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Disposal of Meat, Bonemeal, and Residual Ash by Injection Into Deep Geological Formations
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Chapter 45
DISPOSAL OF MEAT, BONEMEAL, AND RESIDUAL ASH BY INJECTION INTO DEEP GEOLOGICAL FORMATIONS V. Brkic a, I. Omrcena, S. Bukvic a, H. Gotovac b, B. Omrcenc, and M. Zelicc a
INA Oil Industry Plc., Zagreb, Croatia Faculty of Civil Engineering, University of Split, Croatia c Association of Petroleum Engineers and Geologists, Zagreb, Croatia b
45.1
INTRODUCTION
For many years, waste generated in the Republic of Croatia from oil and gas exploration and production has been disposed of by injection into deep geological formations. From our vast experience in oil-industry waste disposal, we can project some possibilities for disposing of waste in other industries, specifically for the disposal of meat and bonemeal (MBM) and residual ash. As we know, since mad cow disease (Bovine Spongiform Encephalopathy, (BSE) first appeared, animal-origin proteins have been forbidden in animal feeds. With this in mind, it is essential that we dispose of existing MBM reserves as well as recently produced MBM in a safe and ecologically appropriate manner. For this purpose, laboratory research has been conducted as preparation for injection of MBM into deep wells. Furthermore, when combusting various types of industrial and municipal waste, the problem of residual ash disposal emerges. Such residual ash contains heavy metals, which (according to newly acquired experience and knowledge from the field) may also be permanently disposed of by deep injection disposal techniques. The chapter presents research into such techniques, as well as the preparation and technological procedures for disposing of the aforementioned waste. Also in this chapter, we will present a model of a plume transport generated by deep aquifer waste injection. In this model, a solute flux, defined as the mass of a solute per time and area unit from the source to the control plane, represents transport within aquifers. Because of the natural heterogeneity of geological formations, groundwater flow and transport are tortuous and unpredictable, and statistically can only be described as a random field. The solute flux is presented in terms of the first two statistical moments (sufficient for most practical purposes) as a space-time process, with time referring to the solute flux breakthrough and space referring to the transverse displacement distribution at the control plane. Velocity fluctuations lead to two basic transport mechanisms: (1) fluctuations on a scale smaller than or equal to the size of a distorted or diffused plume, and (2) fluctuations on a scale larger than a plume size, which cause the plume to “meander” relative to the mean flow direction. Two general transport modeling approaches are used: absolute, which includes both mechanisms, and relative, in which the meandering effects are removed. This transport concept is also useful for both reactive and nonreactive transport. We also present an environmental risk formulation generated from these simulated transport results. This formulation includes the entire process of selecting the risk agent source and its transport through porous media. The calculated risk level incorporates the possibility that deep-well injection could cause undesirable effects on health and environmental resources.
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45.2 HISTORICAL OVERVIEW The exploration of hydrocarbons in the Republic of Croatia started 100 years ago, and significant oil and gas reserves have been discovered. More than 4300 exploration and production wells were drilled into onshore and offshore sedimentary deposits. In the 1990s, Croatia initiated a program for disposing of the waste generated in the process of exploring, drilling, producing, refining, and distributing of hydrocarbons—by deep-well injection into dry exploration or depleted production wells. Deep injection technology enables not only the disposal of waste generated by the oil industry, but also the waste generated in other industries—such as the food, chemical, leather, and pharmaceutical industries. Through deep injection disposal, waste may be permanently and safely isolated into geologically appropriate wells. This technology can also work for disposal of heavy metals (contained in virtually all kinds of waste), and thus can actively support the principles of environmental protection. The overall waste disposal cost and the possibility of heavy metals polluting potable water are thereby reduced. Currently, industrial waste is permanently disposed of by applying a specific procedure developed in Croatia. Regulations on the application of deep-well injection are under preparation. Many dry and depleted wells may be appropriate for permanent disposal of industrial waste generated in various industries, and thus there is little need to pay the cost of creating new wells. However, the existing wells must be upgraded before being used for deep-well injection, and monitoring equipment must be installed. Waste disposal by injection into deep wells is a unique method, one that may be used for permanent disposal of hazardous waste without having an impact on the environment, since waste is disposed out of the biosphere. This chapter also deals with the possibility of waste disposal by incineration, as well as residual ash, meat, and bonemeal disposal (Brkic et al., 2001). 45.3 GEOLOGICAL AND PHYSICAL PROPERTIES OF THE BENICANCI OIL FIELD The Benicanci Oil Field has been designated for the injection of waste fluid. It contains mostly breccia with dolomite detritus (rarely carbonate), and in some upper parts of the formation contains conglomerate breccia of equal composition (see Fig. 45.1). For volume calculations, the value of water saturation Sw 100% has been chosen, since only the wells in the upper parts of the formation or from the less exploited part of the field are considered to be in production (with water saturation above 80%). The dolomite breccia in the Benicanci field is over 300 m thick. Field properties are as follows: ● Mean porosity, Φ 9.4%. ● Effective formation thickness, hef135 m. ● Water saturation, S 100%. w ● Mean permeability, ρ 7.4 × 10−3 µm2. Using the planimeter and the thickness chart, we calculated the volume of the permeable part of Benicanci Field over a 14.5 km2 area, which has well logging data, to be V 1960 × 106 m3. The volume of breccia over the permeable part of the field, for the corresponding drainage radius re 0.564 km, was V 134.8 × 106 m3. The value of expected gradients, using the Eaton formula with variable Poisson coefficient, was a minimal ν 0.22 and maximal ν 0.3. For waste disposal by deep well injection at the Benicanci field, well Be-28 was chosen. Field data are shown in Table 45.1. At this well, an interval from 2141 to 2042 m in depth
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Fig. 45.1. Lithological column of Benicanci formation.
was opened, with the remaining part left as an open hole section (see Fig. 45.2). A chemical wash of this open interval was conducted with 7.5% HCl acid, after which an injectivity test was performed with formation water (Table 45.2). A total of 1,530,863 m3 of waste fluid was injected into the well. After the injection of this waste fluid, the hydrostatic pressure was measured at a depth of 2186 m. The pressure was 186.4 bar.
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Disposal of Meat, Bonemeal, and Residual Ash by Injection Table 45.1. Be-28 well data Field data
Be-28 well
Formation pressure gradient Injection depth Hydrostatic pressure Formation fracture gradient in depth for minimal ν Pressure at injection depth for minimal ν Formation fracture gradient in depth for maximal ν Pressure at injection depth for maximal ν Maximal injection pressure for minimal ν Maximal injection pressure for maximal ν
0.81 bar/10 m 2042 m 200.32 bar 1.17 bar/10 m 238.91 bar 1.36 bar/10 m 277.71 bar 38.59 bar 77.39 bar
45.4 DISPOSAL OF MEAT AND BONEMEAL (MBM) Feed meat and bonemeal (MBM), which contains animal protein and fat (see Table 45.3), has been banned to cattle, sheep, and goats within the European Union (EU) since 1 July 1994. The complete feedstuff ban assumes that contamination cannot be ruled out during the production of feedstuffs, and that current knowledge about transmission paths and quantities of pathogens is incomplete. Our knowledge about the conditions required to adequately destroy the BSE pathogen (infectious prions) must be considered equally incomplete. Infectious prions can currently be detected only when they appear at concentrations of at least a thousandth of the concentrations found in the brain and spinal cord of clinically infected cattle. The most recent research further suggests that the possibility of BSE being transmitted to humans is likely, and much evidence indicates a link to a new variant, called CreutzfeldJacob Disease (nvCJD). Even if findings do not yet constitute proof-positive that BSE can be transmitted to humans, the circumstantial evidence has become compelling. It should be noted that samples of brain from infected hamsters revealed traces of infectiousness even after thermal treatment at 600°C. A possible explanation under discussion is an inorganic “molecular template” capable of triggering a biological replication of the pathogen. Consequently, disposal to landfill is not an option, because simply burying the material cannot destroy all potential BSE pathogens. In addition, this form of disposal is banned for highly organic materials by EU legislation (Landfills Ordinance). Croatia produces yearly about 15,000 tonnes of MBM by processing waste of animal origin. There was no evidence of BSE-pathogen in the Croatian MBM stockpile, according to BSE-analysis conducted by the Croatian Institute of Veterinary Medicine. So far, the problem of managing the MBM has not been approached in an ecologically acceptable way: since kilns lack some basic technical requirements, they are not in a position to take over the old stock for incineration. By a resolution of the Government of the Republic of Croatia in August 2002, it has been ordered that an appropriate solution for managing the accumulated MBM stockpile should be found. MBM is a substance that is the result of thermal treatment of animal dead bodies and of waste of animal origin. MBM, the subject of this chapter, has been thermally processed by pressure sterilization of waste (after maceration into small pieces) at least 133°C for at least 20 minutes and at a water vapor pressure of 3 bar and higher. This procedure results in MBM particles with a size up to 5 mm. From Table 45.3, we can clearly see that MBM is more than half protein, with 10% fat and the rest inorganic substances. We must emphasize that in the process of MBM
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Fig. 45.2. Benicanci-28 well construction.
