Article pubs.acs.org/IECR
Minimize Flaring through Integration with Fuel Gas Networks Anoop Jagannath,† M. M. Faruque Hasan,† Fahad M. Al-Fadhli,‡ I. A. Karimi,*,† and David T. Allen‡ †
Department of Chemical & Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576 Department of Chemical Engineering, The University of Texas at Austin, 1 University Station C0400, Austin, Texas 78712-0231, United States
‡
S Supporting Information *
ABSTRACT: A fuel gas network (FGN) in a petrochemical complex can save energy costs substantially and reduce flaring by utilizing purge/waste fuel streams (Hasan et al. Ind. Eng. Chem. Res. 2011, 50, 7414−7427). A properly designed FGN can involve complex and nonintuitive mixing scenarios and equipment arrangements. Furthermore, the purge/waste gases and their characteristics can vary significantly with changing operation modes in a plant, which makes routing them into an FGN a challenge. This article reports a multiperiod two-stage stochastic programming model to design and operate an FGN that caters to all operating modes, and shows the usefulness of optimized FGN on a refinery case study. Results show that the proposed model produces a resilient FGN and reduces capital costs versus the single-mode model of (Hasan et al. Ind. Eng. Chem. Res. 2011, 50, 7414−7427). In addition, several strategies to minimize flaring and environmental penalties in a refinery operation are examined.
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INTRODUCTION Flares are indispensable units in the petrochemical industry. They are crucial for disposing of waste and purge gases in a safe manner by burning them at high temperatures, producing carbon dioxide (CO2) and steam.1 However, flare emissions can have air-quality impacts, even when very high percentages of the flared gases are destroyed.2−6 Petrochemical facilities face the complex challenge of minimizing air-quality impacts while maintaining essential flare operations. This challenge is made more complex by the wide ranges of waste gas flows to flares and the rapid fluctuations therein. Flow rates to flares vary significantly because of changing industrial operation modes (e.g., start-up, shutdown, maintenance activities, emergency releases). Flare flow variability can be segregated broadly into two different categories: emission events and variable continuous emissions. Emission events are infrequent discrete episodes (such as a plant emergency) in which a very large flow is flared.2 In contrast, variable continuous emissions can occur frequently and be categorized into multiple modes of operation, depending on the scale of the variability.4,6−8 Figure 1 shows the mass flow rate of a typical industrial flare in the HoustonGalveston (HG) area in Texas.9 Whereas most petrochemical plants have multiple sources of waste gases, they also have several potential sinks that can consume these gases as fuel. For example, processing units (PUs), venting storage tanks, fluid catalytic cracker units (FCCUs), catalytic reformer units (CRUs), and crude distillation units (CDUs) are sources of waste gases, whereas boilers, turbines, hydrotreating units, and dehydrogenation units are potential sinks in petroleum refineries. An attractive option is to utilize such impure, waste, surplus, byproduct, purge, or side streams with varying heating values as fuels, instead of sending them to flare. A systematic network of pipelines, valves, compressors, turbines, heaters, coolers, and controllers can be designed to collect various fuels, fuel gases, © 2012 American Chemical Society
Figure 1. Flow to a typical industrial flare in the HG area.
