Article pubs.acs.org/EF
Laboratory Experiment, Production Performance Prediction Model, and Field Application of Multi-slug Microbial Enhanced Oil Recovery Mingming Cheng,* Guanglun Lei,* Jianbo Gao, Tian Xia, and Hongsheng Wang Institute of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, People’s Republic of China ABSTRACT: Because of heterogeneous formations and multi-scale fractures, the accurate evaluation of microbial enhanced oil recovery is faced with numerous difficulties. For the purpose of evaluating the production performance of multi-slug microbial enhanced oil recovery, this work studied the influence of microbial activity on the properties of oil and porous rock and the effect of microbial slug compositions on oil recovery. A mathematical model was built on the basis of the experiment, using the Buckley−Leverett theory and production decline laws. The model is a combination of the characteristic curve of the water cut change and the exponential decline law of oil production, which could predict oilfield production performance, including water breakthrough time, cumulative oil production, and relationship between the water cut and water saturation of the flood front. In comparison to the numerical simulation method, this approach has no additional restrictions because of grid orientation effects, which were found to be suitable for predicting production performance of either large-scale or whole block. The predicted values of the model were compared to the measured values of a field test, indicating that the model predicted the performance of microbial enhanced oil recovery precisely. More specifically, the maximum prediction error of a single well was less than 10%, and the prediction error of the whole block was less than 3%, suggesting the suitability of the model in predicting production performance. In conclusion, it is believed that the multi-slug seems to be a better approach for enhanced oil recovery and that the mathematical model would accurately predict the production performance of microbial enhanced oil recovery.
1. INTRODUCTION The technique that uses microbes or their products to enhance oil recovery is known as microbial enhanced oil recovery (MEOR) and was first proposed by Beckman. Throughout the past several generations, MEOR has become highly developed.1−3 Laboratory experiments have shown that microbes are able to enhance the recovery of residual oil.4−7 Moreover, numerous field trials have shown increased oil production because of microbial activity, indicating the benefits of microorganisms to the decrease of exploitation cost for the petroleum industry.8−11 Zhang et al. reported that, in comparison to chemical and physical treatments, microbial treatments for the prevention of paraffin deposition may offer a non-hazardous and economically viable strategy.12 MEOR has attracted extensive concern from scholars recently.13−15 However, an examination of the literature shows that MEOR generally does not recover as much remaining oil in place as other chemical enhanced oil recovery (EOR) processes, in either the laboratory or the field. Efforts to explain this difference are severely limited by the lack of awareness of the quantitative relationships between microbial performance, reservoir characteristics, and operating conditions (injection rates, residual oil saturation, etc.).16,17 There were both field and laboratory experiments where no effects have been shown.18−20 Nevertheless, the variation characteristics of production performance are an important foundation for the adjustments of development programming and exploitation stage evaluations of oilfields.15,21,22 Therefore, the prediction of indexes such as “water breakthrough time”, “cumulative oil production”, and “liquid yield” occupies a very important position in oilfield development. Numerical simulation is an important predicting methods; an obvious limitation of this © 2014 American Chemical Society
approach is that it causes a significant grid orientation effect in some cases, especially when describing complex fluid flow near the fault. The water cut variation curve is another approach of the reservoir engineering prediction method, but it only predicts the relationship between water cut and flood front water saturation but not the cumulative oil production or cumulative fluid production. The exponential decline method is a prediction method widely used in oilfield development; this method predicts only the variation characteristics of cumulative oil production but not the water cut variation.23−25 Therefore, this work combines the water cut variation curve with the exponential decline method and presents a simultaneous iteration approach to predict the oilfield production performance. In this paper, the influence of microbial activity on oil composition, viscosity, and capillary force were determined through laboratory experiments. On the basis of the Buckley− Leverett theory and statistical regression methods, a dynamic production predication model was established using a range of experimental and field application data; the results of the simultaneous model were validated by experimental and field tests.
