oil viscosity, surface tension and oil–water relative permeability are taken into consideration.
3.2. Biochemical characteristic equation of microorganisms 3.2.1. Growth and reproduction equation of microorganisms In the process of growth and reproduction, the microorganisms have to ingest essential nutrient substances from the surroundings, and release the metabolites. When any one is not enough, the growth of microorganisms will be restricted. The growth and reproduction of microorganisms is described by the Monod model (Dette et al., 2005) S1 S2 m ¼ mmax ð1Þ K 1 þS1 K 2 þ S2 where m is the specific growth rate, h 1; mmax is the maximum value of specific growth rate (determined by experiment), h 1; S1, S2 are the concentrations of nutrients 1 and 2 respectively which limit the growth and reproduction of microorganisms, g/ml; K1, K2 are the saturated constants of nutrients 1 and 2 respectively (determined by experiment).
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
3.2.2. Consumption equation of nutrient substances The consumption rate of nutrient is expressed by the following equation:
g¼
m Y Gi
þ mi
ð2Þ
where g is the consumption rate, h 1; YGi is the production quantity of microorganism consuming unit nutrient i (determined by experiment), cell/g; mi is the consumption rate of nutrient i for the metabolism of microorganisms (determined by experiment), h 1. 3.2.3. Concentration equation of metabolites The concentration of metabolites released by the microorganisms is expressed by the following equation: C j ¼ Y j=X
dX þnj X dt
ð3Þ
where Cj is the production rate of metabolite j, g/(ml h); X is the concentration of microorganisms, cell/ml; Yj/X is the concentration variation of metabolite product j with the variation rate of concentration of microorganisms; nj is the production rate of metabolite product j for microorganisms to maintain their life, h 1. 3.3. Migration equation of microorganisms, nutrient substances and metabolites
41
3.3.2. Migration equation of nutrient substances in porous medium The nutrient substances can move in the porous medium through the carrying of water flow and their convection and proliferation. Besides, they are consumed by the microorganisms and the adsorption on rock surface. Based on the mass balance principle, the migration equation of nutrient substance i is @ðjSw Si Þ ¼ r ðV w Si Þ þ r ðjSw Dsi rSi Þ @t X þðm=Y Gi þ mi ÞX þmi G1 a þbX
ð7Þ
where Dsi is the convection diffusion coefficient of nutrient substance i. 3.3.3. Product migration equation of metabolites in porous medium The metabolites are produced by the microorganisms and suppose all of them dissolved in water phase and can be adsorbed by rock, the migration equation is: @ðjSw C j Þ ¼ r ðV w C j Þ þ r ðjSw Dcj rC j Þ @t ! Cj dX @ 0 þ nj X þ Y j=X G1 0 0 dt @t a þ b Cj where Dcj is the convective diffusion product j (determined by experiment); constant of metabolite product j; ! N0 is tion of metabolite product j adsorbed on
ð8Þ
coefficient of metabolite a0 , b0 are the adsorption the maximum concentraunit rock surface, g/ml.
3.4. Variation equations of physical properties 3.3.1. Migration equation of microorganisms in porous medium In the microbial flooding process, the microorganisms can move in the porous medium through convection and proliferation. Besides, the microorganisms can be adsorbed by the rock surface. The migration equation of microorganisms in porous medium can be expressed by the Fick law (Bird et al., 2002) @X @X @X I ¼ DX þ þ ð4Þ @x @y @z where I is the flow rate of microorganisms through unit area; DX is the convection diffusion coefficient of microorganisms (determined by experiment). The adsorption of microorganisms can be expressed with the Langmuir adsorption formula (Adamson, 1990)
G ¼ G1
X a þ bX
ð5Þ
where ! is the concentration change of microorganisms due to rock adsorption, g/ml; ! N is the maximum concentration of microorganisms when the unit rock volume surface adsorbs the full single-layer microorganisms (determined by experiment), g/ml; a, b are constants (determined by experiment); X is the concentration of microorganisms, g/ml. Based on the balance principle of microorganisms, the migration equation of microorganisms in porous medium can be expressed by the equation: the concentration change of microorganisms in unit time and unit volume¼the concentration change due to the fluid flow þthe concentration change due to diffusionþthe concentration change due to growth and reproductionþthe concentration change due to adsorption. @ðjSw XÞ ¼ r ðV w XÞ þ r @t ðjSw DX rXÞmX þ
@ X G1 @t a þbX
ð6Þ
where Vw is the seepage velocity of water phase, cm/h; Sw is the water saturation; j is the porosity.
When the microorganisms are adsorbed on the rock surface, the rock porosity and permeability will be reduced and the quantity of microorganisms adsorbed by unit rock volume from water phase is m¼
X jSw G1 a þ bX
ð9Þ
Suppose the average volume of microorganism is v, then the total volume of adsorbed microorganisms is V ¼ mv ¼
X G1 jSw v a þ bX
ð10Þ
The proportion of the volume of adsorbed microorganisms according to the rock pore volume is
s¼
V
j
¼
X G1 Sw v a þ bX
ð11Þ
Therefore, the porosity change equation is
j ¼ j0 s ¼ j0
X G1 Sw v a þ bX
ð12Þ
The adsorption of microorganisms on rock surface only changes the rock porosity and capillary tube radius, but the capillary tube number is invariable. According to the capillary flow model of porous medium (Kozeny, 1927), the permeability change equation is 2 j j0 s 2 K ¼ K0 ¼ K0 ð13Þ
j0
j0
The interaction between metabolites and formation fluid causes the viscosity of oil and water to change (Donaldson, 1988). This change can be determined through the fermentation experiment of microorganism and oil. The change relationship can be expressed as ( mwm ¼ f 1 ðSi ,C j ,XÞ ð14Þ mom ¼ f 2 ðC j ,XÞ where mom, mwm are the viscosity of oil and water after interaction with microorganisms, mPa s.
42
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
In the microbial flooding, the water viscosity is related to the concentration of nutrients, metabolites and microorganisms, and the oil viscosity is related to the concentration of metabolites and microorganisms. The metabolites can cause the surface tension between the oil, water and gas and the rock wettability to change. Thus, the capillary pressure changes too. The capillary pressure can be expressed by the Laplace equation 8 P ¼ P w Po ¼ 2swo cos ywo =r > < wo Pog ¼ P o P g ¼ 2sog cos yog =r ð15Þ > : P ¼ P P ¼ 2s cos y =r gw g w gw gw where s, y are the surface tension between oil, gas and water and wetting angle with the rock surface (determined by experiment). 3.5. Percolation equations of oil, water and gas under influence of microorganism The percolation of oil, water and gas in porous media satisfies the three-dimensional and three-phase percolation equations (Lake, 1989; Peaceman, 1977) Oil phase: KK ro ro @ r rPo þqo ¼ ðjSo ro Þ @t Bo mom
r
KK rg rg
mgm
rP g þ
KK ro rgs
mom
!
rPo þ qg ¼
The enhanced oil recovery mechanisms of indigenous microbial flooding are extremely complex. Based on the perspective of seepage mechanics and reservoir engineering, the mechanisms can be explained as three aspects (Lei et al., 2009): (1) the reduction of oil viscosity; (2) the wettability change and the reduction of capillary pressure; (3) the change of oil–water relative permeability and reduction of residual oil saturation. Thus, the variation of seepage parameters due to these 3 aspects is mainly considered in the mathematical model. Fig. 2 shows the concrete method for solving the mathematical model. Based on the numerical simulation software MEOR developed by the Energy Department in the United States, further revision and perfection, the numerical simulation software of indigenous microbial flooding was developed. In order to verify the mathematical model, Eqs. (1) and (2) were used to fit the growth curve of microorganisms and consumption curve of carbon source. Besides, the numerical simulation software of indigenous microbial flooding was used to calculate the enhanced oil recovery of physical simulation of indigenous microbial flooding.
4. Field application 4.1. Overview of Block Zhan-3 ð17Þ
i @h jðSg rg þ So rgs Þ @t
ð18Þ
Due to the absorption of microorganisms and metabolites in pores, the fluid saturation will be changed. The saturations satisfy the subsidiary equation So þ Sg þ Sw þ s ¼ 1
3.6. Solution and verification of mathematical model
ð16Þ
Water phase: KK rw rw @ r rPw þqw ¼ ðjSw rw Þ @t Bw mwm Gas phase:
gas viscosity; s is the porosity occupied by microorganisms and metabolites.