and waste gases from all sources (internal or external), mix them in optimal proportions, and supply them to the various sinks (flares, boilers, turbines, fired heaters, furnaces, etc.). Hasan et al.10 called such a network a fuel gas network (FGN). In most plants, waste gases are normally insufficient in quality and quantity to meet the fuel and energy needs of the entire plant. Thus, a plant might use them to supplement its needs and thereby reduce its consumption of other costly fuels. For instance, a refinery uses products such as vaporized liquefied petroleum gas (LPG) and fuel oil for its base fuel and energy needs. These are known as fuel from product (FFP).10 Special Issue: Industrial Flares Received: Revised: Accepted: Published: 12630
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PROBLEM STATEMENT The detailed description of FGNS (FGN synthesis) problem by Hasan et al.10 applies to single-mode plant operation. In this work, we not only generalize the model for multimodal operation but also revise and simplify some of its aspects. The problem includes the following givens: (1) I gaseous source streams (i = 1, 2, ..., I) containing S species (s = 1, 2, ..., S) with known dynamic profiles of pressures, temperatures, flows, and compositions, where the species can involve hydrocarbon gases such as methane, ethane, and propane; volatile organic compounds (VOCs) such as aromatics, methanol, and acetone; noncombustibles such as water, nitrogen, and CO2; and contaminants such as sulfur, NOx, SOx, H2S, V, and Pb; (2) K sinks (k = 1, 2, ..., K) with known demand profiles of energy demands [lower heating value (LHV)] over time, which require gaseous fuels; (3) time profiles of the allowable ranges for the flows, temperatures, pressures, compositions, and other specifications [e.g., LHV, Wobbe index (WI)] of fuel fed to each sink; (4) operating parameters, capital expenditures (CAPEX), and operating expenditures (OPEX) for valves, compressors, and utility heaters/coolers; and (5) economic (cost, price, value, etc.) data for utilizing, heating, cooling, treating, flaring, and disposing gaseous fuel streams. The goal of the problem is to determine (1) a network (FGN) of transfer lines, mixers, headers, splitters, valves, compressors, heaters, coolers, flares, and other components to obtain acceptable feeds for the K sinks by integrating the I source streams over time; (2) the sizes and dynamic duty profiles of all major equipment (valves, heaters, coolers, and compressors); and (3) the flows, temperatures, pressures, compositions, and fuel specifications of all streams over time while aiming to minimize the total annualized cost (TAC) of FGN. We include three components in the TAC. The first is the annualized CAPEX of the entire network and its equipment. The second is the OPEX related to the various fuels, products, byproducts, utilities, treatments, disposals, heating, cooling, compressing, and flaring. The third is the environmental cost of flaring in terms of emissions fees for the total amount of hydrocarbons flared. The problem is solved based on the following assumptions (1) Plant operation comprises P steady-state scenarios or operation modes (p = 1, 2, ..., P) with πp denoting the fraction of time during which mode p occurs annually. πp can also be interpreted as the probability of occurrence of mode p. (2) Sources (sinks) with identical properties or attributes in a mode are lumped into a single source (sink). (3) The LHVs of fuel components do not change with temperature. (4) All expansions are Joule−Thompson expansions. In other words, the FGN uses only valves, but no turbines. (5) All streams are below their inversion temperatures for Joule−Thompson expansions. No stream is sufficiently pure in hydrogen to have a negative Joule−Thompson coefficient. (6) All compressions are single-stage and adiabatic.
Similarly, a liquefied natural gas (LNG) plant uses its natural gas feed as a fuel source. This is called fuel from feed (FFF).10 By using various fuel and waste gases in an optimal manner, an FGN can reduce the usage of costly fuels such as FFF, FFP, or external fuels. In addition, by recycling waste gases, an FGN can substantially reduce flaring and consequent environmental impacts. Wicaksono et al.11 proposed a mixed-integer nonlinear programming (MINLP) model for integrating various fuel sources in an LNG plant. Wicaksono et al.12 extended this approach to integrate jetty boil-off gas as an additional source. Hasan and Karimi13 addressed the optimal synthesis of FGNs and presented two superstructures, one with one-stage mixing and the other with two-stage mixing. Finally, Hasan et al.10 addressed the optimal synthesis and operation of a steady-state FGN with many practical features such as auxiliary equipment (valves, pipelines, compressors, heaters/coolers, etc.), nonisobaric and nonisothermal operation, nonisothermal mixing, nonlinear fuel quality specifications, fuel/utility costs, disposal/treatment costs, and emission standards. They proposed an FGN superstructure that embeds plausible alternatives for heating/cooling, moving, mixing, and splitting and developed a nonlinear programming (NLP) model. However, one major challenge that still remains and demands attention is that most plant operations are highly dynamic, with source/flare flows that are highly variable in time. A typical industrial flare shows variability in flow with time, as seen in Figure 1. Flow can vary over multiple orders of magnitude. It can also vary substantially over time scales of an hour or less. Because a real plant can transition through several such steady operation modes over a given time horizon, its FGN must be designed to operate in the face of changes over time in fuel gas sources and sinks and their characteristics such as flows, compositions, and contaminants. Often, a source or sink might not even exist at certain times. For instance, jetty boil-off gas is available only when an LNG ship loads at the