LITMIR.BIZ

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>87 7.50 2 81–87 28.33 3 75–81 28.33 4 69–75 28.33 5 <69 7.50

Next, one case from each feed type was selected from among the data sets. The case chosen for each feed type was a case found near the middle of the distribution. A total of five cases were selected (see Table 2.2). From among these cases, the base case was selected as type 3 case since it falls nearest to the middle of the distribution and is representative of the most commonly found composition in this region.

Table 2.2 Selected cases.

Composition (mol%)	Feed #1	Feed #2	Feed #3	Feed #4	Feed #5
Methane	94.11	83.62	77.78	71.94	56.34
Ethane	2.59	7.54	9.42	11.55	16.13
Propane	0.02	4.68	7.26	9.60	16.06
n‐Butane	0.25	2.11	2.65	3.15	4.96
i‐Butane	0.26	1.08	1.27	1.61	2.62
n‐Pentane	0.02	0.30	0.60	0.82	1.60
i‐Pentane	0.03	0.30	0.53	0.76	1.44
Neopentane	0.00	0.00	0.00	0.02	0.04
Carbon dioxide	2.71	0.38	0.49	0.56	0.81

Finally one additional case is considered. As mentioned in the introduction, sulfur species, predominately hydrogen sulfide, may also be found in shale gas [6]. The goal of considering this case is to determine if the additional processing needed for a gas with a high acid (HA) loading would significantly affect the economics of treating such a stream. This inlet composition of this case was chosen such that the methane and NGL content would be similar to that of the base case (Feed #3) (Table 2.3).

Table 2.3 High acid (HA) gas feed composition.

Composition (mol%)	HA feed
Methane	74.35
Ethane	9.00
Propane	6.94
n‐Butane	2.54
i‐Butane	1.22
n‐Pentane	0.57
i‐Pentane	0.51
Neopentane	0.00
Carbon dioxide	4.78
Hydrogen sulfide	0.10

      2.4.2 Process Simulations and Economic Evaluation

Process simulation was performed to meet the required specifications (sales gas gross heating value of 950–1150 Btu/SCF [British thermal unit/standard cubic feet], 1000 psig, and CO₂ content of 2–3 mol% maximum, 0.3 g H₂S/100 SCF gas) [22,23], and process designs were developed for each feed. ProMax simulation software [24] was used to simulate this process.

      The simulation results were then used to size process equipment, develop mass and energy balances, and determine operating conditions and utility consumption of process equipment. Aspen process economic analyzer [25] was used to estimate the equipment purchase costs. The Hand factor was utilized to account for installation and other costs. The fixed capital investment (FCI) for each processing unit was then estimated [26].

      (2.1)

      where FCI_i, fixed capital investment for a given processing unit; , Hand factor for equipment q; and , the purchased cost of equipment q.

Table 2.4 shows the values and assumptions used to estimate the variable costs (raw material costs were considered separately):

Table 2.4 Parameters used for the techno‐economic analysis.

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Parameter	Values	Units	References
Variable cost parameters
TEG price	0.93	$/lb	[27]