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Disposal of Meat, Bonemeal, and Residual Ash by Injection Table 45.2. Injectivity test results Condition
Injected volume (m3)
Flow (l/min)
Injection pressure (bar)
1 2 3 4
10 20 30 40
200 400 600 800
0.7 3.4 10.3–13.8 20.7–34.5
Table 45.3. MBM substances Substance
Quantity (%)
Proteins Raw fat Raw ash Phosphorous Calcium
56 10 21 6.1 12
Table 45.4. Pollutants in MBM Substance
Concentration (mg/kg)
Lead Mercury Cadmium Chrome Copper Nickel Zinc Arsenic
4.25 0.18 0.43 2.6 12.0 3.1 110 0.3
incineration in Croatian cement kilns, excessive amounts of chloride and phosphates have been found in MBM samples. Note that most of the chloride in MBM is present as NaCl (common salt). The composition of the ash, which represents ⬃21% of MBM content, shows high levels of phosphorous and calcium. According to BSE analysis, it should be emphasized that there was no evidence of the BSE pathogen in MBM. (The pollutant content of various MBM samples is given in Table 45.4.) This analysis shows that MBM has a low pollutant content which, added to fact that there is no evidence of the BSE pathogen in MBM, indicates that MBM should not be classified as hazardous waste. Moreover, during the preparatory testing, MBM proved to have very good miscibility in organic acids and in concentrated HCl. Miscibility in different solvents is very important for deep-well injection, since in that way coagulation is inhibited and many problems within the well can be avoided.
45.5 DISPOSAL OF RESIDUAL ASH In the process of hazardous and nonhazardous waste incineration, smaller quantities of waste are generated with regard to the initial quantity. Brkic and Omrcen (2003). Specifically,
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two types of waste are thus generated: slag and residual ash, which has disposal restrictions because of its hazardous effect. Residual ash is a gray to dark gray powder, very light, of 0.365 g/cm3 density. Based on granulometric analysis, about 50% of the residual ash particles are smaller than 10 µm, while only 2% of particles are smaller than 2 µm. Chemical analysis of residual ash from hazardous waste incineration plants has shown that residual ash samples contain Na2CO3, namely sodium bicarbonate, NaCl, Na2SO4 and relatively large quantities of heavy metals. The residual ash analysis is shown in Table 45.5. The results of the residual ash analysis have shown that it is a hazardous waste, one that must not be disposed of at nonhazardous disposal sites (landfills). As concluded from further laboratory research, residual ash is best dissolved in water at a room temperature of 20°C. Such aqueous solutions have shown an alkaline reaction, and therefore they must not be discharged into the environment. The results of the research are provided in Table 45.6.
Table 45.5. The chemical analysis of residual ash Parameter
Results
Density Dry substance Si Al Fe Mn Ca Mg Na K S Cl Ash (800°C) TOC As Cu Zn Cd Cr Mn Ni Pb Fe Hg
0.357 kg/m3 99.06% 0.68 % 0.14% 5.26% 1.17% 0.32% 30.22% 0.21% 2.88% 11.35% 63.50% 0.14% 2.8 mg/kg 1621.0 mg/kg 4794.9 mg/kg 1 mg/kg 189.4 mg/kg 398.0 mg/kg 1851.3 mg/kg 1313.8 mg/kg 60,913.7 mg/kg 5.5 mg/kg
Table 45.6. Residual ash solubility in different solvents at 20°C Solvent
Solvent/residual ash ratio
Solubility (%)
10% HCl 10% HCl 3% HF Brinewater
2:1 3:1 5:1
49.3 79.2 96.2
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45.6 TRANSPORT MODELING AND RISK EVALUATION 45.6.1 Theory and Assumptions At the moment t 0 (see Fig. 45.3), an industrial fluid is injected into the injection zone (x 0). For t 0, a plume is formed and transported along the main flow path by advection (local dispersion can be neglected) toward the control plane (during analysis, it is wise to have several planes). The control plane is usually vertical with respect to the direction of average velocity; in this two-dimensional presentation, it is represented by a line. Because of fluctuations on a scale smaller or equal to the injection zone, the plume has changed its shape in a random, stochastic way. Velocity fluctuations on the scale greater than the injection zone advectively carry the plume as a whole (plume meandering). Hence, the main physical factor in the whole process is the velocity fluctuations and the scale on which they occur. While fluctuations on the smallest scale change the shape of the plume, at the same time, on the larger scale, the plume translation proceeds. This process has two mechanisms that can be observed separately, and thus transport must be defined in two ways: absolute, where both mechanisms exist; and relative, where the meandering effect is removed. Generally, the approach used here is based on the solute flux approach (Dagen et al., 1992) and not on the resident concentration. This approach has proved to be more practical and shows transport properties in a much more direct way than the classical approach based on resident concentration. There are two key random variables: the specific mass flux (q), defined as a solute mass passing through the unit area of the control plane over unit time (M/L2 T1); and solute discharge (Q), defined as a total mass passing through the control plane over unit time (M/T1). In this chapter, the focus is on the variable Q, described with the travel time τ, the time in which a mass particle travels from the injection zone to the control plane. The transport is described in the relative dispersion framework (Andricevic and
Fig. 45.3. The underground plume dispersion.