supply terminal. Clearly, the design and operation of an FGN will change with variations in sources, sinks, temperatures, pressures, flows, compositions, sink demands, and quality specifications. Although Hasan et al.10 incorporated many realistic features such as nonisobaric operation, nonisothermal mixing, and nonlinear quality specifications, their FGN model is valid only for one steady operating mode or a single set of operating conditions. Such an FGN might be suboptimal or even infeasible for a plant with multiple operating modes. Therefore, the FGN model of Hasan et al.10 must be adapted to handle such variability. Instead of synthesizing an FGN for a single static mode, one must consider the various industrial operating modes and resulting dynamic profiles of waste gases. This requires the design and operation of the FGN to be robust and flexible in face of such variability. The objective of this article was thus to generalize and substantially revise the model of Hasan et al.10 to address plant operation comprising several steady operating modes and then demonstrate a resulting reduction in flaring using a refinery case study. We begin by defining FGN synthesis for a plant with multiple steady operating modes. Then, we develop a new NLP model for this multimodal case using the basic ideas from Hasan et al.10 Next, we consider an example of a refinery complex. We demonstrate the impact of considering dynamic versus steady-state operation and study various operating cases to show the significant impact on flaring. 12631
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modes (p = 1, 2, ..., P), we need a hyperstructure of P superstructures. However, designing and using a different FGN for each operating mode is clearly unacceptable. Therefore, the physical details of the FGN must be the same across all operating modes, but the operational details will change from one operating mode to another. Because we consider operating modes with varying probabilities, we need a two-stage stochastic programming formulation, in which physical design decisions related to the existence and sizes of various equipment (transfer lines, heaters, valves, compressors, etc.) are first-stage (or mode-independent) variables and operating decisions related to flows, temperatures, and duties are second stage (or mode-dependent) variables. We begin with the source streams (i = 1, 2, ..., I) and define the following parameters and variables to describe their operation during mode p (p = 1, 2, ..., P): Pip0 is the pressure of source i (known), Tip0 is the temperature of source i (known), Fip (FipL ≤ Fip ≤ FipU) is the usage (MMscf/h) of source stream i, and HCip is the hydrocarbon content of source i (known). For a waste/purge stream that must be used or disposed in the plant, we set FipL = Fip = FipU as the known usable flow of source i. For FFF, FFP, and external fuel gas, we treat Fip is an optimization variable with appropriate bounds. Now, consider the distribution of sources to various sinks. Call SSik the transfer line feeding the header of sink k from source stream i. To describe the operation of SSik during mode p, we define the following parameters: f ikp is the gas flow (MMscf/h) in SSik, Tikp (TipL ≤ Tikp ≤ TipU) is the gas temperature at the outlet with allowable bounds, hikp = B is the product of f ikpcip and temperature change f ikpcipTikp, Δhikp H is the product of f ikpcip and during compression in SSik, Δhikp C is the product temperature change during heating in SSik, Δhikp V of f ikpcip and temperature change during cooling in SSik, Δhikp is the product of f ikpcip and temperature change during valve L U ≤ Pkp ≤ Pkp ) is the pressure of expansion SSik, and Pkp (Pkp sink k. Mass balance around source i demands
(7) Unlimited utilities are available at any desired temperature. (8) The heaters, coolers, headers, and transfer lines have zero pressure drops. (9) All gas flows are in MMscf/h defined at 14.7 psia and 68 °F. Hasan et al.10 classified and described various types of sources and sinks. A source is essentially any gas stream (internal or external) with some heating value that is available for mass integration by recycling. The waste/purge gases from CDUs, PUs, or CRUs in a refinery, feed/product/byproduct gases such as feed natural gas in an LNG plant and LPG in a refinery, and purchased fuel gases such as natural gas are some examples of source streams. The source gases might require some treatment or processing (e.g., heating, cooling, expansion, compression, and purification), before they can be reused in sinks. Thus, an FGN might need auxiliary equipment such as heaters, coolers, compressors, valves, separators, and pipelines to achieve acceptable feeds to sinks. Whereas Hasan et al.10 treated waste/purge gases, FFF, FFP, and external fuels as different types of source streams, we make no such distinction and treat all of them in a uniform manner. We achieve this by controlling the flow of source streams that enter the FGN. For instance, we force all of the available flows of waste/purge gases to enter the FGN, but keep the flows of other source streams as variables below some upper bounds. A sink is any plant unit that needs or consumes fuel gas. Some examples of sinks are turbines, boilers, incinerators, furnaces, fired heaters, and flares. Some sinks such as boilers, turbines, and furnaces produce some heat and power, whereas others such as incinerators and flares do not. All sinks produce emissions, and these emissions might be regulated. In contrast to Hasan et al.,10 who classified sinks as fixed and flexible, we treat all of them uniformly as flexible sinks. According to Hasan et al.,10 a sink is fixed (flexible), if it has a fixed (variable) energy need and cannot (can) generate heat/power that can be sold for additional revenue. Furthermore, whereas Hasan et al.10 considered the flare as a separate entity, we consider it as just another sink with appropriate specifications and zero energy demand.