2. MATERIALS AND METHODS 2.1. Materials. Fermentative enrichment cultures were obtained from the sludge samples collected from the Triassic sandstones of Chang-6 reservoir in 2009,26 which were stored at 4 °C until use. A single colony with different morphologies was isolated and inoculated on the selection medium.27 The cultures were cultivated in a nutrient Received: July 2, 2014 Revised: September 4, 2014 Published: September 24, 2014 6655
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prepared by artificial Ansai brine at 44.4 °C. The oil used in the experiment was obtained from the first oil production of Changqing oilfield, the physical properties of which are shown in Table 1. The
Table 1. Physical Properties of Oil viscosity (mPa s) viscosity of reservoir oil
viscosity of gas-free oil
freezing point (°C)
1.91
14.15
20.77
artificial Ansai brine used in the experiment was prepared according to the salinity of the brine in the Ansai oilfield, with the total salinity being 112 920 mg L−1; the ion composition is shown in Table 2. 2.2. Equipment. The main equipment was the experimental flow for microbial flooding, as shown in Figure 1. The three-piston middle vessels, pressure sensors, and sandpack tubes were all purchased from Petroleum Research Instrument Co., Ltd., Jiangsu, China. Other equipment included a PL-203 electronic balance, 101 electric heating forced air drying incubator, Texas-500 hanging drop interfacial tensiometer, and Brookfield DV-III viscometer (all supplied by Petroleum Research Instrument Co., Ltd., Jiangsu, China). 2.3. Impact of Microbial Activities on Properties of Oil and Porous Rock. Because the change of oil properties is viewed to be an important process in MEOR, potential changes in the oil viscosity, chemical composition, and interfacial tension because of microbial growth were investigated. The cells from a single colony were inoculated in 50 mL of sterile lysogeny broth in a 250 mL culture flask and incubated at 45 °C for 12 h on an orbital shaker (150 rpm) using an inoculum. Then, 150 mL of nutrient medium was added to a 250 mL Erlenmeyer flask. After sterilization for 30 min in high-pressure steam, the flask was vaccinated with an appropriate aliquot of inoculum. Another flask containing the same volume of the medium without test bacteria was used as a control. Then, 20 mL of filtersterilized oil was added to all flasks, after which the flasks were incubated with shaking at a constant temperature of 45 °C for 5 days. At regular intervals of 24 h, the oil−water interfacial tension and the oil viscosity were monitored. The chemical composition of the oil was measured before and after the culturing, and the capillary force of the oil was calculated using the results of the oil−water interfacial tension. 2.4. Physical Simulation of Multi-slug Microbial Flooding. Core flood experiments were conducted to test the effect of Ansai bacteria on oil recovery using sand pack columns. Columns (5.4 × 35.5 mm) were packed with sand from Ansai block in Changqing oilfield. The sand pack columns were vacuumed and saturated with artificial brine to determine the permeability and porosity. The initial core conditions were created by saturating the column with simulated oil that comprised oil and kerosene (viscosity at 44.4 °C was 1.91 mPa s). The oil was filter-sterilized with a 0.22 μm filter before to use.28−30 After this, the oil-saturated sand pack column was flooded to establish residual oil until the water cut at the outlet reached 60−75% (water cut of the block). The discharged volume of brine from the sand pack column was collected and measured to calculate the remaining oil saturation. After this, columns were inoculated with inoculum in two different phases (i.e., one was inoculated in single slug, and the others were inoculated in multi-slug), and a column without microbial inoculation was selected as a control.31−33 The columns were then shut in and incubated at a constant temperature of 44.4 °C for 3 days, after which the column was flooded until no further oil was discharged in the effluent.34−36
Figure 1. Core flooding experimental model.