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Cvetkovic, 1998), where each realization has the same travel time, equaling the average travel time from absolute dispersion. Solute discharge is calculated by Q (t; x) 兰 ρ0 (α)δ (tτ) dα,
(45.1)
A
where ρ0(α) represents the density along the injection zone (M/L), τ is the travel time from the injection zone point y α to the control plane chosen for a set x, dα is the area element, δ is Dirac’s function, and A is the injection zone. Moments for Q can be calculated as follows: 〈Q(t; x)〉 兰 ρ0(α) g1 (t; x, a) dα , A
(45.2)
where 〈 〉 represents the mathematical expectation operator and gl is the probability density function (pdf) of travel time for one particle, which is defined as the probability that the particle travels from y α and t 0 through the control plane in a set x and in time t. The second moment, variance, can be obtained by decomposition of variable Q to its average value and fluctuation around it (Q 〈Q〉 Q), so that the variance can be given as σ 20 〈Q2〉 〈Q2典 〈Q〉2. Since the second part of the equation can easily be obtained by Equation (45.2), only the first one remains to be defined: 〈Q 2 (t; x)〉 兰兰 ρ0 (α) ρ0 (α) g2 (t, t; x, α , α ) dα dα ,
(45.3)
A
where the function g2 presents the pdf of travel time for two particles, defined as the probability that one particle travels from y α and t 0 through the control plane in a set x and in time t, while the other travels from y α and t 0 through the control plane in a set x and in time t. The main problem in theoretical analysis is to define the pdf for both Equations (45.2) and (45.3). The usual approach is to assume a log-normal distribution (Cvetkovic et al., 1992) and then calculate the corresponding statistical moments. However, to define pdf and obtain the above-mentioned equations, we must define the first two moments for the travel time: 〈τ〉 x/U,
1 σ U2 2 τ
冕冕 C (εε, 0) dε dε , x x
u
0 0
1 σττ U2
冕冕 C (εε, αα) dε dε, x x
(45.4)
u
0 0
where Cu represents the longitudinal velocity covariance obtained according to the first-order theory (Rubin, 1990) and separation α–α ′ is the initial separation between two particles in the injection zone for t 0 and x 0. Therefore, for the given velocity covariance (obtained analytically or numerically), the pdf of travel time for one and two particles can be calculated by Equation (45.4). Solute discharge can be calculated by Equations (45.2) and (45.3). Relative dispersion (Andricevic and Cvetkovic, 1998) is a physical concept of transport modeling. It evaluates the spreading of the plume with regard to its center of mass and not to a set coordinate system. In this way, a plume is described that does not have meandering and that is the result of the indefiniteness of the average flow velocity. Transport modeling of a substance via relative dispersion enables us to have a better perspective of real physical dispersion of the plume and the maximal concentration that can occur in the specific time. That type of transport estimation should be used in risk analysis, because it gives us a realistic view of underground behavior. In the case of relative dispersion, plume meandering is
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removed, and only fluctuations on the scale equal to or smaller than the injection zone are taken into consideration. Equations (45.1)–(45.4) are the same for the relative dispersion, but plume meandering should be disregarded. In the modern world, interest in risk evaluation (Andricevic and Cvetkovic, 1996) is increasing every day. Through its basic definition, risk represents the possibility of hazardous occurrence. In this chapter, a risk quantification approach has been used that relates to the risk of exceeding a control point (plane or volume) defined in time or space. By applying the risk of exceedance concept to the transport of an injected waste in a deep geological formation, we are trying to evaluate the probability of the plume spreading into shallow water-bearing layers, which could be used as a water supply in the future. That is called the natural or inherent risk, or the excess risk. In other words, we are looking for risk as a probability that the pollution plume will reach the first or second hydrogeological zone. If L is the variable that represents the size of vertical migration of the injected waste, and C is the variable that represents the level of the geological horizon, which cannot come into contact with waste (that is the depth where shallow water-bearing layers end), then the risk can be given as the following equation: ∞
∞
R* P (C L) ⇒ R* 兰 [ 兰 f(L) dL] g(C) dC, ∞ C
(45.5)
where f (L) and g(C) stand for the distribution functions of L and C. The plume travels through the underground, advectively carried by the main flow and spreading due to the influence of mechanical dispersion, which arises primarily because of the velocity fluctuations on different scales. Since all these processes are very slow, at the beginning there is no risk for shallow water-bearing layers. With time, however, the plume spreads and, over the long time of transport simulation (10,000 years), the risk increases. For each control plane, the plume has the biggest spreading for the average travel time, in other words, in the instant of passing through the control plane. 45.6.2 Modeling Transport modeling of the underground is a very complex process. It requires evaluation of a large number of calculation steps, ending with quantifying the risk of threatening the shallow water-bearing layers. The most demanding step is gathering of input data from all available and often diverse sources. The most important input data are piezometric or pressure conditions in the injection zone and the confining zone or low permeability layers above that zone. Since the stochastic calculation method (Dagan, 1989) is being used, determination of those fields is quite difficult, because not only their values but their variability patterns are unknown. Usually, more types of exploratory work are used for determining the permeability field: measuring of electrical resistance, 3-D seismic, logging while drilling, laboratory measurements on cores, pressure drop down and pressure rise in the open hole. The permeability of the whole field can be determined by a combination of various types of measurements. Unfortunately, most of them are not done in deep wells, so some new, sophisticated methods are used that convert a small number of input data into a stochastic form. One of those very effective methods involves determining permeability from a combination of electrical resistance and permeability measurements, connected by analogy to Darcy’s and Archie’s experimental laws (Purvance and Andricevic, 2000a, b). A second commonly used method involves using geostatistics for determining the stochastic parameters of permeability fields and piezometric head (Deutsch and Journel, 1992). That approach is used in this chapter. In 1990, based on the three wells where the hydraulic
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permeability was measured and the 12 wells where piezometric head was measured in 1990, a geostatistical analysis of the piezometric head was performed, using a universal kriging method. The results are given in the form of first two moments: average value (Fig. 45.4) and double standard deviation (Fig. 45.5). For each point, a confidence interval of 95% is given, within which the piezometric head is to be found. On the basis of these results, it can be concluded from Figure 45.4 that the hydraulic gradient is around 0.01 in the north–south direction. By correlating permeability and piezometric head (Kitanidis, 1988, 1997), the mean field of hydraulic permeability was estimated by a cokriging method (Fig. 45.6). The field has been obtained from optimal isotropic exponential variograms, given by the residual concept
Fig. 45.4. Piezometric head field, 1990, mean value.
Fig. 45.5. Piezometric head field, 1990, double standard deviation.
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Fig. 45.6. Field of hydraulic permeability, mean value.
(Kitanidis, 1997), and has the average value 〈K〉 1.85 106 (m/s), with a variance of 0.90 and a range from 3.57 105 (m/s) to 2.06 1010 (m/s), which shows that the range from 0.6 to 1600 103 µm2 that was obtained by measurement at the Benicanci Oil Field is contained in that estimated field. Both the variability of the mean field and the zones of low or high permeability are shown (Fig. 45.6). What is interesting is that all three wells where the measuring was done have relatively uniform values—from just these three measurements alone, we would never have been able to conclude that such a variability in permeability fields exists. The injection zone is located in the Benicanci Oil Field at a depth of 1900–2200 m, with intervals of dolomite breccia containing high secondary porosity and marl. A substantial loss of drilling fluid occurred in the drilling process. The waste was disposed of into the formation, where pore pressure is lower than the hydrostatic pressure. Using this method, the waste can be disposed of without further processing and grinding, and any injection of slurried solids will not result in fracturing of the receiving formation. The effective thickness of the injection zone is in some parts over 300 m, whereas the average effective thickness obtained by kriging has the value of 135 m. Technological fluid is injected under pressure (lower than formation fracture pressure), increasing the pore volume. The injection volume is about 77,000 m3. The density of the injected fluid is between 1006 and 1020 kg/m3, and the length of the injection zone is 135 m. Integral or correlational length is estimated as one-tenth of the observed area (Gelhar, 1993). Shallow water-bearing layers that are not to be endangered lie at a depth of 550 m. The calculation of plume transport is done after gathering and processing the input data. Horizontal flow is assumed in the injection zone, since there is no vertical hydraulic gradient. Transport is considered in absolute and relative forms. These two types of transport are complementary, with each providing data that is not given in the other form. In relative dispersion, we remove the meandering; each realization has the same travel time. That is why the mean plume is very similar to the ones from single realizations, which also results in a far more realistic maximal solute discharge than in the case of absolute transport.