ð19Þ
where q is the output; Kro, Krw, Krq are the permeability of oil, water and gas during microbial flooding respectively; mgm is the
Block Zhan-3 lies in the north of Shaojia Oilfield in Shengli Oilfield and the oil-bearing formation is mainly the first sand body (Dongying Group) of lower tertiary. The oil-bearing area is 1.5 km2, the geological reserves are 282 104 t, the reservoir buried depth is 1240–1360 m and the primary reservoir temperature is 65 1C. The reservoir is composed of sand-shale, pebbled sandstone and interbedded fine sandstone, the oil layers are thin and their distribution is not stable. The oil layer belongs to fluvial deposition with good physical properties. The permeability is 800–1000 10 3 mm2, the porosity is 30% and the initial oil saturation is 0.62. This experimental region has been developed with water flooding for 20 years. Up to September 2010, there are 11 oil wells and 7 water wells, the oil production rate is 31.6 t/d, the total water cut is 92.36%, the cumulative oil production is
Fig. 2. Concrete method for solving the mathematical model.
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
60.23 104 t and the oil recovery is only 21.4%. Now, this experimental region has entered the high water-cut time. However there is still about 70% remaining oil in the layer after water flooding. Thus, it is very important to take measures to enhance oil recovery further. 4.2. Historical fitting and performance prediction The empirical formulas of oil–water relative permeability used in Block Zhan-3 are as follows: ( kro ¼ ð1SwD Þ2:4605 ð20Þ krw ¼ 0:4161 SwD 1:4053 The original irreducible water saturation is 0.471 and the original residual oil saturation is 0.162. Based on the geologic model, fluid model and historical production data, the production history of water flooding in Block Zhan-3 was fitted. 4.3. Optimization of injection parameter In this study, three injection parameters including activating nutrient concentration, air injection quantity (liquid–gas ratio) and injection PV were selected to be optimized. Each parameter had four levels, as shown in Table 1. According to the orthogonal design principle (Sun et al., 2006), 16 programs were designed, as shown in Table 2. The fuzzy comprehensive evaluation model (Liu et al., 2010) was used to select the optimal program and the evaluating indicators include 8 > < dA ¼ ðDQ o DQ omin Þ=ðDQ omax DQ omin Þ dB ¼ ðDf w Df wmin Þ=ðDf wmax Df wmin Þ ð21Þ > : d ¼ ðDpDp Þ=ðDp C min max Dpmin Þ where dA is the dimensionless incremental oil; dB is the dimensionless water-cut reduction; dC is the dimensionless profit; DQo Table 1 Injecting parameters of indigenous microbial flooding. Parameter
Level
Activating nutrient concentration (%) Air injection quantity (liquid–gas ratio) Injection PV
1
2
3
4
0.14 1:4 0.1
0.42 1:8 0.2
0.70 1:12 0.3
0.98 1:16 0.4
43
is the oil increment of current program, t; DQomax is the maximum oil increment of all programs, t; DQomin is the minimum oil increment of all programs, t; Dfw is the water-cut reduction of the current program, %; Dfwmax is the maximum water-cut reduction of all program, %; Dfwmin is the minimum water-cut reduction of all program, %; Dp is the profile of current program, RMB; Dpmax is the maximum profile of all program, RMB; Dpmin is the minimum profile of all programs, RMB. The calculation formula of profit is:p ¼ DN p Po Mg Pg M y P y W, where p is the profit, RMB; DNp is the cumulative oil increment, t; Po is the oil price, RMB; Mg is the time of air injecting, h; Pg is the air price, RMB/h; My is the amount of activating nutrient, t; Py is the activating nutrient price, RMB/t; W is the prophase investment, RMB. 4.4. Optimization of injection mode Three injection modes in this study were considered. Injecting continuously for the whole cycle: under the condition of same amount of activating nutrient and air injected in each cycle, injecting nutrient and air with a low concentration continuously for the whole cycle (20 d). Injecting continuously for a half cycle: under the condition of same amount of activating nutrient and air injected in each cycle, injecting activating nutrient with a higher concentration for a half cycle and the rest half cycle (10 d) only injecting water and air. Injecting centrally with a high concentration: under the condition of injecting same amount of activating nutrient and air in each cycle, injecting activating nutrient with a high concentration for 1 day and after injecting a cycle activating nutrient, the rest time (19 d) only injecting water and air.
5. Results and discussions 5.1. Activation results of indigenous microorganisms The experiment results of activation with different carbon source (Fig. 3) show that taking glucose, sucrose, blend oil or molasses as carbon source, the total concentration of indigenous microorganisms is not very high, only about 104–106 cell/ml. The corn steep liquor can activate the indigenous microorganisms better and the total concentration of microorganisms is up to 108 cell/ml. The results indicate that the corn steep liquor is the optimal carbon source. The experiment results of activation with different concentration of optimal carbon source (Fig. 4) shows
Table 2 Orthogonal design and simulation results of 16 programs. Program no.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Activating nutrient concentration (%)
Air injection quantity (liquid–gas ratio)
0.14 0.14 0.14 0.14 0.42 0.42 0.42 0.42 0.70 0.70 0.70 0.70 0.98 0.98 0.98 0.98
1:4 1:8 1:12 1:16 1:4 1:8 1:12 1:16 1:4 1:8 1:12 1:16 1:4 1:8 1:12 1:16
Injection PV
0.1 0.2 0.3 0.4 0.2 0.1 0.4 0.3 0.1 0.4 0.3 0.2 0.4 0.3 0.2 0.1
Cumulative oil production (104 t)
Cumulative oil increment (104 t)
Recovery
87.26 89.04 92.42 97.62 92.46 92.76 99.62 100.26 87.42 103.58 103.76 92.21 88.58 90.21 90.55 89.69
0.92 2.70 6.08 11.28 6.12 6.42 13.28 13.92 1.08 17.24 17.42 5.87 2.24 3.87 4.21 3.35
30.94 31.58 32.77 34.62 32.79 32.89 35.32 35.55 31.00 36.73 36.80 32.70 31.41 31.99 32.11 31.80
(%)
44
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
Fig. 3. Experiment results of activation with different carbon source.
Fig. 4. Experiment results of activation with different concentration of optimal carbon source.
that the total concentration of microorganisms increases with the increase of the concentration of corn steep liquor; when the concentration of corn steep liquor is 0.3%, the total concentration of microorganisms obtains a higher value of 2 108 cell/ml; when the concentration of corn steep liquor increases further, the increase rate of microorganisms concentration becomes lower. Thus, in order to reduce the investment, the optimal concentration of corn steep liquor is selected as 0.3%. The experiment results of activation with different phosphorus source (Fig. 5) show that the three phosphorus source can all activate the indigenous microorganisms successfully. That is because these three phosphate solutions are all alkaline and can neutralize the pH of produced water so that the water environment is near neutral and suitable for the growth of microorganisms. Among them, the activation effect of (NH4)2HPO4 is better than others and the total concentration of microorganisms is up to 108 cell/ml. The results indicate that (NH4)2HPO4 is the optimal phosphorus source. Besides, the total concentration change of microorganisms is not obvious when the concentration of (NH4)2HPO4 is more than 0.3%. Thus, the optimal concentration of (NH4)2HPO4 is 0.3%. Simultaneously, (NH4)2HPO4 can also provide sufficient nitrogen source for microorganisms. The growth curve of indigenous microorganisms (Fig. 6) shows that under the function of activating nutrient, the total concentration of microorganisms is relatively smaller in the former 5 days; then the microorganisms reproduce rapidly and the total concentration of microorganisms is up to 2 108 cell/ml at about
Fig. 5. Experiment results of activation with different phosphorus sources.
Fig. 6. Growth curve of indigenous microorganisms.
15 days; afterward, the concentration will still keep at the magnitude of 2 108 cell/mL. The results indicate that the activation time for indigenous microorganisms of Block Zhan-3 in sandpack is about 15 days. The consumption curve of carbon source (Fig. 7) shows that the concentration of corn steep liquor decreases obviously with the increase of culture time which indicates that the indigenous microorganisms have been activated and start to reproduce rapidly; the concentration of corn steep liquor becomes a lower level of 0.05% about 15 days which indicates that the growth of microorganisms comes into the decline stage and their activeness weakens so that the consumption of carbon source is reduced and the concentration of corn steep liquor is almost constant. Fig. 8 shows the surface tension change of bacterial liquid in the activation process of microorganisms. It can be seen that the surface tension change of bacterial liquid is reduced only a little. Thus, the change of water–oil interface tension is very small depending on the function of microorganisms. 5.2. Oil viscosity change result after interaction with microorganisms The oil viscosity change result after interaction with microorganisms is given in Fig. 9. It can be seen that the viscosity decreases first, then the change of viscosity is not obvious with the increase of the concentration of microorganisms. When the concentration of microorganisms is 2 108 cell/mL, the oil viscosity becomes to 275.4 mPa s and reduces by about 15%. The
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
45
microbial flooding (Fig. 10) shows that the enhanced oil recovery increases with the concentration of microorganisms increases. When the total concentration of microorganisms is 2 108 cell/ml, the enhanced oil recovery is about 8.5%. Besides, the calculation result of indigenous microbial flooding (Fig. 10) indicates that the mathematical model can be used to simulate the process of indigenous microbial flooding. 5.4. Field application results
Fig. 7. Consumption curve of carbon source.