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MODEL FORMULATION Figure 2 shows the superstructure proposed by Hasan et al.10 for a single steady operating mode. To address P operating
K
Fip =
∑ fikp k=1
(1)
The gas in SSik can undergo valve expansion, compression, heating, and/or cooling. For compression and valve expansion, we use ⎡⎛ P ⎞nip ⎤ kp C B ⎟ − 1⎥ ηipΔhikp ≥ (cipTip0fikp − Δhikp)⎢⎜⎜ ⎢ Pip0 ⎟ ⎥ ⎠ ⎣⎝ ⎦
(2)
V ≥ μ c f (P − P ) Δhikp kp ip ip ikp ip0
(3)
where cip is the known constant-pressure heat capacity (Cp) of source stream i, μip is its Joule−Thompson expansion coefficient, nip = R/cip is its adiabatic compression coefficient, and ηip is the adiabatic compression efficiency of the compressor on SSik. Because the use of a valve or compressor will incur costs, eqs 2 and 3 ensure that the FGN uses a valve (compressor), only when Pip0 > Pkp (Pip0 < Pkp). As the four
Figure 2. Schematic superstructure for an FGN. 12632
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operate adequately. We now consider some specifications individually. The specific gravity (SG) of a gas is the ratio of its density to that of the air at the same temperature and pressure. For an ideal gas, this is simply the ratio of the molecular weights of the gas and the air. If SGip denotes the known specific gravity of source stream i during mode p, then the bilinear constraint
possible operations will change the temperature of the gas in SSik, the temperature at the outlet of SSik can be computed as B + Δh H − Δh C − Δh V hikp = cipTip0fikp + Δhikp ikp ikp ikp (4)
However, the gas temperature must be maintained within the range [TipL , TipU] throughout SSik. The lowest temperature in SSik will occur when a cooler is used with a valve, because valves and coolers decrease temperature. The resulting temperature must exceed TipL V + Δh C ≤ c (T − T L)f Δhikp ip ip0 ip ikp ikp
I
SGkpGkp =
i=1
I
LHVkpGkp ≤
∑ i=1
WI =
∑ fikp LHVip i=1
L )2 SG ≤ (LHV )2 ≤ (WIU )2 SG (WIkp kp kp kp kp
(8)
∑ hikp i=1
(9)
I
Ckp =
∑ cipfikp i=1
(13ab)
A plant might have a regulatory limit on the amount of hydrocarbons that it can burn in its flares or incinerators. It can incur a penalty if this limit is exceeded. To accommodate this environmental aspect in our model, we let HCip denote the mass of hydrocarbon in source stream i. Then, we let hcpU denote the total mass of hydrocarbons that the plant can burn without incurring a hydrocarbon penalty during mode p and hcp denote the amount of hydrocarbons burned by the plant in excess of the allowable limit (hcpU). Thus, the hydrocarbon emissions from a flare or incinerator in each period should satisfy
(7)
I
TkpCkp =
LHV SG
Note that this definition of WI does not have a correction factor for temperature as suggested by Elliot et al.14 and used by Hasan et al.10 We decided to use the above definition, because it seems to be the most widely used definition in the literature.15,16 Most sinks other than flares and incinerators require adequate WI values. WI is a key factor in analyzing the heating value of a gas. The higher the WI, the greater the heating value of the gas flowing through a hole of given size in a given amount of time. For any given orifice, all gas mixtures with an identical WI value will deliver the same amount of L U heat.17 If [WIkp ,WIkp ] denotes the acceptable limits on WI of the feed to sink k during mode p, then we can write the bilinear constraint
I
Ekp =
(12)
WI is another critical specification for fuel gas quality with the same units as LHV, defined as
I
fikp
∑ fikp LHVip i=1