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On the other hand, relative dispersion does not give us information on all possible locations of the plume, because every realization is moved so as to correspond to the average travel time from absolute dispersion. That means that in relative dispersion (Andricevic and Cvetkovic, 1998), the real dimensions of the plume in time and space and the solute discharge are determined, while in absolute dispersion (Cvetkovic et al., 1992; Dagan et al., 1992) we determine the reach of the plume in the horizontal direction for the given time of 10,000 years. The calculation of absolute and relative dispersion for the control plane X 60 km is given in Figure 45.7. After 10,000 years, the maximum reach of the plume is 60 km, maximum solute discharge is 80,000 kg/year, and the standard deviation is 25,000 kg/year. Transverse spreading of the plume for the same control plane is shown in Figure 45.8. This first phase of risk calculation is given for transport under the condition that the entire ground above the injection zone has the properties of dolomite breccia. That condition is unrealistically conservative, very unfavorable for risk, for the sake of safety (upper limit). In the two-dimensional analysis, it can be seen that the largest transverse spreading corresponds with the moment the plume center transits the control plane. Therefore, Equation (45.5) defines the excess risk for every control plane and the corresponding moment when the plume center is in transit. The excess risk for six different control planes is given in Table 45.7. For planes close to the injection zone, there is no danger and the risk is very small, as expected. For remote planes, the excess risk increases, and for a maximum calculated time of 10,000 years, it would be 3.75 104. From the transverse results of plume spreading for the same period of 10,000 years, the solute discharge would decrease to 104 of its initial value. The second phase of risk calculation incorporates a far more realistic geological interpretation of the area above the injection zone. Generally, on the upper side, the injection zone is bounded by the confining formation, creating a low-permeability zone that reduces the transverse spreading of the plume and thus also reduces the risk. The input data for this calculation phase are: ● The ground above the injection zone has lower permeability than the ground within the injection zone, the roof formation consists mostly of clay and marl, and literature and experience show K 108 (m/s) and an effective porosity of 1%. ● The spreading in the vertical direction in the low-permeability roof formation is caused by the pressure difference between the injection zone and the roof formation; gradient is i 0.001. ● On the basis of relative dispersion calculations, we estimate that the mass entering the confining formation in the first 1000 years is about 20% of the total injected mass, and that the plume has spread under the confining formation over 20 integral lengths (ca. 4 km). It is assumed that the plume spreads toward shallow water-bearing formations with the above-mentioned gradient. ● The variance is the same as in the first phase of calculations (0.90), and the integral length has been reduced to 20 m, owing to the influence of anisotropy in the vertical direction. ● The distance between the injection zone and shallow water-bearing formations is about 1350 m. ● The calculation of absolute dispersion will be applied because of the determination of the reach of a maximum plume extension in the vertical direction within the next 9000 years. (Note: relative dispersion describes only the physical dispersion within the plume and does not account for a plume meandering.) The calculation of plume expansion in the vertical direction is given in Figure 45.9. If we consider the average value function from Figure 45.9 as the distribution function of
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Fig. 45.7. Analysis of absolute (a) and relative dispersion (b) for control plane X 60 (km).
transverse plume spreading f(L), then, using the function f(C) (here as Dirac’s function defined for t 9000 years) and Equation (45.5), we can calculate the excess risk for 10,000 years as 0.5 1022. This risk is very low in comparison with the first phase of the calculation. From this calculation, the risk can be considered totally acceptable and the waste management process entirely safe due to the extremely small risk of having the waste
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Fig. 45.8. Two-dimensional transversal expansion of the plume.
appearing in the upper aquifers. It results directly from the influence of the low-permeability confining formation on plume spreading. Figure 45.9 shows that the plume practically has not reached the water-bearing formations after 9000 years. That is the reason for the reduced excess risk. It should also be mentioned that, besides this analytical way of calculating
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Disposal of Meat, Bonemeal, and Residual Ash by Injection Table 45.7. Exceeding risk in characteristic control planes Control plane (m)
Time (year)
Risk (dimensionless)
2000 5000 10,000 20,000 40,000 60,000
250 750 1600 3200 6200 10,000
4.22 1031 6.54 1023 1.84 1011 9.31 109 3.95 105 3.75 104
Fig. 45.9. Transverse plume spreading.
transport, there exist far more complex numerical calculations that employ the Monte Carlo method (Hassan et al., 1997, 2001).
45.7 CONCLUSION Numerous technological, financial, and ecological advantages with respect to existing waste disposal methods have been achieved through application of the procedure described herein for permanent waste disposal by deep-well injection. Our risk assessment modeling indicates that the use of deep-well injection into dry abandoned wells is also an appropriate method for the disposal of waste from other industries and incineration plants, as well as biosolids like MBM. Our company has a number of such wells at its disposal. Transport modeling of the injected waste is a very complex process supported by a stochastic approach that is based on mass flux rather than on the resident concentration. It requires evaluation of a large number of calculation steps, including the preparation of input data, geostatistical interpretation, and calculation of absolute and relative dispersion—and concludes with quantifying the exceedance risk of threatening the shallow water-bearing layers.
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International experience has been taken into account when developing the project documentation for waste disposal by deep-well injection. Furthermore, deep-well injection, for safe and permanent disposal into geologically appropriate formations, not only applies to waste generated in the petroleum industry, but also could apply to waste from other industries and incineration plants, as well as biosolids like MBM (if there is no evidence of the BSE pathogen). By using up-to-date injection-well equipment with permanent monitoring, the environment is fully protected. From an environmental perspective, the deep injection of waste is the only disposal option that effectively removes waste from the biosphere, minimizing its effect on human health. Disposal is complete, and there is no residual waste product that must be disposed of—future liabilities are thus minimized. All other forms of disposal place the waste either into the air; or into landfills located above the water table; or into rivers and streams that serve as recreation facilities, wildlife habitats, and sources of food and drinking water. Also, surface reclamation can be total, in comparison with the common remediation strategy of collecting and capping the waste on site, which may require perpetual monitoring and maintenance. Exceedance risk, which we have calculated in this chapter, is extremely small and does not represent a danger when the well is used for injection of various types of waste, such as residual ash and MBM. Since the risk limit of 106 is often taken as acceptable in risk assessment analysis, the risk we have calculated in this chapter is considerably smaller than that standard. That risk does not represent a danger when using the well for injection of various types of waste, such as residual ash and MBM. The risk assessment results have confirmed that deep-well injection is one of the more acceptable alternatives for disposal of various types of waste. In the near future, waste injection will be expected to compete favorably with other disposal technologies in the areas of economics, time, and public relations.
REFERENCES Andricevic, R. and Cvetkovic, V., 1996. Evaluation of risk from contaminants migrating by groundwater. Water Resour. Res., 32(3): 611–621. Andricevic, R. and Cvetkovic, V., 1998. Relative dispersion for solute flux in aquifers., J. Fluid Mech., 361: 145–174. Brkic, V. and Omrcen, I., 2003. Disposal of mercury-sulfide and residual ash by deep well injection. SPE 80579, SPE Exploration and Production Environmental Conference. San Antonio, TX. Brkic, V., Omrcen, B. and Loncaric, B., 2001. Waste disposal by injection into deep wells. Proc. 23rd ASME Energy Sources Technology Conference and Exposition, 2001. Houston, TX, USA. Cvetkovic, V., Shapiro, A.M. and Dagan, G., 1992. Solute flux approach to transport in heterogeneous formations, 2, Uncertainty analysis. Water Resour. Res., 28(5): 1377–1388. Dagan, G., 1989. Flow and Transport in Porous Media. Springer, New York. Dagan, G., Cvetkovic, V. and Shapiro, A.M., 1992. Solute flux approach to transport in heterogeneous formations, 1, The general framework. Water Resour. Res., 28(5): 1369–1376. Deutsch, C.V. and Journel, A.G., 1992. GSLIB, Geostatistical Software Library and User’s Guide. Oxford University Press, New York. Gelhar, L.W., 1993. Stochastic Subsurface Hydrology. Prentice–Hall, Englewood Cliffs, NY.
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Hassan, A.E., Andric evic´, R. and Cvetkovic, V., 2001. Computational issues in the determination of solute discharge moments and implications for comparison to analytical solutions. Adv. Water Resour., 24: 607–619. Hassan, A.E., Cushman, J.H. and Delleur, J.W., 1997. Monte–Carlo studies of flow and transport in fractal conductivity fields: comparison with stochastic perturbation theory. Water Resour. Res., 33(11): 2519–2534. Kitanidis, P.K., 1988. Prediction by the method of moments of transport in a heterogeneous formations. J. Hydrol., 102: 453–473. Kitanidis, P.K., 1997. Introduction to Geostatistics. Cambridge University Press, Cambridge. Purvance, D.T. and Andricevic, R., 2000a. On the electrical–hydraulic conductivity correlation in aquifers. Water Resour. Res., 36(10): 2905–2913. Purvance, D.T. and Andricevic, R., 2000b. Geoelectric characterization of the hydraulic conductivity field and its spatial structure at variable scales. Water Resour. Res., 36(10): 2915–2924. Rubin, Y., 1990. Stochastic analysis of macrodispersion in heterogeneous porous media. Water Resour. Res., 26(1): 133–141.