5.4.1. Relative permeability under effect of microorganisms Combining with the empirical formulas of oil–water relative permeability in Block Zhan-3 and Fig. 10, the oil–water relative permeability curve under the effect of indigenous microorganisms (Fig. 11) was calculated, which was used in the numerical simulation of indigenous microbial flooding. Fig. 11 shows that the end point of oil–water relative permeability curve of indigenous in microbial flooding shifts to right and the shift degree increases with the concentration of microorganisms increases. The results indicate that indigenous microbial flooding can reduce the residual oil saturation. Fig. 11 also shows that indigenous microbial flooding can increase the relative permeability of oil phase and decrease the relative permeability of water phase. 5.4.2. Historical fitting result The historical fitting results (Figs. 12 and 13) indicate that the reservoir model can reflect the reality of Block Zhan-3. Thus the
Fig. 8. Surface tension change of bacterial liquid in the activation process of microorganisms.
Fig. 10. Physical simulation result of indigenous microbial flooding.
Fig. 9. Oil viscosity change result after interaction with microorganisms.
result indicates that the microorganisms with optimal concentration can reduce the viscosity of oil sample in Block Zhan-3. 5.3. Verification result of mathematical model The fitting results of growth curve of indigenous microorganisms and consumption curve of carbon source (Figs. 6 and 7) show that the growth and metabolism properties of microorganisms can be well described by the biochemical characteristic equation of microorganisms. The physical simulation result of indigenous
Fig. 11. Oil–water relative permeability curve under the effect of indigenous microorganisms.
46
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
simulation based on this reservoir model will have high reference value. 5.4.3. Optimization and evaluation result of injection parameter Table 2 shows the numerical simulation results of 16 programs and separately lists the cumulative oil production, cumulative oil increment and enhanced oil recovery. It can be seen that the program 11 is better and the value of these three parameters is 0.7% (activating nutrient concentration), 1:12 (liquid–gas ratio of air injection), and 0.3 PV (injection PV). Table 3 shows the economic evaluation results using fuzzy comprehensive evaluation method. From the range values, the major and minor relationship of the three factors can be obtained. For the dimensionless incremental oil, the important degree is injection PV, air injection quantity and activating nutrient concentration in turn; for the dimensionless water reduction, the important degree is air injection quantity, injection PV and activating nutrient concentration in turn; for the dimensionless profit, the important degree is air injection quantity and injection PV, and activating nutrient concentration in turn. Then, according to the expert estimation scores of the important degree of nutrient concentration, air injection quantity and injection PV, the weight values of the three factors are selected as 0.3, 0.3 and 0.4 respectively, and the comprehensive evaluation results are obtained, showed in Table 3. From the range values of comprehensive evaluation, the important degree is injection PV, air injection quantity and nutrient concentration in turn.
Fig. 12. Historical fitting and prediction results of cumulative oil production.
5.4.4. Optimization result of injection mode The prediction result of the three injection modes is given in Table 4. Table 4 shows that the enhanced oil recovery of injecting continuously for the whole cycle and injecting continuously for a half cycle are all better than that of injecting centrally with a high concentration. The microorganisms have a certain cycle of growth,
Fig. 13. Historical fitting and prediction results of total water-cut.
Table 3 Economic evaluation results using fuzzy comprehensive evaluation method. Program no.
Activating nutrient concentration (%)
Air injection quantity (liquid–gas ratio)
Injection PV
Dimensionless incremental oil
Dimensionless water reduction
Dimensionless profit
Comprehensive evaluation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0.14 0.14 0.14 0.14 0.42 0.42 0.42 0.42 0.70 0.70 0.70 0.70 0.98 0.98 0.98 0.98
1:4 1:8 1:12 1:16 1:4 1:8 1:12 1:16 1:4 1:8 1:12 1:16 1:4 1:8 1:12 1:16
0.1 0.2 0.3 0.4 0.2 0.1 0.4 0.3 0.1 0.4 0.3 0.2 0.4 0.3 0.2 0.1
0.00 0.11 0.31 0.63 0.31 0.33 0.75 0.79 0.01 0.99 1.00 0.30 0.08 0.18 0.20 0.15
0.00 0.12 0.41 0.83 0.28 0.32 0.72 1.00 0.12 0.72 0.76 0.52 0.29 0.16 0.30 0.27
0.00 0.11 0.32 0.64 0.31 0.34 0.75 0.79 0.00 0.98 1.00 0.29 0.02 0.14 0.18 0.14
0.00 0.11 0.34 0.69 0.30 0.33 0.74 0.85 0.04 0.90 0.93 0.36 0.12 0.16 0.22 0.18
ki
Evaluation indexes Dimensionless incremental oil
k1 k2 k3 k4 R
Dimensionless water reduction
Dimensionless profit
Comprehensive evaluation
A
B
C
A
B
C
A
B
C
A
B
C
0.26 0.55 0.57 0.15 0.42
0.10 0.40 0.57 0.47 0.46
0.12 0.23 0.57 0.61 0.49
0.34 0.58 0.53 0.25 0.33
0.17 0.33 0.55 0.65 0.48
0.18 0.31 0.58 0.64 0.46
0.27 0.55 0.57 0.12 0.45
0.08 0.39 0.56 0.47 0.48
0.12 0.22 0.56 0.60 0.48
0.29 0.56 0.56 0.17 0.39
0.12 0.38 0.56 0.52 0.44
0.14 0.25 0.57 0.61 0.48
Where A represents activating nutrient concentration; B represents air injection quantity; C represents injection PV; R represents the range of various factors; k1, k2, k3 and k4 represent the comprehensive evaluation values of the three factors and four levels.
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
Table 4 Prediction results of different injection modes. Injection mode
Cumulative oil production (104 t)
Original water flooding 86.34 Injecting continuously 103.78 through the entire cycle Injecting continuously for a 103.41 half cycle Injecting centrally with a 103.18 high concentration
Cumulative oil increment (104 t)
Recovery EOR (%)
(%)
– 17.43
30.62 36.80
– 6.18
17.06
36.67
6.05
16.84
36.59
5.97
metabolism and survival. If the amount of nutrient injected is certain in one cycle, the total amount of microorganisms will also be certain, thus the enhanced oil recovery of indigenous microbial flooding is almost same. However, due to the short time and easy operation of injecting centrally with a high concentration, the third mode was recommended. 5.4.5. Prediction result and field test Considering the reality of Block Zhan-3 and optimization results, the optimal program was obtained. The injection PV of nutrient was 0.3 PV; the injection cycle was 20 days; in each injection cycle, the nutrient with a concentration of 14% (effective content 30%) was injected for 1 day and for the rest 19 days, only water and air were injected. The prediction results of the optimal program are given in Figs. 12 and 13. It can be seen that indigenous microbial flooding can enhance oil recovery by 5.97%, increase oil by 16.84 104 t and the maximum value of water-cut reduction is up to 5.04%. In September 1, 2010, this optimal program was carried out in Block Zhan-3. Up to March 1, 2012, the nutrient has been injected for 0.075 PV. The cumulative oil production was 2.43 104 t which was in agreement with the simulation data of 2.37 104 t. The total water-cut decreased from 92.36% to 91.81% which was in agreement with the simulation data of 91.67%. The field test shows that the indigenous microbial flooding in Block Zhan-3 can achieve better effect of water reduction and oil production increase. The results indicate that the experiment and simulation results are reliable.
6. Conclusions The indigenous microorganisms can be activated successfully under the simulation of high temperature high pressure condition (65 1C, 11 MPa) of Block Zhan-3 when the corn steep liquor and (NH4)2HPO4 are used as carbon source and nitrogen and phosphorus source, respectively. The enhanced oil recovery of indigenous microbial flooding increases with the concentration of microorganisms and when the concentration of microorganisms is about 108 cell/ml, the enhanced oil recovery is up to 8.5%. The mathematical model of indigenous microbial flooding can simulate the migration, growth, metabolism and adsorption properties of indigenous microorganisms. The calculation result of growth of microorganisms and consumption of carbon source are in
47
agreement with experimental data. The mathematical model can also be used to optimize the injection parameters and predict the performance of indigenous microbial flooding. For Block Zhan-3, the optimal total concentration of activating nutrient is 0.7%, the optimal liquid–gas ratio of air injection is 1:12, the optimal injection PV is 0.3 PV, the injection cycle is 20 days (the activating nutrient is injected for 1 day, then only water and air are injected for the rest 19 days), and the predicted value of enhanced oil recovery is 5.97%. Simultaneously, the field test in Block Zhan-3 achieves better effect of water reduction and enhanced oil recovery which indicates that the experiment and simulation results are reliable. The results can effectively provide guidance for the popularization and application of the IMEOR technology.
