MODEL-BASED FRAMEWORK FOR OPTIMAL DESIGN OF A BITUMEN UPGRADING PLANT BASED ON OPERATING PROFIT MAXIMIZATION Jennifer Charry-Sánchez, Alberto Betancourt-Torcat, Ali Almansoori* Department of Chemical Engineering, The Petroleum Institute, Abu Dhabi, United Arab Emirates Contact:
[email protected]
CASE STUDY
INTRODUCTION & MOTIVATION •
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The Canadian Oil Sands holds the 3rd largest bitumen deposits (API˚ < 10). The hostile environment surrounding the remote bitumen producing regions demands major capital investments The bitumen conversion process requires energy-intensive extraction and upgrading processes, both associated with high costs and severe environmental damages Oil Sands developments prompted rapid economic growth during the oil bonanza, but also triggered an escalating inflation rate which quadrupled from 2003 to 2014. The current low oil price environment challenges producer’s economics
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The upgrading process includes six major unit operations: 1) Atmospheric Distillation (AD), 2) Diluent Recovery (DR), 3) Vacuum Distillation (VD), 4) Cracking (C), 5) Hydrotreating (HT), and 6) Blending (B)
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A MINLP optimization framework is proposed to determine the most suitable bitumen upgrading configuration to maximize the plant’s operating profit under different average inflation rate scenarios with respect to the base year 2003
The proposed MINLP optimization framework was used to assess SCO plant’s gate supply cost for upgraders built during 8 different time periods considering average inflation rate until the end of each period. The year 2003 was the selected baseline Lead time (y) (2000s)
01-03 03-05 05-07 07-09 09-11 11-13 12-14 14-16
Average Inflation Rate (%)
Upstream Cost (CA$/bbl.)
0 50 100 150 200 250 300 280
21.90 24.31 26.72 29.08 31.53 33.94 36.35 35.39
Bitumen Upgrader’s Unit Cost Components (CA$/bbl.) Fuel– Operating Cost 9.96 9.92 9.88 9.80 9.72 9.64 9.56 9.46
O&M Cost (non-fuel) 5.96 5.93 5.90 5.83 5.77 5.71 5.66 5.60
Capital Cost
CO2 Tax
Total
5.13 7.69 10.25 12.76 15.38 17.94 20.50 19.48
1.39 1.39 1.38 1.37 1.36 1.35 1.33 1.32
22.44 24.93 27.41 29.76 32.23 34.64 37.05 35.86
SCO Plant’s Gate Supply Cost (CA$/bbl.)
44.33 49.24 54.13 58.84 63.76 68.58 73.40 71.25
Findings • Upstream/upgrading costs steadily grew between 2003 - 2014, then decreased for the last period (coinciding with the latest oil price plunge) • Fuel operating cost decreases as upgraders built on later periods are more efficient • O&M costs are higher for older upgraders due to equipment aging • Upgrader’s upfront investment involves large capital amortization and depreciation costs which are split along the entire operating lifetime of the plant
Figure 1. Bitumen Upgrading Plant Schemes Figure 2. SCO gate supply cost and average inflation rate as a function of lead time periods
MATHEMATICAL MODEL
The capital cost inflation rate has a major impact on SCO gate supply cost. Bitumen upgraders built after the average inflation rate hit 100%, with respect to the year 2003, might be having troubles to break-even due to the current low oil price environment (45-50 US$/bbl)
The model objective function and conceptual formulation are given as follows:
m ax η
Pr ofit y = Re venues y − TotalCost y
(1)
Where η represents the set of decision variables such as: processing steps, type of processing unit, and utility supply source Material balance
Fm ,i , j = zi , j TFi Yi ,m
(2)
where Fm,i,j denotes the flowrate of component m leaving the ith processing unit to enter the jth unit, zi,j is a binary variable determining the unit’s products flow path, TFi is the total material feed to the ith unit, and Yi,m represents the yield of component m in the ith unit. Binary Decision Variable (3)
CONCLUSIONS Thermocracking-based upgraders are the most cost-efficient option to convert raw bitumen into SCO. Nonetheless, hydrocracking-based upgraders could potentially be more environmentally friendly
The economics for bitumen upgraders within Alberta under a low oil price environment remains unfavorable, and there are presently no upgrading project plans with an announced start date
Constrains the selection of the processing steps’ (sth) forming the upgrading configuration Capital Cost (4) Where Xi is an integer variable. The parameters λi and βi define the unit’s i capital expenses while ICi is the installed capacity associated to the ith unit. The parameters EF, LF, IFy, and AF represent US$/CA$ exchange rate, the location cost factor, inflation rate factor for the yth period, and annual capital depreciation and amortization factor, respectively Total SCO Plant’s Gate Supply Cost
TotalCost y = UPCost y + ∑ UCk , y + ∑ CAPEX i , y + OMC y + EC y k
i
(5)
where UPCosty are the upstream costs, UCk,y are the utility costs, OMCy are the operating and maintenance costs, and Ecy represents the total environment compliance cost Revenues Collected from selling SCO product at local/international oil prices, here represented by the parameter OP – Crude oil local/international market price (6)
Capital cost upsurges during the last decade have ultimately eroded producers’ returns and played a significant role in undermining the economic competitiveness of the Oil Sands upgrading sector
REFERENCES • A. Betancourt-Torcat, G. Gutierrez, A. Elkamel, L. Ricardez-Sandoval, 2011, Integrated Energy Optimization Model for Oil Sands Operations, Industrial & Engineering Chemistry Research, 50, 12641–12663. • J. Charry-Sanchez, A. Betancourt-Torcat, A. Almansoori, 2016, Environmental and Economics Trade-Offs for the Optimal Design of a Bitumen Upgrading Plant, Industrial & Engineering Chemistry Research, 55, 11996– 12013. • P.J. Findlay, 2016, The Future of the Canadian Oil Sands: Growth Potential of a Unique Resource Amidst Regulation, Egress, Cost, and Price Uncertainty, Oxford Institute for Energy Studies. • M.R. Gray, 2015, Upgrading of Oil Sands Bitumen and Heavy Oil, The University of Alberta Press, Edmonton, Canada. • D. Millington, C. Murillo, R. McWhinney, 2014, Canadian Oil Sands Supply Costs and Development Projects (2014-2048), Canadian Energy Research Institute. • M. Nawaz, E. Zondervan, J. Woodley, R. Gani, 2011, Design of an Optimal Biorefinery, Computer Aided Chemical Engineering, 29, 371–376.