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Chapter 45
DISPOSAL OF MEAT, BONEMEAL, AND RESIDUAL ASH BY INJECTION INTO DEEP GEOLOGICAL FORMATIONS V. Brkic a, I. Omrcena, S. Bukvic a, H. Gotovac b, B. Omrcenc, and M. Zelicc a
INA Oil Industry Plc., Zagreb, Croatia Faculty of Civil Engineering, University of Split, Croatia c Association of Petroleum Engineers and Geologists, Zagreb, Croatia b
45.1
INTRODUCTION
For many years, waste generated in the Republic of Croatia from oil and gas exploration and production has been disposed of by injection into deep geological formations. From our vast experience in oil-industry waste disposal, we can project some possibilities for disposing of waste in other industries, specifically for the disposal of meat and bonemeal (MBM) and residual ash. As we know, since mad cow disease (Bovine Spongiform Encephalopathy, (BSE) first appeared, animal-origin proteins have been forbidden in animal feeds. With this in mind, it is essential that we dispose of existing MBM reserves as well as recently produced MBM in a safe and ecologically appropriate manner. For this purpose, laboratory research has been conducted as preparation for injection of MBM into deep wells. Furthermore, when combusting various types of industrial and municipal waste, the problem of residual ash disposal emerges. Such residual ash contains heavy metals, which (according to newly acquired experience and knowledge from the field) may also be permanently disposed of by deep injection disposal techniques. The chapter presents research into such techniques, as well as the preparation and technological procedures for disposing of the aforementioned waste. Also in this chapter, we will present a model of a plume transport generated by deep aquifer waste injection. In this model, a solute flux, defined as the mass of a solute per time and area unit from the source to the control plane, represents transport within aquifers. Because of the natural heterogeneity of geological formations, groundwater flow and transport are tortuous and unpredictable, and statistically can only be described as a random field. The solute flux is presented in terms of the first two statistical moments (sufficient for most practical purposes) as a space-time process, with time referring to the solute flux breakthrough and space referring to the transverse displacement distribution at the control plane. Velocity fluctuations lead to two basic transport mechanisms: (1) fluctuations on a scale smaller than or equal to the size of a distorted or diffused plume, and (2) fluctuations on a scale larger than a plume size, which cause the plume to “meander” relative to the mean flow direction. Two general transport modeling approaches are used: absolute, which includes both mechanisms, and relative, in which the meandering effects are removed. This transport concept is also useful for both reactive and nonreactive transport. We also present an environmental risk formulation generated from these simulated transport results. This formulation includes the entire process of selecting the risk agent source and its transport through porous media. The calculated risk level incorporates the possibility that deep-well injection could cause undesirable effects on health and environmental resources.
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45.2 HISTORICAL OVERVIEW The exploration of hydrocarbons in the Republic of Croatia started 100 years ago, and significant oil and gas reserves have been discovered. More than 4300 exploration and production wells were drilled into onshore and offshore sedimentary deposits. In the 1990s, Croatia initiated a program for disposing of the waste generated in the process of exploring, drilling, producing, refining, and distributing of hydrocarbons—by deep-well injection into dry exploration or depleted production wells. Deep injection technology enables not only the disposal of waste generated by the oil industry, but also the waste generated in other industries—such as the food, chemical, leather, and pharmaceutical industries. Through deep injection disposal, waste may be permanently and safely isolated into geologically appropriate wells. This technology can also work for disposal of heavy metals (contained in virtually all kinds of waste), and thus can actively support the principles of environmental protection. The overall waste disposal cost and the possibility of heavy metals polluting potable water are thereby reduced. Currently, industrial waste is permanently disposed of by applying a specific procedure developed in Croatia. Regulations on the application of deep-well injection are under preparation. Many dry and depleted wells may be appropriate for permanent disposal of industrial waste generated in various industries, and thus there is little need to pay the cost of creating new wells. However, the existing wells must be upgraded before being used for deep-well injection, and monitoring equipment must be installed. Waste disposal by injection into deep wells is a unique method, one that may be used for permanent disposal of hazardous waste without having an impact on the environment, since waste is disposed out of the biosphere. This chapter also deals with the possibility of waste disposal by incineration, as well as residual ash, meat, and bonemeal disposal (Brkic et al., 2001). 45.3 GEOLOGICAL AND PHYSICAL PROPERTIES OF THE BENICANCI OIL FIELD The Benicanci Oil Field has been designated for the injection of waste fluid. It contains mostly breccia with dolomite detritus (rarely carbonate), and in some upper parts of the formation contains conglomerate breccia of equal composition (see Fig. 45.1). For volume calculations, the value of water saturation Sw 100% has been chosen, since only the wells in the upper parts of the formation or from the less exploited part of the field are considered to be in production (with water saturation above 80%). The dolomite breccia in the Benicanci field is over 300 m thick. Field properties are as follows: ● Mean porosity, Φ 9.4%. ● Effective formation thickness, hef135 m. ● Water saturation, S 100%. w ● Mean permeability, ρ 7.4 × 10−3 µm2. Using the planimeter and the thickness chart, we calculated the volume of the permeable part of Benicanci Field over a 14.5 km2 area, which has well logging data, to be V 1960 × 106 m3. The volume of breccia over the permeable part of the field, for the corresponding drainage radius re 0.564 km, was V 134.8 × 106 m3. The value of expected gradients, using the Eaton formula with variable Poisson coefficient, was a minimal ν 0.22 and maximal ν 0.3. For waste disposal by deep well injection at the Benicanci field, well Be-28 was chosen. Field data are shown in Table 45.1. At this well, an interval from 2141 to 2042 m in depth
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Fig. 45.1. Lithological column of Benicanci formation.
was opened, with the remaining part left as an open hole section (see Fig. 45.2). A chemical wash of this open interval was conducted with 7.5% HCl acid, after which an injectivity test was performed with formation water (Table 45.2). A total of 1,530,863 m3 of waste fluid was injected into the well. After the injection of this waste fluid, the hydrostatic pressure was measured at a depth of 2186 m. The pressure was 186.4 bar.
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Be-28 well
Formation pressure gradient Injection depth Hydrostatic pressure Formation fracture gradient in depth for minimal ν Pressure at injection depth for minimal ν Formation fracture gradient in depth for maximal ν Pressure at injection depth for maximal ν Maximal injection pressure for minimal ν Maximal injection pressure for maximal ν
0.81 bar/10 m 2042 m 200.32 bar 1.17 bar/10 m 238.91 bar 1.36 bar/10 m 277.71 bar 38.59 bar 77.39 bar
45.4 DISPOSAL OF MEAT AND BONEMEAL (MBM) Feed meat and bonemeal (MBM), which contains animal protein and fat (see Table 45.3), has been banned to cattle, sheep, and goats within the European Union (EU) since 1 July 1994. The complete feedstuff ban assumes that contamination cannot be ruled out during the production of feedstuffs, and that current knowledge about transmission paths and quantities of pathogens is incomplete. Our knowledge about the conditions required to adequately destroy the BSE pathogen (infectious prions) must be considered equally incomplete. Infectious prions can currently be detected only when they appear at concentrations of at least a thousandth of the concentrations found in the brain and spinal cord of clinically infected cattle. The most recent research further suggests that the possibility of BSE being transmitted to humans is likely, and much evidence indicates a link to a new variant, called CreutzfeldJacob Disease (nvCJD). Even if findings do not yet constitute proof-positive that BSE can be transmitted to humans, the circumstantial evidence has become compelling. It should be noted that samples of brain from infected hamsters revealed traces of infectiousness even after thermal treatment at 600°C. A possible explanation under discussion is an inorganic “molecular template” capable of triggering a biological replication of the pathogen. Consequently, disposal to landfill is not an option, because simply burying the material cannot destroy all potential BSE pathogens. In addition, this form of disposal is banned for highly organic materials by EU legislation (Landfills Ordinance). Croatia produces yearly about 15,000 tonnes of MBM by processing waste of animal origin. There was no evidence of BSE-pathogen in the Croatian MBM stockpile, according to BSE-analysis conducted by the Croatian Institute of Veterinary Medicine. So far, the problem of managing the MBM has not been approached in an ecologically acceptable way: since kilns lack some basic technical requirements, they are not in a position to take over the old stock for incineration. By a resolution of the Government of the Republic of Croatia in August 2002, it has been ordered that an appropriate solution for managing the accumulated MBM stockpile should be found. MBM is a substance that is the result of thermal treatment of animal dead bodies and of waste of animal origin. MBM, the subject of this chapter, has been thermally processed by pressure sterilization of waste (after maceration into small pieces) at least 133°C for at least 20 minutes and at a water vapor pressure of 3 bar and higher. This procedure results in MBM particles with a size up to 5 mm. From Table 45.3, we can clearly see that MBM is more than half protein, with 10% fat and the rest inorganic substances. We must emphasize that in the process of MBM
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Fig. 45.2. Benicanci-28 well construction.