Acknowledgments This research was supported by the National High Technology Research and Development Program (863) Project of China (no. 2009AA063505). References Adamson, A.W., 1990. Physical Chemistry of Surfaces. John Wiley & Sons Inc., New York. Awan, A.R., Teigland, R., Kleppe, J., 2008. A survey of North Sea enhanced-oilrecovery projects initiated during the years 1975 to 2005. SPEREE 11, 497–512. Bao, M.T., Wang, B., Yuan, C.Z., Li, X.M., 2008. Simulated activation of indigenous microorganisms in Zhan-3 block, Shengli Oilfield. J. Chem. Ind. Eng. 59, 2334–2338. Bird, R.B., Stewart, W.E., Lightfoot, E.N., 2002. Transport Phenomena. John Wiley & Sons, Inc., New York. Chen, Y., Geng, X.L., Xu, P., Tan, Y.X., Wang, W.D., Wang, X., 2006. Metabolic product analysis of microbe oil displacement bacteria in Luo-801 Block. Oil Drilling Prod. Technol. 28, 48–53. Cheng, H.Y., Wang, X.L., Xu, D.T., Ma, G.D., Wang, W.D., 2006. Experiments on EOR employing indigenous microorganisms. Pet. Explor. Dev. 33, 91–95. Cheng, H.Y., Xiao, S.K., Wang, W.D., Zhang, H.S., Wang, X.L., 2005. Application of denaturing gradient gel electrophoresis method to study of oil recovery using indigenous microorganism. Acta Petrolei Sin. 26, 82–85. Dette, H., Melas, V.B., Pepelyshev, A., Strigul, N., 2005. Robust and efficient design of experiments for the Monod model. J. Theor. Biol. 234, 537–550. Donaldson, E.C., 1988. Microbial Enhanced Oil Recovery. Elsevier Science Publishing Company, New York. Kozeny, J., 1927. Uber kapillare Leitung des Wassers in Boden. S. B. Akad. Wiss. Wien Math. Naturwiss 136, 271–306. Lake, L.W., 1989. Enhanced Oil Recovery. Prentice Hall, Inc., Englewood Cliffs, New Jersey. Lei, G.L., Ma, J.Y., Wang, W.D., Guo, S.X., Guo, L.Y., Song, S., 2009. Micromechanism of microbial enhanced oil recovery. J. China Univ. Pet. 33, 108–113. Liu, R.J., Zhang, Y.W., Wen, C.W., Tang, J., 2010. Study on the design and analysis methods of orthogonal experiment. Exp. Technol. Manage. 27, 52–55. Maudgalya, S., Knapp, R.M., McInerney, M.J., 2007. Microbially enhanced oil recovery technologies: a review of the past, present and future. SPE106978. Peaceman, D.W., 1977. Fundamentals of Numerical Reservoir Simulation. Elsevier Science Publishing Company, New York. Rrebecca, S.B., 1996. World wide application of microbial technology for improving oil recovery. SPE35356. Sun, J.Y., Wu, Y., Hao, Y.Z., 2006. Fuzzy comprehensive evaluation model and influence factors analysis on comprehensive performance of green buildings. ESL-IC-06-11-271. Thrasher, D., Puckett, D.A., Davies, A., Beattie, G., Pospisil, G.G., Boccardo, G., Vance, I., Jackson, S., 2010. MEOR from lab to field. SPE129701. Zhang, X.M., She, Y.H., Huang, J.F., Zhang, F., Wang, J., Wang, W.J., Wang, H.Z., Wang, Z.L., 2008. Microbial diversity of the Daqing oilfield after polymer flooding. Chin. J. Appl. Environ. Biol. 14, 668–672.
Contents lists available at SciVerse ScienceDirect
Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol
Laboratory experiment, modeling and field application of indigenous microbial flooding Chuanjin Yao a,n, Guanglun Lei a, Jiye Ma b, Fengmin Zhao c, Gongze Cao c a
School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong, China Research Institute of Exploration and Development, PetroChina Changqing Oilfield Company, Xi’an, Shanxi, China c Research Institute of Oil Production Technology, Sinopec Shengli Oilfield Company, Dongying, Shandong, China b
a r t i c l e i n f o
abstract
Article history: Received 2 January 2011 Accepted 2 April 2012 Available online 24 April 2012
In this study, the indigenous microorganisms in the produced water of Block Zhan-3 in Shengli Oilfield were activated successfully when corn steep liquor and (NH4)2HPO4 was used as carbon source and nitrogen and phosphorus source, respectively. The oil viscosity change after interaction with microorganisms was measured and the physical simulation experiment of indigenous microbial flooding was performed. Based on the main mechanisms of IMEOR (indigenous microbial enhanced oil recovery), a mathematical model of indigenous microbial flooding reflecting the migration, growth, metabolism and adsorption properties of indigenous microorganisms was established. Using the orthogonal design method, the concentration of activating nutrient, air injection quantity, injection PV and injection mode of indigenous microbial flooding were optimized. According to the actual situation of Block Zhan-3, an optimal program was proposed and field test in Block Zhan-3 was conducted. This study shows that the indigenous microbial flooding can achieve better water reduction and enhanced oil recovery effect; by using the mathematical model of indigenous microbial flooding, it is possible to achieve a reliable performance prediction. The results can effectively provide guidance for the popularization and application of the IMEOR technology. & 2012 Elsevier B.V. All rights reserved.
Keywords: indigenous microorganisms physical simulation mathematical model numerical simulation field test enhanced oil recovery
1. Introduction At present, many oilfields in the world have entered the high water-cut stage. However, the decline rate of oil production is quick and the ultimate oil recovery is low. Therefore, how to enhance oil recovery further becomes an urgent question to be solved currently. As a new tertiary recovery technology, the indigenous microbial flooding has many advantages in comparison with other technologies. The indigenous microorganisms have better adaptability to the environment of oil reservoirs. The equipment and the construction are simpler, and the investment is lower. Most important of all, this technology has almost no damage to the oil reservoirs and does not pollute the environment. Thus, the indigenous microbial flooding has a wide application prospect (Cheng et al., 2006; Maudgalya et al., 2007). In recent years, field applications of this technology have been reported in Russia, Norway and so on, and obtained better effect of enhanced oil recovery (Awan et al., 2008; Rrebecca, 1996; Thrasher et al., 2010). In China, this technology has been applied in Oilfields of Daqing, Shengli, Dagang, Xinjiang and so on, and achieved significant effect of water decreasing and oil increasing (Cheng et al., 2005; Chen et al., 2006; Zhang et al., 2008). However, the mechanisms of indigenous microbial enhanced oil recovery
n
Corresponding author. Tel.: þ86 15165283060. E-mail address: [email protected] (C. Yao).
0920-4105/$ - see front matter & 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.petrol.2012.04.001
(IMEOR) and mathematical model of indigenous microbial flooding need to be performed further. In this paper, the indigenous microorganisms in the produced water of Block Zhan-3 in Shengli Oilfield were activated under the laboratory simulation of high temperature high pressure condition (65 1C, 11 MPa) of Block Zhan-3. The growth of microorganisms and consumption of carbon source were investigated through the colony analysis. The oil viscosity change after interaction with microorganisms was measured and the physical simulation experiment of indigenous microbial flooding was performed. A mathematical model of indigenous microbial flooding reflecting the migration, growth, metabolism and adsorption properties of indigenous microorganisms was established and verified with the experimental data. Furthermore, an optimal program of indigenous microbial flooding for Block Zhan-3 was proposed using the mathematical model. At last, field test of indigenous microbial flooding in Block Zhan-3 was conducted.