(6)
V C Note that f ikp = 0 forces Δhikp = Δhikp = 0 by eq 5, and then B H eq 6 forces Δhikp = Δhikp = 0. After the operation of SSik, we now use the following to L describe the operation of sink k and its header: Tkp (Tkp ≤ Tkp ≤ U L U Tkp ) is the temperature of sink k, Gkp (Gkp ≤ Gkp ≤ Gkp ) is the L U gas flow into sink k, Ekp (Ekp ≤ Ekp ≤ Ekp ) is the energy flow in L terms of LHV (lower heating value) into sink k, SGkp (SGkp ≤ U SGkp ≤ SGkp ) is the specific gravity of the feed to sink k, LHVkp L U (LHVkp ≤ LHVkp ≤ LHVkp ) is the LHV of the feed to sink k, I and Ckp = ∑i=1 cip f ikp. If a sink (e.g., fired heater with a given heating duty) is dedicated to a specific use and cannot consume L U more energy than its demand, then we set Ekp = Ekp = Ekp to be its known energy demand. If a sink (e.g., boiler or gas turbine) can consume beyond its demand to produce extra utility such as steam or power, then we treat Ekp as an optimization variable with appropriate bounds. If a sink is a flare, incinerator, or disposal, then L U we set Ekp = 0, and Ekp = ∞. Then, using the above definitions, we write the following relations for each mode p
Gkp =
(11)
applies. As mentioned earlier, a minimum LHV is usually required for satisfactory flaring and fuel combustion in a sink. We can compute the LHV of the feed to sink k during mode p as
(5)
As discussed earlier, the compressor inlet must be at the lowest temperature to minimize the compression work. Therefore, the highest temperature will be at the outlet of SSik and must not exceed TipU Uf hikp ≤ cipTip ikp
∑ fikp SGip
⎛ I K ⎞ hcp ≥ ⎜⎜ ∑ ∑ HCipfikp ⎟⎟ − hc U p ⎝i = 1 k = 1 ⎠
(10)
where LHVip is the known LHV (heat per MMscf) of source stream i. Hasan et al.10 identified several specifications such as LHV, Wobbe index (WI), and methane number (MN) for fuel gas quality that might be essential for a sink to operate satisfactorily. For instance, gases entering even a flare or incinerator must have sufficient LHV. Plants might even add some natural gas to boost the LHV of a flare gas, so that the flare would
(14a)
Later, we will impose an emission fee on hcp in the FGN cost. Note that the sum in eq 14a includes all sinks that are flares or incinerators. Similarly, a plant might have regulatory limits on emissions such as NOx and SOx from all sinks. These limits and the corresponding emissions fees can be handled in the same manner as the hydrocarbon penalty discussed above. To this 12633
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maximum duty of the compressor on SSik, ΔHikH is the maximum duty of the heater on SSik, ΔHikC is the maximum duty of V the cooler on SSik, and ΔHikV is the maximum Δhikp value for SSik. Physically, these variables represent the sizes or capacities of the auxiliary equipment in the FGN. For instance, Fik measures the capacity or the maximum flow that SSik must allow. We compute the OPEX and CAPEX of various units as linear functions of these sizes or capacities. The following relations link the design variables with the operational variables
end, we define ejikp as the amount of pollutant j that sink k would emit if it used 1 MMscf of gas from source i during mode p. Furthermore, we let ejpU be the regulatory limit on this emission during mode p. Then, the constraint on the amount of emissions of pollutant j for any environmental penalty is given by I
ejp ≥
K
∑ ∑ ejikpfikp
U − e jp (14b)
i=1 k=1 10