3. PRODUCTION PERFORMANCE PREDICTION MODEL The production performance prediction model was established on the basis of the Buckley−Leverett theory, mass conservation, and Darcy’s law. The production performances were predicted for two cases: single-well stimulation model and microbial oil displacement model. The calculation steps are shown in Figure 2. 3.1. Single-Well Stimulation Model. Microbial stimulation technology uses the presence of microbial biomass, compounds produced by the bacteria, or growth of microorganisms to achieve the purpose of paraffin control and plug removal of near wellbore formation. For simplicity, we assume that the effective radius of microbial activity is R, the initial reservoir pressure at the point of R is p, the liquid production capacity is Q, the oil production is Q0, the water cut is f w, oil viscosity is μo, the viscosity of the aqueous phase is μw, the oil relative permeability is Kro, and the water relative permeability is Krw. After the microbial activity, the oil-phase relative permeability turns into Krom (can be derived from experimental data), the oil viscosity turns into μom, and the oil production is increased by ΔQo. The viscosity (mPa s) of microbial degraded oil is calculated by eq 1 μom = μo (1 − n)
(1)
where n is the viscosity reduction rate of degassed oil. This method gives an estimation of the impact of microbial activity on reservoir oil viscosity, assuming that the viscosity reduction rate of reservoir oil is the same as that of the degassed crude oil. The pressure at one point in the water-driven oilfield is basically unchanged for the duration of water flooding (i.e., the pressure at the effective radius of microbial activity is constant before and after the stimulation); only the oil viscosity and as well as cumulative oil production have changed Q oΩo = (Q o + ΔQ o)Ωom
(2)
Table 2. Formation of Water Salinity analysis item (mg L−1) pH value
CO32−
HCO3−
Cl−
SO42−
Ba2+
Ca2+
Mg2+
K+ + Na+
salinity
water type
6.80
0
80
56480
0
650
21000
80
13000
112920
CaCl2
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Figure 2. Concrete method for the production prediction model.
where Ωo is the initial flow resistance of oil, and Ωom is the flow resistance of oil after the microbial activity. The flow resistance is given by the radial flow formula μo R Ωo = ln 2πkK roh Rw (3) Ωom
The time of the water-flood front mass reaching the oil well can be calculated by solving eq 9 t=
(4)
where h is the reservoir thickness and Rw is the oil well radius. The increase of oil production is calculated by substituting eqs 3 and 4 into eq 2. ⎛ μ K ⎞ ΔQ o = ⎜⎜ o rom − 1⎟⎟Q o ⎝ μom K ro ⎠
ER =
μo K rom Q μom K ro o
(5)
(6)
Because the viscosity and flowability of water changes little during the microbial stimulation, the liquid production of a single well can be calculated by assuming the water yield to be constant. ⎛ μ K ⎞ Q m = Q + ⎜⎜ o rom − 1⎟⎟Q o ⎝ μom K ro ⎠
Q+
(
)
− 1 Qo
(12)
k rw = ωSwDδ
(13)
(∂Sw /∂t ) Q dr =− =− f ′w (Sw ) dt ∂Sw /∂r ϕA(r )
Sw − Swi 1 − Swi − Sor
(14)
where Sw is the water saturation, Swi is the irreducible water saturation, Sor is the residual oil saturation, SwD is the standardization of water saturation, λ is the oil-phase relative permeability when Sw = Swi, SwD = 0, ω is the water-phase relative permeability when Sw = 1 − Sor, SwD = 1, and ε and δ are constants that depend upon rock wettability and characteristics of the pore structure. The constant logarithm is taken on both sides of eqs 12 and 13
(8)
After single-well stimulation, the velocity of water flooding is given by the Beckley− Leverett equation, when water saturation is Sw Vsw =
k ro = λ(1 − SwD)ε
SwD =
Qfw μo K rom μom K ro
(11)
(7)
The water cut of the treated wells can be deduced from the Beckley− Leverett theory. fom =
Qt Ns
where ER is the recovery ratio (%), ρo is the crude oil density (g/cm3), Q is the single-well production (kg day−1), and Ns is the geological reserves of a single well (kg), Ns = πr2hϕ(1 − Swc)ρ0/Bo. 3.2. Microbial Oil Displacement Model. For simplicity, we assume that the initial single-well-controlled radius is Re, the reservoir pressure is pe, the liquid production capacity is Q, the oil production is Q0, the water cut is f w, the oil viscosity is μo, the viscosity of the aqueous phase is μw, the oil relative permeability is Kro, and the water relative permeability is Krw. After the microbial activity, the oil viscosity turns into μom and the oil relative permeability turns into Krom. On the basis of the experimental statistical regression, it is believed that the relative permeability of the oil phase in the water-wet reservoir is calculated by the relevant empirical eqs 12 and 13
The oil production because of microbial activity is given by eq 6. Q om =
(10)
where Ro is the initial oil radius and r is the position of the oil− water isosaturation surface at time t. The recovery ratio of water flooding is given by eq 11
μom
R = ln 2πkK romh Rw
(R o 2 − r 2)πϕh f ′w (Swf )Q
(9)
where A(r) is the filtration cross-section area, A(r) = 2πrh, and f ′w(Sw) is given by the water cut trend of water flooding.