2. Experimental 2.1. Materials Na2HPO4 (purity above 98.0%), K2HPO4 (purity above 98.0%) and (NH4)2HPO4 (purity above 98.0%) were all purchased from
40
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
Sinopharm Chemical Reagent Co., Ltd. (China). The glucose, sucrose, blend oil, molasses and corn steep liquor were supplied by Qingdao Yihai Food Marketing Company (China). All reagents were used without any further purification. The produced water with salinity of less than 10,000 mg/L, produced oil with viscosity of 324.0 mPa s at 50 1C and produced sand for sandpack models were all obtained from Block Zhan-3 of Shengli Oilfield in China. The initial concentration of microorganisms in the produced water was 103 cell/ml. The microorganisms included the hydrocarbon oxidation bacteria, methane producing bacteria, denitrifying bacteria and sulfate reducing bacteria (Bao et al., 2008).
2.4. Oil viscosity change after interaction with microorganisms The experiment was carried out in a 250 ml conical flask equipped with a breathable rubber plug. First, 50 ml of activating nutrient was introduced into the flask. After sterilization for 30 min in high pressure steam, the flask was vaccinated with 200 ml produced water and cultivated on shaking table with constant temperature of 65 1C for 10 days. Then the vaccine was disposed into different concentration and interacted with the produced oil for 10 days. At last, the viscosity of oil samples was determined by DV III Viscosimeter.
2.2. Equipments 2.5. Physical simulation of indigenous microbial flooding The main equipment was the experimental flow for indigenous microbial flooding, as shown in Fig. 1. The 2PB00C constant-flux pump was supplied by Beijing Satellite Factory, China. The three piston middle vessels, sandpack tubes, pressure sensors and thermostat were all purchased from Petroleum Research Instrument Co., Ltd., Jiangsu, China. Other equipments included the surface tension determinator, hand sling pressure pump, injector, conical flask, anaerobic jar and 1000-fold microscope, DV III viscosimeter (all supplied by Petroleum Research Instrument Co., Ltd., Jiangsu, China). 2.3. Activation of indigenous microorganisms In this experiment, the sandpacks were all sterilized in high pressure steam and saturated by the produced water. The pressure in the thermostat was kept at 11 MPa. Then the following experiments were performed. (1) Activation with different carbon source: different carbon sources (glucose, sucrose, blend oil, molasses and corn steep liquor) with same concentration and air (liquid–gas ratio 1:12) were injected into five sandpacks for 1.0 PV, respectively. (2) Activation with different phosphorus source: different phosphorus sources (Na2HPO4, K2HPO4 and (NH4)2HPO4) with same concentration and air (liquid–gas ratio 1:12) were injected into three sandpacks for 1.0 PV, respectively. (3) Activation with different concentration of optimal carbon source: the optimal carbon source with different concentration and air (fluid–gas ratio 1:12) were injected into six sandpacks for 1.0 PV, respectively. The sandpacks were all placed in the incubator with constant temperature of 65 1C and cultured for 10 days. Then the bacterium fluid samples were taken through nitrogen gas flooding and the colony was analyzed. (4) Growth of microorganisms and consumption of carbon source: the optimal carbon, nitrogen and phosphorus source at optimal concentration and air (liquid–gas ratio 1:12) were injected into a sandpack; then the sandpack was placed in the incubator with constant temperature of 65 1C; then the samples cultured for different time were taken through nitrogen gas flooding and the colony, surface tension and consumption of carbon source were analyzed.
Fig. 1. Experimental flow for indigenous microbial flooding.
In this experiment, the sandpacks were all sterilized in high pressure steam and saturated by the produced water. The sandpacks were then saturated by the produced oil and the original oil saturation was calculated. After aging in incubator with constant temperature 65 1C for 24 h, the produced water was injected into the sandpacks until the water-cut was up to 98% and the residual oil volume (Vr) was calculated. The microorganisms solution with different concentration was then injected with a rate of 1.0 ml/min for 0.5 PV. At last, the produced water was injected into the sandpacks again until the water-cut was up to 98%. Simultaneously, the oil volume output (DV) was recorded and the enhanced oil recovery (EOR) was calculated using the formula: EOR¼ DV/Vr 100%.
3. Mathematical model 3.1. Assumption The assumption conditions for the mathematical model are as follows: (1) the percolation of oil, water and gas obeys Darcy law; (2) the microorganisms, nutrient substances and metabolites are mainly in water phase; (3) the migration, growth, metabolism and adsorption properties of indigenous microorganisms in displacement process are taken into consideration; (4) the carbon source and oxygen are the nutrient substances which restrict the growth of microorganisms and other nutrient substances are sufficient; (5) the variation of rock porosity and permeability, oil viscosity, surface tension and oil–water relative permeability are taken into consideration.
3.2. Biochemical characteristic equation of microorganisms 3.2.1. Growth and reproduction equation of microorganisms In the process of growth and reproduction, the microorganisms have to ingest essential nutrient substances from the surroundings, and release the metabolites. When any one is not enough, the growth of microorganisms will be restricted. The growth and reproduction of microorganisms is described by the Monod model (Dette et al., 2005) S1 S2 m ¼ mmax ð1Þ K 1 þS1 K 2 þ S2 where m is the specific growth rate, h 1; mmax is the maximum value of specific growth rate (determined by experiment), h 1; S1, S2 are the concentrations of nutrients 1 and 2 respectively which limit the growth and reproduction of microorganisms, g/ml; K1, K2 are the saturated constants of nutrients 1 and 2 respectively (determined by experiment).
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
3.2.2. Consumption equation of nutrient substances The consumption rate of nutrient is expressed by the following equation:
g¼
m Y Gi
þ mi
ð2Þ
where g is the consumption rate, h 1; YGi is the production quantity of microorganism consuming unit nutrient i (determined by experiment), cell/g; mi is the consumption rate of nutrient i for the metabolism of microorganisms (determined by experiment), h 1. 3.2.3. Concentration equation of metabolites The concentration of metabolites released by the microorganisms is expressed by the following equation: C j ¼ Y j=X
dX þnj X dt
ð3Þ
where Cj is the production rate of metabolite j, g/(ml h); X is the concentration of microorganisms, cell/ml; Yj/X is the concentration variation of metabolite product j with the variation rate of concentration of microorganisms; nj is the production rate of metabolite product j for microorganisms to maintain their life, h 1. 3.3. Migration equation of microorganisms, nutrient substances and metabolites
41
3.3.2. Migration equation of nutrient substances in porous medium The nutrient substances can move in the porous medium through the carrying of water flow and their convection and proliferation. Besides, they are consumed by the microorganisms and the adsorption on rock surface. Based on the mass balance principle, the migration equation of nutrient substance i is @ðjSw Si Þ ¼ r ðV w Si Þ þ r ðjSw Dsi rSi Þ @t X þðm=Y Gi þ mi ÞX þmi G1 a þbX
ð7Þ
where Dsi is the convection diffusion coefficient of nutrient substance i. 3.3.3. Product migration equation of metabolites in porous medium The metabolites are produced by the microorganisms and suppose all of them dissolved in water phase and can be adsorbed by rock, the migration equation is: @ðjSw C j Þ ¼ r ðV w C j Þ þ r ðjSw Dcj rC j Þ @t ! Cj dX @ 0 þ nj X þ Y j=X G1 0 0 dt @t a þ b Cj where Dcj is the convective diffusion product j (determined by experiment); constant of metabolite product j; ! N0 is tion of metabolite product j adsorbed on
ð8Þ
coefficient of metabolite a0 , b0 are the adsorption the maximum concentraunit rock surface, g/ml.
3.4. Variation equations of physical properties 3.3.1. Migration equation of microorganisms in porous medium In the microbial flooding process, the microorganisms can move in the porous medium through convection and proliferation. Besides, the microorganisms can be adsorbed by the rock surface. The migration equation of microorganisms in porous medium can be expressed by the Fick law (Bird et al., 2002) @X @X @X I ¼ DX þ þ ð4Þ @x @y @z where I is the flow rate of microorganisms through unit area; DX is the convection diffusion coefficient of microorganisms (determined by experiment). The adsorption of microorganisms can be expressed with the Langmuir adsorption formula (Adamson, 1990)
G ¼ G1
X a þ bX
ð5Þ
where ! is the concentration change of microorganisms due to rock adsorption, g/ml; ! N is the maximum concentration of microorganisms when the unit rock volume surface adsorbs the full single-layer microorganisms (determined by experiment), g/ml; a, b are constants (determined by experiment); X is the concentration of microorganisms, g/ml. Based on the balance principle of microorganisms, the migration equation of microorganisms in porous medium can be expressed by the equation: the concentration change of microorganisms in unit time and unit volume¼the concentration change due to the fluid flow þthe concentration change due to diffusionþthe concentration change due to growth and reproductionþthe concentration change due to adsorption. @ðjSw XÞ ¼ r ðV w XÞ þ r @t ðjSw DX rXÞmX þ
@ X G1 @t a þbX
ð6Þ
where Vw is the seepage velocity of water phase, cm/h; Sw is the water saturation; j is the porosity.
