Estimate the Capital Cost of Shale-Gas Monetization Projects

December
,
2017

As the number of shale-gas monetization options has increased, so has the need to develop quick, preliminary estimates of the cost of proposed projects and manufacturing pathways. This article presents a method for an order-of-magnitude cost estimation.

Discovery of substantial shale gas reserves in the U.S. has spurred a boom in production of chemicals and fuels. U.S. production of shale gas jumped from about 2 trillion ft3 in 2007 to about 15 trillion ft3 in 2015 (1). This increase is expected to continue over the coming decades. Some estimates predict a cumulative production of 459 trillion ft3 of shale gas from 2014 to 2040 (2).

As shale gas gains an advantage as a competitive feedstock in the U.S., a concomitant growth in the chemical industry is taking place. The American Chemistry Council reports that 264 shale-gas-dependent projects had been announced by April 2016 with estimated capital investments totaling $164 billion (3). About half of these projects have already been completed or are in construction and implementation phases.

Shale-gas monetization refers to the physical and/or chemical transformation of shale-gas constituents into value-added products. A wide variety of chemicals and fuels can be produced from shale gas (4–6). In addition to the conventional manufacturing chemistries and production routes, there are significant opportunities to create novel pathways and technologies. There is a critical need to quickly assess the economic viability of these new and emerging alternatives before detailed design and cost estimates are carried out.

This article develops and explains an order-of-magnitude correlation for estimating the capital investment required for a shale-gas monetization plant. Technology developers and process engineers can use this correlation — along with other preliminary cost-estimation techniques — to help make technology selection and design decisions prior to chartering laborious and costly techno-economic studies.

Capital cost estimation methods

The fixed capital investment (FCI) or capital expenditure (CAPEX) of a plant refers to the money required to design, procure, deliver, and install the process equipment, ancillary units, piping, instrumentation and controls, civil and electrical installations, and service facilities needed to ready the process for operation. The total capital investment (TCI) of a plant is the sum of the FCI and the working capital investment (WCI) that is necessary to cover the operating expenses up to the start of operation.

There are several approaches for estimating the TCI of a project (7):

  • manufacturer’s quotation
  • computer-aided tools
  • capacity ratios (e.g., six-tenths factor rule)
  • cost indices (e.g., Chemical Engineering Plant Cost Index [CEPCI])
  • factors based on equipment cost (e.g., Lang method, Hand’s factors)
  • empirical correlations.

The accuracy of each method varies, depending on the available information and level of project definition.

The Association for the Advancement of Cost Engineering-International (AACE-International) recommends five levels (or classes) of cost estimates. The least-detailed level is referred to as an order-of-magnitude estimate, and is given a Class Level 5. Such an estimate is based on very little information (0–2% of project definition) and is used mostly for preliminary and rapid assessment of the economic viability of a proposed project. The accuracy of a Class Level 5 estimate is typically on the order of ±30–50%. The most-detailed level (i.e., check estimate, contractor’s estimate, or Class Level 1 estimate) is based on almost full detailing of the project, and is used to issue bids and tenders. Its accuracy is on the order of ±5–10%.

A commonly used approach for an order-of-magnitude estimate is to use information from similar processes or technologies. In this context, correlations based on the type of industry, plant capacity, and number of functional units are particularly useful (8–11). We adopted this approach to develop an order-of-magnitude cost estimation method for rapidly predicting the FCI required for a proposed shale-gas monetization process. Coupling this order-of-magnitude cost estimate with other sustainability criteria and performance targets can allow preliminary assessment of the sustainability of a gas monetization project (12).

Data collection and correlation development

We collected and analyzed economic data for 50 gas conversion plants from various sources, including databases, company websites, published techno-economic analyses, and reported information. The data were not always available as FCI. Other forms of reported data included TCI and inside battery limit capital expenditure (ISBL CAPEX). We then processed the data using the following equations to create a consistent basis for cost correlation. The processed data are shown in Table 1.

Table 1. Economic data for 50 gas conversion plants were collected and processed to create a consistent basis for cost correlation. References are included in the online version of the article, available at www.aiche.org/cep.
Process Relevant Technology and/or Company* Capacity (105 MTPA) FCI (MM$, 2016) Literature Cited
Ethylene Production via Cracking of Ethane-Propane (Steam-Cracking) Intratec Solutions 15.42 2,077.03 16
Ethylene: Ethane and Ethane/Propane mix Linde AG 15.00 2383.68 17
Ethylene Glycol Production OMEGA catalytic process by Shell Global Solutions 6.80

6.00
502.14

404.91
18

19
Hydrogen Production from Natural Gas Intratec Solutions 4.08 439.32 20
Hydrogen Production from Natural Gas CB&I 0.78 139.19 21
Propane Dehydrogenation: Oxydehydrogenation The STAR process 4.08 320.03 22
Propylene Production via Propane Dehydrogenation Oleflex process by UOP 5.00

4.50
361.83

351.23
23

24
Propylene Production via Propane Dehydrogenation CATOFIN by CB&I 5.35

5.00
399.27

338.07
25

26
D,L-Methionine Production via the Carbonate Process Evonik Industries AG 1.35

5.26
306.80

589.13
27

28
Hydrogen Cyanide Production Andrussow process 0.23 62.79 29
Methanol-to-Olefins Process UOP 5.44

7.10

6.00
327.83

337.08

298.74
30

31

32
Methanol-to-Propylene Technology Lurgi GmbH, JGC Corp., and Mitsubishi Chemical 5.08

5.68
294.00

322.08
33

34
Polypropylene Production via Gas-Phase Process: Stirred-Bed Reactor Lummus Novolen Technology 2.72 197.29 35
Polypropylene Production via Gas-Phase Process Unipol 3.63

5.00
242.87

323.58
36

37
Ethylene Production via Ethanol Dehydration BP Chemicals 1.90 184.53

 

Author Bios: 

Chi Zhang

Chi Zhang is a PhD candidate at the Artie McFerrin Dept. of Chemical Engineering, Texas A&M Univ. He has a BS with double majors in chemical engineering and mathematics from Univ. of Wisconsin-­Madison. His research activities are in process design and integration.
 Read more

Mahmoud M. El-Halwagi

Mahmoud M. El-Halwagi, PhD, holds the McFerrin Professorship at the Artie McFerrin Dept. of Chemical Engineering, Texas A&M Univ., and is the Managing Director of the Texas A&M Engineering Experiment Station Gas and Fuels Research Center (Phone: (979) 845-3484; Email: el-halwagi@tamu.edu). His main research area is sustainable design of industrial systems through process integration. He has published more than 250 refereed papers and 85 book chapters. He has also authored three textbooks and coedited six books. He received a PhD from the...Read more

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