The methane number (MN) measures the knock resistance of a gaseous fuel entering a gas turbine. If xisp is the mole fraction of a hydrocarbon component s in source stream i during mode p, then Hasan et al.10 proposed the following expression to ensure an adequate MN for a sink k that is a gas turbine 0.242 ∑ fikp xi ,CH4, p i
≥
∑ (1.516fikp xi ,C2H6, p + 3.274fikp xi ,C3H8, p i
⎛ + 5.032fikp xi ,C4H10, p) + ∑ ⎜⎜6.79fikp xi ,C5H12, p i ⎝ ⎞ + 8.548 ∑ fikp xi ,C5 +, p⎟⎟ ⎠ i
(19)
B ΔHikB ≥ Δhikp
(20)
H ΔHikH ≥ Δhikp
(21)
C ≥ Δh C ΔHik ikp
(22)
V ΔHikV ≥ Δhikp
(23)
Finally, the expected total annualized cost (TAC) of an FGN with P modes is given by the sum of its CAPEX values and the weighted sum of its OPEX values under various modes. If OST denotes the on-stream time of the plant per year and AF denotes the annualization factor, then the expected TAC is expressed as
(15)
10
I K ⎛ I K T BΔ Fik + ∑ ∑ CAPEX ik TAC = AF⎜⎜ ∑ ∑ CAPEX ik ⎝i = 1 k = 1 i=1 k=1
Hasan et al. used a treatment factor or removal ratio for each component s in the above equation, which we assumed to be unity in this work. Hasan et al.10 also proposed the following constraints for preventing condensation in the FGN and ensuring sufficient superheating ⎧ ⎫ ⎤⎪ ⎛ Pkp ⎞ ⎪ 5⎡ ⎢5.15⎜ ⎥⎬ ≤ Tkp ⎨ + − MDP 312 ⎟ kp ⎪ ⎥⎦⎪ 9 ⎢⎣ ⎝ 100 ⎠ ⎩ ⎭
Fik ≥ fikp
I
I
+
(16)
∑
−
∑
K L) + βkp(Ekp − Ekp
k=1 J
+
I
K
T ∑ ξjpejp + ∑ ∑ OPEX ikp fikp j=1 I K
+
∑ δkpGkp + γphcp k=1
i=1 k=1 I
i=1 k=1 I
H × hikp +
(18)
K
B Δh B + ∑ ∑ OPEX H Δ ∑ ∑ OPEX ikp ikp ikp i=1 k=1
UG qispfikp ≤ qksp kp
K
i=1 k=1
K
where MDPkp is the moisture dew-point temperature and HDPkp is the hydrocarbon dew-point temperature for the sink k in period p. Apart from the above fuel specifications, most sinks can impose limits on the levels of some gas components in their feeds. Let qisp denote the amount of component s in source L U stream i during mode p and [qksp , qksp ] represent the acceptable limits on this amount. Then, we need
i=1
I
∑ ∑ CAPEX CikΔHikC + ∑ ∑ CAPEX ikVΔ
⎡ I P ⎞ ⎢ ⎟ V × Hik ⎟ + OST ∑ π p⎢ ∑ αipFip ⎠ p = 1 ⎢⎣ i = 1
(17)
I
i=1 k=1 K
i=1 k=1
⎧ ⎡ ⎤⎫ ⎛ Pkp ⎞2 ⎛ Pkp ⎞ ⎪ ⎪ 5⎢ ⎨HDPkp + 2.33⎜ ⎟ − 2.8⎜ ⎟ − 305⎥⎬ ≤ Tkp ⎢ ⎥ 9⎣ ⎪ ⎝ 100 ⎠ ⎝ 100 ⎠ ⎦⎪ ⎩ ⎭
L G ≤ qksp kp
K
∑ ∑ CAPEX ikHΔHikH
× HikB +
K
C ∑ ∑ OPEX CikpΔhikp i=1 k=1
One can suitably modify this equation to accommodate groups of components rather than individual ones. Similarly, one could use appropriate weights for various constituents. Having modeled the operational aspects of the FGN for a given mode, we now define the following mode-independent or design variables and relate them to the various mode-dependent variables: Fik is the flow capacity (MMscf/h) of SSik, ΔHikB is the
I
×
K
∑∑ i=1 k=1
⎤
V Δh V ⎥ OPEX ikp ikp⎥
⎥⎦
(24)
where the first five terms represent the annualized CAPEX values for various equipment in the SSik: CAPEXikT for the 12634
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Figure 3. Fuel sources and sinks for the refinery case study.
Table 1. Data and Parameters for the Sources in the Refinery Case Study specification/parameter flow (MMscf/h) mode 1 mode 2 mode 3 mode 4 mode 5 temperature (K) pressure (psia) Cp (kJ/MMscfK) μ (K/psia) η n LHV (MMBtu/MMscf) SG methane (mol %) ethane (mol %) propane (mol %) C3+ (mol %) hydrogen (mol %) carbon monoxide (mol %) nitrogen (%) sulfur (ppm) H2S (ppm) VOC (ppm) hydrocarbon content (kg/MMscf) price ($/MMscf)
S1
S2
S3
S4
S5
S6
S7
0.04 0.08 0.02 0.04 0.06 373 50 42791 0.03 0.75 0.2 880 0.769 88 2 0.5 1 0.5 1 7 55 0.05 4 21400 0
0.4 0.5 0.45 0.27 0.25 400 35 43210 0.028 0.75 0.2 915 0.74 90 3 2 0 0 0 5 70 201 6 21523 0
0.18 0.12 0.15 0.1 0.24 350 25 42270 0.03 0.75 0.17 850 0.769 88 2 0 0 4 3 3 55 0.05 5 20622 0