When the microorganisms are adsorbed on the rock surface, the rock porosity and permeability will be reduced and the quantity of microorganisms adsorbed by unit rock volume from water phase is m¼
X jSw G1 a þ bX
ð9Þ
Suppose the average volume of microorganism is v, then the total volume of adsorbed microorganisms is V ¼ mv ¼
X G1 jSw v a þ bX
ð10Þ
The proportion of the volume of adsorbed microorganisms according to the rock pore volume is
s¼
V
j
¼
X G1 Sw v a þ bX
ð11Þ
Therefore, the porosity change equation is
j ¼ j0 s ¼ j0
X G1 Sw v a þ bX
ð12Þ
The adsorption of microorganisms on rock surface only changes the rock porosity and capillary tube radius, but the capillary tube number is invariable. According to the capillary flow model of porous medium (Kozeny, 1927), the permeability change equation is 2 j j0 s 2 K ¼ K0 ¼ K0 ð13Þ
j0
j0
The interaction between metabolites and formation fluid causes the viscosity of oil and water to change (Donaldson, 1988). This change can be determined through the fermentation experiment of microorganism and oil. The change relationship can be expressed as ( mwm ¼ f 1 ðSi ,C j ,XÞ ð14Þ mom ¼ f 2 ðC j ,XÞ where mom, mwm are the viscosity of oil and water after interaction with microorganisms, mPa s.
42
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
In the microbial flooding, the water viscosity is related to the concentration of nutrients, metabolites and microorganisms, and the oil viscosity is related to the concentration of metabolites and microorganisms. The metabolites can cause the surface tension between the oil, water and gas and the rock wettability to change. Thus, the capillary pressure changes too. The capillary pressure can be expressed by the Laplace equation 8 P ¼ P w Po ¼ 2swo cos ywo =r > < wo Pog ¼ P o P g ¼ 2sog cos yog =r ð15Þ > : P ¼ P P ¼ 2s cos y =r gw g w gw gw where s, y are the surface tension between oil, gas and water and wetting angle with the rock surface (determined by experiment). 3.5. Percolation equations of oil, water and gas under influence of microorganism The percolation of oil, water and gas in porous media satisfies the three-dimensional and three-phase percolation equations (Lake, 1989; Peaceman, 1977) Oil phase: KK ro ro @ r rPo þqo ¼ ðjSo ro Þ @t Bo mom
r
KK rg rg
mgm
rP g þ
KK ro rgs
mom
!
rPo þ qg ¼
The enhanced oil recovery mechanisms of indigenous microbial flooding are extremely complex. Based on the perspective of seepage mechanics and reservoir engineering, the mechanisms can be explained as three aspects (Lei et al., 2009): (1) the reduction of oil viscosity; (2) the wettability change and the reduction of capillary pressure; (3) the change of oil–water relative permeability and reduction of residual oil saturation. Thus, the variation of seepage parameters due to these 3 aspects is mainly considered in the mathematical model. Fig. 2 shows the concrete method for solving the mathematical model. Based on the numerical simulation software MEOR developed by the Energy Department in the United States, further revision and perfection, the numerical simulation software of indigenous microbial flooding was developed. In order to verify the mathematical model, Eqs. (1) and (2) were used to fit the growth curve of microorganisms and consumption curve of carbon source. Besides, the numerical simulation software of indigenous microbial flooding was used to calculate the enhanced oil recovery of physical simulation of indigenous microbial flooding.
4. Field application 4.1. Overview of Block Zhan-3 ð17Þ
i @h jðSg rg þ So rgs Þ @t
ð18Þ
Due to the absorption of microorganisms and metabolites in pores, the fluid saturation will be changed. The saturations satisfy the subsidiary equation So þ Sg þ Sw þ s ¼ 1
3.6. Solution and verification of mathematical model
ð16Þ
Water phase: KK rw rw @ r rPw þqw ¼ ðjSw rw Þ @t Bw mwm Gas phase:
gas viscosity; s is the porosity occupied by microorganisms and metabolites.
ð19Þ
where q is the output; Kro, Krw, Krq are the permeability of oil, water and gas during microbial flooding respectively; mgm is the
Block Zhan-3 lies in the north of Shaojia Oilfield in Shengli Oilfield and the oil-bearing formation is mainly the first sand body (Dongying Group) of lower tertiary. The oil-bearing area is 1.5 km2, the geological reserves are 282 104 t, the reservoir buried depth is 1240–1360 m and the primary reservoir temperature is 65 1C. The reservoir is composed of sand-shale, pebbled sandstone and interbedded fine sandstone, the oil layers are thin and their distribution is not stable. The oil layer belongs to fluvial deposition with good physical properties. The permeability is 800–1000 10 3 mm2, the porosity is 30% and the initial oil saturation is 0.62. This experimental region has been developed with water flooding for 20 years. Up to September 2010, there are 11 oil wells and 7 water wells, the oil production rate is 31.6 t/d, the total water cut is 92.36%, the cumulative oil production is
Fig. 2. Concrete method for solving the mathematical model.
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
60.23 104 t and the oil recovery is only 21.4%. Now, this experimental region has entered the high water-cut time. However there is still about 70% remaining oil in the layer after water flooding. Thus, it is very important to take measures to enhance oil recovery further. 4.2. Historical fitting and performance prediction The empirical formulas of oil–water relative permeability used in Block Zhan-3 are as follows: ( kro ¼ ð1SwD Þ2:4605 ð20Þ krw ¼ 0:4161 SwD 1:4053 The original irreducible water saturation is 0.471 and the original residual oil saturation is 0.162. Based on the geologic model, fluid model and historical production data, the production history of water flooding in Block Zhan-3 was fitted. 4.3. Optimization of injection parameter In this study, three injection parameters including activating nutrient concentration, air injection quantity (liquid–gas ratio) and injection PV were selected to be optimized. Each parameter had four levels, as shown in Table 1. According to the orthogonal design principle (Sun et al., 2006), 16 programs were designed, as shown in Table 2. The fuzzy comprehensive evaluation model (Liu et al., 2010) was used to select the optimal program and the evaluating indicators include 8 > < dA ¼ ðDQ o DQ omin Þ=ðDQ omax DQ omin Þ dB ¼ ðDf w Df wmin Þ=ðDf wmax Df wmin Þ ð21Þ > : d ¼ ðDpDp Þ=ðDp C min max Dpmin Þ where dA is the dimensionless incremental oil; dB is the dimensionless water-cut reduction; dC is the dimensionless profit; DQo Table 1 Injecting parameters of indigenous microbial flooding. Parameter
Level
Activating nutrient concentration (%) Air injection quantity (liquid–gas ratio) Injection PV
1
2
3
4
0.14 1:4 0.1
0.42 1:8 0.2
0.70 1:12 0.3
0.98 1:16 0.4
43
is the oil increment of current program, t; DQomax is the maximum oil increment of all programs, t; DQomin is the minimum oil increment of all programs, t; Dfw is the water-cut reduction of the current program, %; Dfwmax is the maximum water-cut reduction of all program, %; Dfwmin is the minimum water-cut reduction of all program, %; Dp is the profile of current program, RMB; Dpmax is the maximum profile of all program, RMB; Dpmin is the minimum profile of all programs, RMB. The calculation formula of profit is:p ¼ DN p Po Mg Pg M y P y W, where p is the profit, RMB; DNp is the cumulative oil increment, t; Po is the oil price, RMB; Mg is the time of air injecting, h; Pg is the air price, RMB/h; My is the amount of activating nutrient, t; Py is the activating nutrient price, RMB/t; W is the prophase investment, RMB. 4.4. Optimization of injection mode Three injection modes in this study were considered. Injecting continuously for the whole cycle: under the condition of same amount of activating nutrient and air injected in each cycle, injecting nutrient and air with a low concentration continuously for the whole cycle (20 d). Injecting continuously for a half cycle: under the condition of same amount of activating nutrient and air injected in each cycle, injecting activating nutrient with a higher concentration for a half cycle and the rest half cycle (10 d) only injecting water and air. Injecting centrally with a high concentration: under the condition of injecting same amount of activating nutrient and air in each cycle, injecting activating nutrient with a high concentration for 1 day and after injecting a cycle activating nutrient, the rest time (19 d) only injecting water and air.