log k ro = α + ε log(1 − SwD) 6657
(15)
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log k rw = β + δ log SwD
exponential decline of the microbial-treated block, which is related to the strain species, injection rate, and single-well oil production. The following expression for water breakthrough time is derivable from the Buckley−Leverett theory, assuming that the single-well production is constant:
(16)
where α = 10λ and β = 10ϖ. The residual oil saturation after microbial flooding can be calculated by eq 17
Sor = S′or − Soi(ΔE R )
(17)
where ΔER is the increment of oil recovery because of microbial activity, Soi is the initial oil saturation, S′or is the remaining oil saturation after water flooding, and Sor is the residual oil saturation after microbial flooding. The standardization of water saturation, SwD, can be calculated by the residual oil saturation of microbial flooding, and the correlation coefficients λ, ω, ε, and δ can be calculated by fitting a curve to eqs 15 and 16 according to the relative permeability of the field trail. The oil−water relative permeability equation of a water-wet reservoir can be derived assuming the viscosity of water being constant. The oil production of a single well can be calculated by 2πkK roh pe − pw Qo = ln(R e/R w) μo (18)
T=
(R o 2 − r 2)πϕh f ′w (Swf )Q
(25)
where Ro is the initial oil radius and r is the position of equal saturation at time t. The recovery ratio is given by eq 24 ER =
QT Ns
(26)
where ER is the recovery ratio (%), ρo is the crude oil density (g/cm3), Q is the single-well production (kg day−1), and Ns is the geological reserves of a single well (kg), Ns = πr2hϕ(1 − Swc)ρ0/Bo.
4. RESULTS AND DISCUSSION 4.1. Impact of Microbial Activity on Oil Viscosity. The oil viscosity was monitored along with microbial growth during batch fermentation. It can be observed in Figure 3 that oil viscosity decreased with the increasing shear rate. Furthermore, the decrease of oil viscosity after the action of 3 bacteria was found to be the highest (4.97−25.80%).
The oil production turns into Qom after microbial flooding, and the following expression for the oil production of a single well because of microbial activity is derived: 2πkK romh pe − pw Q om = ln(R e/R w) μom (19) The oil production is given by assuming that the production pressure is constant. μ K Q om = o rom Q o μom K ro (20) The water yield can be calculated by assuming the water yield to be constant. 2πkK rwh pe − pw Qw = ln(R e/R w) μw (21) The liquid production of a single well can be calculated by solving eqs 18 and 19 simultaneously. μ K 2πkK rwh pe − pw + o rom Q o Q m = Q w + Q om = ln(R e/R w) μw μom K ro μo K rom or Q m = Qfw + Q μom K ro o (22)
Figure 3. Pre- and post-treatment performance of oil viscosity.