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Injected volume (m3)
Flow (l/min)
Injection pressure (bar)
1 2 3 4
10 20 30 40
200 400 600 800
0.7 3.4 10.3–13.8 20.7–34.5
Table 45.3. MBM substances Substance
Quantity (%)
Proteins Raw fat Raw ash Phosphorous Calcium
56 10 21 6.1 12
Table 45.4. Pollutants in MBM Substance
Concentration (mg/kg)
Lead Mercury Cadmium Chrome Copper Nickel Zinc Arsenic
4.25 0.18 0.43 2.6 12.0 3.1 110 0.3
incineration in Croatian cement kilns, excessive amounts of chloride and phosphates have been found in MBM samples. Note that most of the chloride in MBM is present as NaCl (common salt). The composition of the ash, which represents ⬃21% of MBM content, shows high levels of phosphorous and calcium. According to BSE analysis, it should be emphasized that there was no evidence of the BSE pathogen in MBM. (The pollutant content of various MBM samples is given in Table 45.4.) This analysis shows that MBM has a low pollutant content which, added to fact that there is no evidence of the BSE pathogen in MBM, indicates that MBM should not be classified as hazardous waste. Moreover, during the preparatory testing, MBM proved to have very good miscibility in organic acids and in concentrated HCl. Miscibility in different solvents is very important for deep-well injection, since in that way coagulation is inhibited and many problems within the well can be avoided.
45.5 DISPOSAL OF RESIDUAL ASH In the process of hazardous and nonhazardous waste incineration, smaller quantities of waste are generated with regard to the initial quantity. Brkic and Omrcen (2003). Specifically,
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two types of waste are thus generated: slag and residual ash, which has disposal restrictions because of its hazardous effect. Residual ash is a gray to dark gray powder, very light, of 0.365 g/cm3 density. Based on granulometric analysis, about 50% of the residual ash particles are smaller than 10 µm, while only 2% of particles are smaller than 2 µm. Chemical analysis of residual ash from hazardous waste incineration plants has shown that residual ash samples contain Na2CO3, namely sodium bicarbonate, NaCl, Na2SO4 and relatively large quantities of heavy metals. The residual ash analysis is shown in Table 45.5. The results of the residual ash analysis have shown that it is a hazardous waste, one that must not be disposed of at nonhazardous disposal sites (landfills). As concluded from further laboratory research, residual ash is best dissolved in water at a room temperature of 20°C. Such aqueous solutions have shown an alkaline reaction, and therefore they must not be discharged into the environment. The results of the research are provided in Table 45.6.
Table 45.5. The chemical analysis of residual ash Parameter
Results
Density Dry substance Si Al Fe Mn Ca Mg Na K S Cl Ash (800°C) TOC As Cu Zn Cd Cr Mn Ni Pb Fe Hg
0.357 kg/m3 99.06% 0.68 % 0.14% 5.26% 1.17% 0.32% 30.22% 0.21% 2.88% 11.35% 63.50% 0.14% 2.8 mg/kg 1621.0 mg/kg 4794.9 mg/kg 1 mg/kg 189.4 mg/kg 398.0 mg/kg 1851.3 mg/kg 1313.8 mg/kg 60,913.7 mg/kg 5.5 mg/kg
Table 45.6. Residual ash solubility in different solvents at 20°C Solvent
Solvent/residual ash ratio
Solubility (%)
10% HCl 10% HCl 3% HF Brinewater
2:1 3:1 5:1
49.3 79.2 96.2
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45.6 TRANSPORT MODELING AND RISK EVALUATION 45.6.1 Theory and Assumptions At the moment t 0 (see Fig. 45.3), an industrial fluid is injected into the injection zone (x 0). For t 0, a plume is formed and transported along the main flow path by advection (local dispersion can be neglected) toward the control plane (during analysis, it is wise to have several planes). The control plane is usually vertical with respect to the direction of average velocity; in this two-dimensional presentation, it is represented by a line. Because of fluctuations on a scale smaller or equal to the injection zone, the plume has changed its shape in a random, stochastic way. Velocity fluctuations on the scale greater than the injection zone advectively carry the plume as a whole (plume meandering). Hence, the main physical factor in the whole process is the velocity fluctuations and the scale on which they occur. While fluctuations on the smallest scale change the shape of the plume, at the same time, on the larger scale, the plume translation proceeds. This process has two mechanisms that can be observed separately, and thus transport must be defined in two ways: absolute, where both mechanisms exist; and relative, where the meandering effect is removed. Generally, the approach used here is based on the solute flux approach (Dagen et al., 1992) and not on the resident concentration. This approach has proved to be more practical and shows transport properties in a much more direct way than the classical approach based on resident concentration. There are two key random variables: the specific mass flux (q), defined as a solute mass passing through the unit area of the control plane over unit time (M/L2 T1); and solute discharge (Q), defined as a total mass passing through the control plane over unit time (M/T1). In this chapter, the focus is on the variable Q, described with the travel time τ, the time in which a mass particle travels from the injection zone to the control plane. The transport is described in the relative dispersion framework (Andricevic and
Fig. 45.3. The underground plume dispersion.