5. Results and discussions 5.1. Activation results of indigenous microorganisms The experiment results of activation with different carbon source (Fig. 3) show that taking glucose, sucrose, blend oil or molasses as carbon source, the total concentration of indigenous microorganisms is not very high, only about 104–106 cell/ml. The corn steep liquor can activate the indigenous microorganisms better and the total concentration of microorganisms is up to 108 cell/ml. The results indicate that the corn steep liquor is the optimal carbon source. The experiment results of activation with different concentration of optimal carbon source (Fig. 4) shows
Table 2 Orthogonal design and simulation results of 16 programs. Program no.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Activating nutrient concentration (%)
Air injection quantity (liquid–gas ratio)
0.14 0.14 0.14 0.14 0.42 0.42 0.42 0.42 0.70 0.70 0.70 0.70 0.98 0.98 0.98 0.98
1:4 1:8 1:12 1:16 1:4 1:8 1:12 1:16 1:4 1:8 1:12 1:16 1:4 1:8 1:12 1:16
Injection PV
0.1 0.2 0.3 0.4 0.2 0.1 0.4 0.3 0.1 0.4 0.3 0.2 0.4 0.3 0.2 0.1
Cumulative oil production (104 t)
Cumulative oil increment (104 t)
Recovery
87.26 89.04 92.42 97.62 92.46 92.76 99.62 100.26 87.42 103.58 103.76 92.21 88.58 90.21 90.55 89.69
0.92 2.70 6.08 11.28 6.12 6.42 13.28 13.92 1.08 17.24 17.42 5.87 2.24 3.87 4.21 3.35
30.94 31.58 32.77 34.62 32.79 32.89 35.32 35.55 31.00 36.73 36.80 32.70 31.41 31.99 32.11 31.80
(%)
44
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
Fig. 3. Experiment results of activation with different carbon source.
Fig. 4. Experiment results of activation with different concentration of optimal carbon source.
that the total concentration of microorganisms increases with the increase of the concentration of corn steep liquor; when the concentration of corn steep liquor is 0.3%, the total concentration of microorganisms obtains a higher value of 2 108 cell/ml; when the concentration of corn steep liquor increases further, the increase rate of microorganisms concentration becomes lower. Thus, in order to reduce the investment, the optimal concentration of corn steep liquor is selected as 0.3%. The experiment results of activation with different phosphorus source (Fig. 5) show that the three phosphorus source can all activate the indigenous microorganisms successfully. That is because these three phosphate solutions are all alkaline and can neutralize the pH of produced water so that the water environment is near neutral and suitable for the growth of microorganisms. Among them, the activation effect of (NH4)2HPO4 is better than others and the total concentration of microorganisms is up to 108 cell/ml. The results indicate that (NH4)2HPO4 is the optimal phosphorus source. Besides, the total concentration change of microorganisms is not obvious when the concentration of (NH4)2HPO4 is more than 0.3%. Thus, the optimal concentration of (NH4)2HPO4 is 0.3%. Simultaneously, (NH4)2HPO4 can also provide sufficient nitrogen source for microorganisms. The growth curve of indigenous microorganisms (Fig. 6) shows that under the function of activating nutrient, the total concentration of microorganisms is relatively smaller in the former 5 days; then the microorganisms reproduce rapidly and the total concentration of microorganisms is up to 2 108 cell/ml at about
Fig. 5. Experiment results of activation with different phosphorus sources.
Fig. 6. Growth curve of indigenous microorganisms.
15 days; afterward, the concentration will still keep at the magnitude of 2 108 cell/mL. The results indicate that the activation time for indigenous microorganisms of Block Zhan-3 in sandpack is about 15 days. The consumption curve of carbon source (Fig. 7) shows that the concentration of corn steep liquor decreases obviously with the increase of culture time which indicates that the indigenous microorganisms have been activated and start to reproduce rapidly; the concentration of corn steep liquor becomes a lower level of 0.05% about 15 days which indicates that the growth of microorganisms comes into the decline stage and their activeness weakens so that the consumption of carbon source is reduced and the concentration of corn steep liquor is almost constant. Fig. 8 shows the surface tension change of bacterial liquid in the activation process of microorganisms. It can be seen that the surface tension change of bacterial liquid is reduced only a little. Thus, the change of water–oil interface tension is very small depending on the function of microorganisms. 5.2. Oil viscosity change result after interaction with microorganisms The oil viscosity change result after interaction with microorganisms is given in Fig. 9. It can be seen that the viscosity decreases first, then the change of viscosity is not obvious with the increase of the concentration of microorganisms. When the concentration of microorganisms is 2 108 cell/mL, the oil viscosity becomes to 275.4 mPa s and reduces by about 15%. The
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
45
microbial flooding (Fig. 10) shows that the enhanced oil recovery increases with the concentration of microorganisms increases. When the total concentration of microorganisms is 2 108 cell/ml, the enhanced oil recovery is about 8.5%. Besides, the calculation result of indigenous microbial flooding (Fig. 10) indicates that the mathematical model can be used to simulate the process of indigenous microbial flooding. 5.4. Field application results
Fig. 7. Consumption curve of carbon source.
5.4.1. Relative permeability under effect of microorganisms Combining with the empirical formulas of oil–water relative permeability in Block Zhan-3 and Fig. 10, the oil–water relative permeability curve under the effect of indigenous microorganisms (Fig. 11) was calculated, which was used in the numerical simulation of indigenous microbial flooding. Fig. 11 shows that the end point of oil–water relative permeability curve of indigenous in microbial flooding shifts to right and the shift degree increases with the concentration of microorganisms increases. The results indicate that indigenous microbial flooding can reduce the residual oil saturation. Fig. 11 also shows that indigenous microbial flooding can increase the relative permeability of oil phase and decrease the relative permeability of water phase. 5.4.2. Historical fitting result The historical fitting results (Figs. 12 and 13) indicate that the reservoir model can reflect the reality of Block Zhan-3. Thus the
Fig. 8. Surface tension change of bacterial liquid in the activation process of microorganisms.
Fig. 10. Physical simulation result of indigenous microbial flooding.
Fig. 9. Oil viscosity change result after interaction with microorganisms.
result indicates that the microorganisms with optimal concentration can reduce the viscosity of oil sample in Block Zhan-3. 5.3. Verification result of mathematical model The fitting results of growth curve of indigenous microorganisms and consumption curve of carbon source (Figs. 6 and 7) show that the growth and metabolism properties of microorganisms can be well described by the biochemical characteristic equation of microorganisms. The physical simulation result of indigenous
Fig. 11. Oil–water relative permeability curve under the effect of indigenous microorganisms.
46
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
simulation based on this reservoir model will have high reference value. 5.4.3. Optimization and evaluation result of injection parameter Table 2 shows the numerical simulation results of 16 programs and separately lists the cumulative oil production, cumulative oil increment and enhanced oil recovery. It can be seen that the program 11 is better and the value of these three parameters is 0.7% (activating nutrient concentration), 1:12 (liquid–gas ratio of air injection), and 0.3 PV (injection PV). Table 3 shows the economic evaluation results using fuzzy comprehensive evaluation method. From the range values, the major and minor relationship of the three factors can be obtained. For the dimensionless incremental oil, the important degree is injection PV, air injection quantity and activating nutrient concentration in turn; for the dimensionless water reduction, the important degree is air injection quantity, injection PV and activating nutrient concentration in turn; for the dimensionless profit, the important degree is air injection quantity and injection PV, and activating nutrient concentration in turn. Then, according to the expert estimation scores of the important degree of nutrient concentration, air injection quantity and injection PV, the weight values of the three factors are selected as 0.3, 0.3 and 0.4 respectively, and the comprehensive evaluation results are obtained, showed in Table 3. From the range values of comprehensive evaluation, the important degree is injection PV, air injection quantity and nutrient concentration in turn.
Fig. 12. Historical fitting and prediction results of cumulative oil production.
5.4.4. Optimization result of injection mode The prediction result of the three injection modes is given in Table 4. Table 4 shows that the enhanced oil recovery of injecting continuously for the whole cycle and injecting continuously for a half cycle are all better than that of injecting centrally with a high concentration. The microorganisms have a certain cycle of growth,
Fig. 13. Historical fitting and prediction results of total water-cut.
Table 3 Economic evaluation results using fuzzy comprehensive evaluation method. Program no.