The water cut of the treated wells is given by eq 18. fom =
The use of residual oil and production of biogas play an important role in the decrease of oil viscosity. Bryant et al. reported that the production of CO2 or CH4 could reduce oil viscosity and that insufficient quantities could facilitate miscible displacement of residual oil.11 The prospect of using residual oil as a carbon source is a significant potential advantage of MEOR. In this case, the cost and logistics of field implementation would likely be closer to those of water flooding than to those of a chemical EOR process. Biosurfactants are another reason for the decrease of oil viscosity. Khyati et al. reported that the emulsifying activity of crude biosurfactants from Bacillus subtilis K1 were found to be highest with hexadecane and decreased with a decrease in the hydrocarbon chain length.37 Taken together, these factors
Qfw Qfw +
μo K rom Qo μom K ro
(23)
The change of oil production is mainly related to the variation of residual oil in the reservoirs. In essence, the production decline law of a thin oil reservoir is almost identical. The process of microbial flooding is equivalent to the process of decreasing oil viscosity. Therefore, the residual oil shows the same exponential descending law Q om(t ) = Q ome−at
(24)
where t is the production time of the microbial-treated block, Qom(t) is the oil production at the moment of t, and a is the 6658
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90°; rock is hydrophobic when θ > 90°; and rock is neutrally wetted when θ = 90°. Reservoir rocks are composed of numerous capillaries, and the capillary force is given by the Laplace equation
indicate that oil viscosity decreases because cell multiplication is likely to be significant. 4.2. Impact of Microbial Activity on Chemical Composition of Oil. Microorganisms take long-chain hydrocarbons, especially the wax components, as a carbon source to maintain their growth.38,39 Most likely, the microbes produce a type of enzyme that can break a long carbon chain into a short carbon chain for better use by the bacteria. The variation of the oil component, especially the decrease of the long carbon chain, is responsible for changes in the properties of the oil; i.e., one significant potential advantage of heavy oil extraction is the prospect of microbial use of residual oil as a carbon source. The component analysis of biodegradable oil is shown in Figure 4 (take 3 strains, for example).
Pc = 2σwo cos θ /r
(27)
where Pc is the capillary force (Pa), σwo is the oil−water interfacial tension (mN/m), θ is the contact angle (deg), and r is the capillary radius (m). The capillary force is the main effort driving hydrocarbons in water-wet reservoirs, because it is going in the same direction as the water drive, and for that reason, the capillary force plays the role of capillary resistance in oil-wet reservoirs. Capillary force curves of the fermentative microorganisms and the brine were conducted to determine the influence of porous rock on oil displacement. The capillary force of the culture supernatant turned positive after incubation for 48 h at 44.4 °C, indicating its high surfactivity. The capillary force of the culture supernatant remained unaltered even upon incubation for 4 days (Figure 5). The capillary force of the
Figure 4. Hydrocarbon composition curve of the pre- and post-treated oil.
As illustrated in Figure 4, the content of hydrocarbon with carbon numbering more than 30 is decreased by 18.36% and the content of light hydrocarbon with carbon numbering less than 30 is increased by 14.62%, indicating that microbial activity has been exerting great impact on oil degradation. We suggest that the microbial cells and their products may play an important role in the recovery of additional oil. The results confirmed the ability of the microorganisms to remove and control paraffin and the possibility of enhancing oil recovery by using residual oil as a carbon source. From a reservoir engineering perspective, there is a clear need to investigate the concentration and transport of microbial cells and their products to determine their efficacy for oil displacement as a function of the concentration and to conclude the dynamic output related to water saturation through an immiscible percolation model. Given the wide gap between field and laboratory oil recoveries for chemical EOR processes and given that MEOR involves the same mechanisms as EOR, the modest lab recoveries reported to date suggest that field applications of MEOR would yield marginal recoveries at best.11 4.3. Impact of Microbial Activity on the Property of Porous Rock. The growth pattern of microorganisms in porous media is most likely a combination of colony-forming units at the pore wall and biofilm formation.40 During the use of residual oil as a carbon source, the microbes will multiply and, in that process, produce EOR chemicals, which will significantly change the property of the porous rock. Wettability, which affects the form of existence of residual oil, is an important property of porous rock. The size of wettability is indicated by the contact angle. Rock is hydrophilic when θ