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Cvetkovic, 1998), where each realization has the same travel time, equaling the average travel time from absolute dispersion. Solute discharge is calculated by Q (t; x) 兰 ρ0 (α)δ (tτ) dα,
(45.1)
A
where ρ0(α) represents the density along the injection zone (M/L), τ is the travel time from the injection zone point y α to the control plane chosen for a set x, dα is the area element, δ is Dirac’s function, and A is the injection zone. Moments for Q can be calculated as follows: 〈Q(t; x)〉 兰 ρ0(α) g1 (t; x, a) dα , A
(45.2)
where 〈 〉 represents the mathematical expectation operator and gl is the probability density function (pdf) of travel time for one particle, which is defined as the probability that the particle travels from y α and t 0 through the control plane in a set x and in time t. The second moment, variance, can be obtained by decomposition of variable Q to its average value and fluctuation around it (Q 〈Q〉 Q), so that the variance can be given as σ 20 〈Q2〉 〈Q2典 〈Q〉2. Since the second part of the equation can easily be obtained by Equation (45.2), only the first one remains to be defined: 〈Q 2 (t; x)〉 兰兰 ρ0 (α) ρ0 (α) g2 (t, t; x, α , α ) dα dα ,
(45.3)
A
where the function g2 presents the pdf of travel time for two particles, defined as the probability that one particle travels from y α and t 0 through the control plane in a set x and in time t, while the other travels from y α and t 0 through the control plane in a set x and in time t. The main problem in theoretical analysis is to define the pdf for both Equations (45.2) and (45.3). The usual approach is to assume a log-normal distribution (Cvetkovic et al., 1992) and then calculate the corresponding statistical moments. However, to define pdf and obtain the above-mentioned equations, we must define the first two moments for the travel time: 〈τ〉 x/U,
1 σ U2 2 τ
冕冕 C (εε, 0) dε dε , x x
u
0 0
1 σττ U2
冕冕 C (εε, αα) dε dε, x x
(45.4)
u
0 0
where Cu represents the longitudinal velocity covariance obtained according to the first-order theory (Rubin, 1990) and separation α–α ′ is the initial separation between two particles in the injection zone for t 0 and x 0. Therefore, for the given velocity covariance (obtained analytically or numerically), the pdf of travel time for one and two particles can be calculated by Equation (45.4). Solute discharge can be calculated by Equations (45.2) and (45.3). Relative dispersion (Andricevic and Cvetkovic, 1998) is a physical concept of transport modeling. It evaluates the spreading of the plume with regard to its center of mass and not to a set coordinate system. In this way, a plume is described that does not have meandering and that is the result of the indefiniteness of the average flow velocity. Transport modeling of a substance via relative dispersion enables us to have a better perspective of real physical dispersion of the plume and the maximal concentration that can occur in the specific time. That type of transport estimation should be used in risk analysis, because it gives us a realistic view of underground behavior. In the case of relative dispersion, plume meandering is
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removed, and only fluctuations on the scale equal to or smaller than the injection zone are taken into consideration. Equations (45.1)–(45.4) are the same for the relative dispersion, but plume meandering should be disregarded. In the modern world, interest in risk evaluation (Andricevic and Cvetkovic, 1996) is increasing every day. Through its basic definition, risk represents the possibility of hazardous occurrence. In this chapter, a risk quantification approach has been used that relates to the risk of exceeding a control point (plane or volume) defined in time or space. By applying the risk of exceedance concept to the transport of an injected waste in a deep geological formation, we are trying to evaluate the probability of the plume spreading into shallow water-bearing layers, which could be used as a water supply in the future. That is called the natural or inherent risk, or the excess risk. In other words, we are looking for risk as a probability that the pollution plume will reach the first or second hydrogeological zone. If L is the variable that represents the size of vertical migration of the injected waste, and C is the variable that represents the level of the geological horizon, which cannot come into contact with waste (that is the depth where shallow water-bearing layers end), then the risk can be given as the following equation: ∞
∞
R* P (C L) ⇒ R* 兰 [ 兰 f(L) dL] g(C) dC, ∞ C
(45.5)
where f (L) and g(C) stand for the distribution functions of L and C. The plume travels through the underground, advectively carried by the main flow and spreading due to the influence of mechanical dispersion, which arises primarily because of the velocity fluctuations on different scales. Since all these processes are very slow, at the beginning there is no risk for shallow water-bearing layers. With time, however, the plume spreads and, over the long time of transport simulation (10,000 years), the risk increases. For each control plane, the plume has the biggest spreading for the average travel time, in other words, in the instant of passing through the control plane. 45.6.2 Modeling Transport modeling of the underground is a very complex process. It requires evaluation of a large number of calculation steps, ending with quantifying the risk of threatening the shallow water-bearing layers. The most demanding step is gathering of input data from all available and often diverse sources. The most important input data are piezometric or pressure conditions in the injection zone and the confining zone or low permeability layers above that zone. Since the stochastic calculation method (Dagan, 1989) is being used, determination of those fields is quite difficult, because not only their values but their variability patterns are unknown. Usually, more types of exploratory work are used for determining the permeability field: measuring of electrical resistance, 3-D seismic, logging while drilling, laboratory measurements on cores, pressure drop down and pressure rise in the open hole. The permeability of the whole field can be determined by a combination of various types of measurements. Unfortunately, most of them are not done in deep wells, so some new, sophisticated methods are used that convert a small number of input data into a stochastic form. One of those very effective methods involves determining permeability from a combination of electrical resistance and permeability measurements, connected by analogy to Darcy’s and Archie’s experimental laws (Purvance and Andricevic, 2000a, b). A second commonly used method involves using geostatistics for determining the stochastic parameters of permeability fields and piezometric head (Deutsch and Journel, 1992). That approach is used in this chapter. In 1990, based on the three wells where the hydraulic
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permeability was measured and the 12 wells where piezometric head was measured in 1990, a geostatistical analysis of the piezometric head was performed, using a universal kriging method. The results are given in the form of first two moments: average value (Fig. 45.4) and double standard deviation (Fig. 45.5). For each point, a confidence interval of 95% is given, within which the piezometric head is to be found. On the basis of these results, it can be concluded from Figure 45.4 that the hydraulic gradient is around 0.01 in the north–south direction. By correlating permeability and piezometric head (Kitanidis, 1988, 1997), the mean field of hydraulic permeability was estimated by a cokriging method (Fig. 45.6). The field has been obtained from optimal isotropic exponential variograms, given by the residual concept
Fig. 45.4. Piezometric head field, 1990, mean value.
Fig. 45.5. Piezometric head field, 1990, double standard deviation.
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Fig. 45.6. Field of hydraulic permeability, mean value.
(Kitanidis, 1997), and has the average value 〈K〉 1.85 106 (m/s), with a variance of 0.90 and a range from 3.57 105 (m/s) to 2.06 1010 (m/s), which shows that the range from 0.6 to 1600 103 µm2 that was obtained by measurement at the Benicanci Oil Field is contained in that estimated field. Both the variability of the mean field and the zones of low or high permeability are shown (Fig. 45.6). What is interesting is that all three wells where the measuring was done have relatively uniform values—from just these three measurements alone, we would never have been able to conclude that such a variability in permeability fields exists. The injection zone is located in the Benicanci Oil Field at a depth of 1900–2200 m, with intervals of dolomite breccia containing high secondary porosity and marl. A substantial loss of drilling fluid occurred in the drilling process. The waste was disposed of into the formation, where pore pressure is lower than the hydrostatic pressure. Using this method, the waste can be disposed of without further processing and grinding, and any injection of slurried solids will not result in fracturing of the receiving formation. The effective thickness of the injection zone is in some parts over 300 m, whereas the average effective thickness obtained by kriging has the value of 135 m. Technological fluid is injected under pressure (lower than formation fracture pressure), increasing the pore volume. The injection volume is about 77,000 m3. The density of the injected fluid is between 1006 and 1020 kg/m3, and the length of the injection zone is 135 m. Integral or correlational length is estimated as one-tenth of the observed area (Gelhar, 1993). Shallow water-bearing layers that are not to be endangered lie at a depth of 550 m. The calculation of plume transport is done after gathering and processing the input data. Horizontal flow is assumed in the injection zone, since there is no vertical hydraulic gradient. Transport is considered in absolute and relative forms. These two types of transport are complementary, with each providing data that is not given in the other form. In relative dispersion, we remove the meandering; each realization has the same travel time. That is why the mean plume is very similar to the ones from single realizations, which also results in a far more realistic maximal solute discharge than in the case of absolute transport.