Activating nutrient concentration (%)
Air injection quantity (liquid–gas ratio)
Injection PV
Dimensionless incremental oil
Dimensionless water reduction
Dimensionless profit
Comprehensive evaluation
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0.14 0.14 0.14 0.14 0.42 0.42 0.42 0.42 0.70 0.70 0.70 0.70 0.98 0.98 0.98 0.98
1:4 1:8 1:12 1:16 1:4 1:8 1:12 1:16 1:4 1:8 1:12 1:16 1:4 1:8 1:12 1:16
0.1 0.2 0.3 0.4 0.2 0.1 0.4 0.3 0.1 0.4 0.3 0.2 0.4 0.3 0.2 0.1
0.00 0.11 0.31 0.63 0.31 0.33 0.75 0.79 0.01 0.99 1.00 0.30 0.08 0.18 0.20 0.15
0.00 0.12 0.41 0.83 0.28 0.32 0.72 1.00 0.12 0.72 0.76 0.52 0.29 0.16 0.30 0.27
0.00 0.11 0.32 0.64 0.31 0.34 0.75 0.79 0.00 0.98 1.00 0.29 0.02 0.14 0.18 0.14
0.00 0.11 0.34 0.69 0.30 0.33 0.74 0.85 0.04 0.90 0.93 0.36 0.12 0.16 0.22 0.18
ki
Evaluation indexes Dimensionless incremental oil
k1 k2 k3 k4 R
Dimensionless water reduction
Dimensionless profit
Comprehensive evaluation
A
B
C
A
B
C
A
B
C
A
B
C
0.26 0.55 0.57 0.15 0.42
0.10 0.40 0.57 0.47 0.46
0.12 0.23 0.57 0.61 0.49
0.34 0.58 0.53 0.25 0.33
0.17 0.33 0.55 0.65 0.48
0.18 0.31 0.58 0.64 0.46
0.27 0.55 0.57 0.12 0.45
0.08 0.39 0.56 0.47 0.48
0.12 0.22 0.56 0.60 0.48
0.29 0.56 0.56 0.17 0.39
0.12 0.38 0.56 0.52 0.44
0.14 0.25 0.57 0.61 0.48
Where A represents activating nutrient concentration; B represents air injection quantity; C represents injection PV; R represents the range of various factors; k1, k2, k3 and k4 represent the comprehensive evaluation values of the three factors and four levels.
C. Yao et al. / Journal of Petroleum Science and Engineering 90–91 (2012) 39–47
Table 4 Prediction results of different injection modes. Injection mode
Cumulative oil production (104 t)
Original water flooding 86.34 Injecting continuously 103.78 through the entire cycle Injecting continuously for a 103.41 half cycle Injecting centrally with a 103.18 high concentration
Cumulative oil increment (104 t)
Recovery EOR (%)
(%)
– 17.43
30.62 36.80
– 6.18
17.06
36.67
6.05
16.84
36.59
5.97
metabolism and survival. If the amount of nutrient injected is certain in one cycle, the total amount of microorganisms will also be certain, thus the enhanced oil recovery of indigenous microbial flooding is almost same. However, due to the short time and easy operation of injecting centrally with a high concentration, the third mode was recommended. 5.4.5. Prediction result and field test Considering the reality of Block Zhan-3 and optimization results, the optimal program was obtained. The injection PV of nutrient was 0.3 PV; the injection cycle was 20 days; in each injection cycle, the nutrient with a concentration of 14% (effective content 30%) was injected for 1 day and for the rest 19 days, only water and air were injected. The prediction results of the optimal program are given in Figs. 12 and 13. It can be seen that indigenous microbial flooding can enhance oil recovery by 5.97%, increase oil by 16.84 104 t and the maximum value of water-cut reduction is up to 5.04%. In September 1, 2010, this optimal program was carried out in Block Zhan-3. Up to March 1, 2012, the nutrient has been injected for 0.075 PV. The cumulative oil production was 2.43 104 t which was in agreement with the simulation data of 2.37 104 t. The total water-cut decreased from 92.36% to 91.81% which was in agreement with the simulation data of 91.67%. The field test shows that the indigenous microbial flooding in Block Zhan-3 can achieve better effect of water reduction and oil production increase. The results indicate that the experiment and simulation results are reliable.
6. Conclusions The indigenous microorganisms can be activated successfully under the simulation of high temperature high pressure condition (65 1C, 11 MPa) of Block Zhan-3 when the corn steep liquor and (NH4)2HPO4 are used as carbon source and nitrogen and phosphorus source, respectively. The enhanced oil recovery of indigenous microbial flooding increases with the concentration of microorganisms and when the concentration of microorganisms is about 108 cell/ml, the enhanced oil recovery is up to 8.5%. The mathematical model of indigenous microbial flooding can simulate the migration, growth, metabolism and adsorption properties of indigenous microorganisms. The calculation result of growth of microorganisms and consumption of carbon source are in
47
agreement with experimental data. The mathematical model can also be used to optimize the injection parameters and predict the performance of indigenous microbial flooding. For Block Zhan-3, the optimal total concentration of activating nutrient is 0.7%, the optimal liquid–gas ratio of air injection is 1:12, the optimal injection PV is 0.3 PV, the injection cycle is 20 days (the activating nutrient is injected for 1 day, then only water and air are injected for the rest 19 days), and the predicted value of enhanced oil recovery is 5.97%. Simultaneously, the field test in Block Zhan-3 achieves better effect of water reduction and enhanced oil recovery which indicates that the experiment and simulation results are reliable. The results can effectively provide guidance for the popularization and application of the IMEOR technology.
Acknowledgments This research was supported by the National High Technology Research and Development Program (863) Project of China (no. 2009AA063505). References Adamson, A.W., 1990. Physical Chemistry of Surfaces. John Wiley & Sons Inc., New York. Awan, A.R., Teigland, R., Kleppe, J., 2008. A survey of North Sea enhanced-oilrecovery projects initiated during the years 1975 to 2005. SPEREE 11, 497–512. Bao, M.T., Wang, B., Yuan, C.Z., Li, X.M., 2008. Simulated activation of indigenous microorganisms in Zhan-3 block, Shengli Oilfield. J. Chem. Ind. Eng. 59, 2334–2338. Bird, R.B., Stewart, W.E., Lightfoot, E.N., 2002. Transport Phenomena. John Wiley & Sons, Inc., New York. Chen, Y., Geng, X.L., Xu, P., Tan, Y.X., Wang, W.D., Wang, X., 2006. Metabolic product analysis of microbe oil displacement bacteria in Luo-801 Block. Oil Drilling Prod. Technol. 28, 48–53. Cheng, H.Y., Wang, X.L., Xu, D.T., Ma, G.D., Wang, W.D., 2006. Experiments on EOR employing indigenous microorganisms. Pet. Explor. Dev. 33, 91–95. Cheng, H.Y., Xiao, S.K., Wang, W.D., Zhang, H.S., Wang, X.L., 2005. Application of denaturing gradient gel electrophoresis method to study of oil recovery using indigenous microorganism. Acta Petrolei Sin. 26, 82–85. Dette, H., Melas, V.B., Pepelyshev, A., Strigul, N., 2005. Robust and efficient design of experiments for the Monod model. J. Theor. Biol. 234, 537–550. Donaldson, E.C., 1988. Microbial Enhanced Oil Recovery. Elsevier Science Publishing Company, New York. Kozeny, J., 1927. Uber kapillare Leitung des Wassers in Boden. S. B. Akad. Wiss. Wien Math. Naturwiss 136, 271–306. Lake, L.W., 1989. Enhanced Oil Recovery. Prentice Hall, Inc., Englewood Cliffs, New Jersey. Lei, G.L., Ma, J.Y., Wang, W.D., Guo, S.X., Guo, L.Y., Song, S., 2009. Micromechanism of microbial enhanced oil recovery. J. China Univ. Pet. 33, 108–113. Liu, R.J., Zhang, Y.W., Wen, C.W., Tang, J., 2010. Study on the design and analysis methods of orthogonal experiment. Exp. Technol. Manage. 27, 52–55. Maudgalya, S., Knapp, R.M., McInerney, M.J., 2007. Microbially enhanced oil recovery technologies: a review of the past, present and future. SPE106978. Peaceman, D.W., 1977. Fundamentals of Numerical Reservoir Simulation. Elsevier Science Publishing Company, New York. Rrebecca, S.B., 1996. World wide application of microbial technology for improving oil recovery. SPE35356. Sun, J.Y., Wu, Y., Hao, Y.Z., 2006. Fuzzy comprehensive evaluation model and influence factors analysis on comprehensive performance of green buildings. ESL-IC-06-11-271. Thrasher, D., Puckett, D.A., Davies, A., Beattie, G., Pospisil, G.G., Boccardo, G., Vance, I., Jackson, S., 2010. MEOR from lab to field. SPE129701. Zhang, X.M., She, Y.H., Huang, J.F., Zhang, F., Wang, J., Wang, W.J., Wang, H.Z., Wang, Z.L., 2008. Microbial diversity of the Daqing oilfield after polymer flooding. Chin. J. Appl. Environ. Biol. 14, 668–672.