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On the other hand, relative dispersion does not give us information on all possible locations of the plume, because every realization is moved so as to correspond to the average travel time from absolute dispersion. That means that in relative dispersion (Andricevic and Cvetkovic, 1998), the real dimensions of the plume in time and space and the solute discharge are determined, while in absolute dispersion (Cvetkovic et al., 1992; Dagan et al., 1992) we determine the reach of the plume in the horizontal direction for the given time of 10,000 years. The calculation of absolute and relative dispersion for the control plane X 60 km is given in Figure 45.7. After 10,000 years, the maximum reach of the plume is 60 km, maximum solute discharge is 80,000 kg/year, and the standard deviation is 25,000 kg/year. Transverse spreading of the plume for the same control plane is shown in Figure 45.8. This first phase of risk calculation is given for transport under the condition that the entire ground above the injection zone has the properties of dolomite breccia. That condition is unrealistically conservative, very unfavorable for risk, for the sake of safety (upper limit). In the two-dimensional analysis, it can be seen that the largest transverse spreading corresponds with the moment the plume center transits the control plane. Therefore, Equation (45.5) defines the excess risk for every control plane and the corresponding moment when the plume center is in transit. The excess risk for six different control planes is given in Table 45.7. For planes close to the injection zone, there is no danger and the risk is very small, as expected. For remote planes, the excess risk increases, and for a maximum calculated time of 10,000 years, it would be 3.75 104. From the transverse results of plume spreading for the same period of 10,000 years, the solute discharge would decrease to 104 of its initial value. The second phase of risk calculation incorporates a far more realistic geological interpretation of the area above the injection zone. Generally, on the upper side, the injection zone is bounded by the confining formation, creating a low-permeability zone that reduces the transverse spreading of the plume and thus also reduces the risk. The input data for this calculation phase are: ● The ground above the injection zone has lower permeability than the ground within the injection zone, the roof formation consists mostly of clay and marl, and literature and experience show K 108 (m/s) and an effective porosity of 1%. ● The spreading in the vertical direction in the low-permeability roof formation is caused by the pressure difference between the injection zone and the roof formation; gradient is i 0.001. ● On the basis of relative dispersion calculations, we estimate that the mass entering the confining formation in the first 1000 years is about 20% of the total injected mass, and that the plume has spread under the confining formation over 20 integral lengths (ca. 4 km). It is assumed that the plume spreads toward shallow water-bearing formations with the above-mentioned gradient. ● The variance is the same as in the first phase of calculations (0.90), and the integral length has been reduced to 20 m, owing to the influence of anisotropy in the vertical direction. ● The distance between the injection zone and shallow water-bearing formations is about 1350 m. ● The calculation of absolute dispersion will be applied because of the determination of the reach of a maximum plume extension in the vertical direction within the next 9000 years. (Note: relative dispersion describes only the physical dispersion within the plume and does not account for a plume meandering.) The calculation of plume expansion in the vertical direction is given in Figure 45.9. If we consider the average value function from Figure 45.9 as the distribution function of
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Fig. 45.7. Analysis of absolute (a) and relative dispersion (b) for control plane X 60 (km).
transverse plume spreading f(L), then, using the function f(C) (here as Dirac’s function defined for t 9000 years) and Equation (45.5), we can calculate the excess risk for 10,000 years as 0.5 1022. This risk is very low in comparison with the first phase of the calculation. From this calculation, the risk can be considered totally acceptable and the waste management process entirely safe due to the extremely small risk of having the waste
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Fig. 45.8. Two-dimensional transversal expansion of the plume.
appearing in the upper aquifers. It results directly from the influence of the low-permeability confining formation on plume spreading. Figure 45.9 shows that the plume practically has not reached the water-bearing formations after 9000 years. That is the reason for the reduced excess risk. It should also be mentioned that, besides this analytical way of calculating
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Time (year)
Risk (dimensionless)
2000 5000 10,000 20,000 40,000 60,000
250 750 1600 3200 6200 10,000
4.22 1031 6.54 1023 1.84 1011 9.31 109 3.95 105 3.75 104
Fig. 45.9. Transverse plume spreading.
transport, there exist far more complex numerical calculations that employ the Monte Carlo method (Hassan et al., 1997, 2001).
45.7 CONCLUSION Numerous technological, financial, and ecological advantages with respect to existing waste disposal methods have been achieved through application of the procedure described herein for permanent waste disposal by deep-well injection. Our risk assessment modeling indicates that the use of deep-well injection into dry abandoned wells is also an appropriate method for the disposal of waste from other industries and incineration plants, as well as biosolids like MBM. Our company has a number of such wells at its disposal. Transport modeling of the injected waste is a very complex process supported by a stochastic approach that is based on mass flux rather than on the resident concentration. It requires evaluation of a large number of calculation steps, including the preparation of input data, geostatistical interpretation, and calculation of absolute and relative dispersion—and concludes with quantifying the exceedance risk of threatening the shallow water-bearing layers.
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International experience has been taken into account when developing the project documentation for waste disposal by deep-well injection. Furthermore, deep-well injection, for safe and permanent disposal into geologically appropriate formations, not only applies to waste generated in the petroleum industry, but also could apply to waste from other industries and incineration plants, as well as biosolids like MBM (if there is no evidence of the BSE pathogen). By using up-to-date injection-well equipment with permanent monitoring, the environment is fully protected. From an environmental perspective, the deep injection of waste is the only disposal option that effectively removes waste from the biosphere, minimizing its effect on human health. Disposal is complete, and there is no residual waste product that must be disposed of—future liabilities are thus minimized. All other forms of disposal place the waste either into the air; or into landfills located above the water table; or into rivers and streams that serve as recreation facilities, wildlife habitats, and sources of food and drinking water. Also, surface reclamation can be total, in comparison with the common remediation strategy of collecting and capping the waste on site, which may require perpetual monitoring and maintenance. Exceedance risk, which we have calculated in this chapter, is extremely small and does not represent a danger when the well is used for injection of various types of waste, such as residual ash and MBM. Since the risk limit of 106 is often taken as acceptable in risk assessment analysis, the risk we have calculated in this chapter is considerably smaller than that standard. That risk does not represent a danger when using the well for injection of various types of waste, such as residual ash and MBM. The risk assessment results have confirmed that deep-well injection is one of the more acceptable alternatives for disposal of various types of waste. In the near future, waste injection will be expected to compete favorably with other disposal technologies in the areas of economics, time, and public relations.
REFERENCES Andricevic, R. and Cvetkovic, V., 1996. Evaluation of risk from contaminants migrating by groundwater. Water Resour. Res., 32(3): 611–621. Andricevic, R. and Cvetkovic, V., 1998. Relative dispersion for solute flux in aquifers., J. Fluid Mech., 361: 145–174. Brkic, V. and Omrcen, I., 2003. Disposal of mercury-sulfide and residual ash by deep well injection. SPE 80579, SPE Exploration and Production Environmental Conference. San Antonio, TX. Brkic, V., Omrcen, B. and Loncaric, B., 2001. Waste disposal by injection into deep wells. Proc. 23rd ASME Energy Sources Technology Conference and Exposition, 2001. Houston, TX, USA. Cvetkovic, V., Shapiro, A.M. and Dagan, G., 1992. Solute flux approach to transport in heterogeneous formations, 2, Uncertainty analysis. Water Resour. Res., 28(5): 1377–1388. Dagan, G., 1989. Flow and Transport in Porous Media. Springer, New York. Dagan, G., Cvetkovic, V. and Shapiro, A.M., 1992. Solute flux approach to transport in heterogeneous formations, 1, The general framework. Water Resour. Res., 28(5): 1369–1376. Deutsch, C.V. and Journel, A.G., 1992. GSLIB, Geostatistical Software Library and User’s Guide. Oxford University Press, New York. Gelhar, L.W., 1993. Stochastic Subsurface Hydrology. Prentice–Hall, Englewood Cliffs, NY.
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Hassan, A.E., Andric evic´, R. and Cvetkovic, V., 2001. Computational issues in the determination of solute discharge moments and implications for comparison to analytical solutions. Adv. Water Resour., 24: 607–619. Hassan, A.E., Cushman, J.H. and Delleur, J.W., 1997. Monte–Carlo studies of flow and transport in fractal conductivity fields: comparison with stochastic perturbation theory. Water Resour. Res., 33(11): 2519–2534. Kitanidis, P.K., 1988. Prediction by the method of moments of transport in a heterogeneous formations. J. Hydrol., 102: 453–473. Kitanidis, P.K., 1997. Introduction to Geostatistics. Cambridge University Press, Cambridge. Purvance, D.T. and Andricevic, R., 2000a. On the electrical–hydraulic conductivity correlation in aquifers. Water Resour. Res., 36(10): 2905–2913. Purvance, D.T. and Andricevic, R., 2000b. Geoelectric characterization of the hydraulic conductivity field and its spatial structure at variable scales. Water Resour. Res., 36(10): 2915–2924. Rubin, Y., 1990. Stochastic analysis of macrodispersion in heterogeneous porous media. Water Resour. Res., 26(1): 133–141.