(625h) Project Portfolio Optimization of Carbon Capture and Utilization Technologies for Oil and Gas Industries

Increasing global energy demand and Greenhouse Gas (GHG) emissions are the two major and conflicting challenges of rapid industrialization and urbanization. The global energy demand of the industrial and transportation sectors has doubled over the last three decades [1], which increased the demand for energy products. This resulted in high GHGs emissions due to the heat and electricity production involved in the production of energy products [2]. CO₂ related to energy demands and other causes forms over 76% of the GHGs [3] and it remains in the atmosphere for an extended period, thereby contributing to global warming and climate changes [4]. Thus, the need for reducing carbon emissions while meeting the global energy demands is of utmost importance.

In this study, Carbon Capture and Utilization (CCU) has been studied as an emission mitigation option for GHG emissions abatement. But CCU is a cost-intensive process and depends on various factors like location of sources, carbon emission intensities, capture technology used, and the decision to utilize or sequester carbon [5]. Thus, portfolio optimization models have been used for the selection of emission sources, capture material and technology, and design of supply chain for CO₂ capture, utilization, and sequestration in the previous studies [6-10].

This work develops a comprehensive portfolio optimization model specific to the oil and gas industries. In the previous studies [6-10], the emission intensities were assumed to remain constant over the planning horizon, and the capture and the utilization costs were estimated under steady-state conditions (i.e., the decision variables were optimized without deriving a migration pathway over time) [10]. This assumption can result in a large penalty due to over- or under-estimation of capture facility capacities as the production rates are influenced by production life and societal factors such as demand for the products. For example, for an emission source with a production increase in the later stages of the planning horizon, the addition of an expansion to the capture facility might cost less than building a large capture facility a priori. Such decisions can also impact the selection of capture technologies. Besides the emission sources, the carbon reduction target and the demand for utilization products might also vary with time. Hence, a portfolio planning that accounts for the changes in the emission sources, carbon reduction targets, and utilization product demands over the planning period is necessary for the oil and gas industries. Furthermore, only the capture technologies using post-combustion techniques have been studied in the previous works [6-10] as they are suitable for capture from most industrial emissions. This may not be appropriate for the oil and gas industries as some of its major emission sources like acid gas removal units may require pre-combustion techniques. Thus, more capture techniques should be considered for the capture and utilization of the emission sources of oil and gas industries.

Here, we develop an optimization-based framework that integrates the major emission sources in the oil and gas industries, potential capture technologies, and carbon utilization technologies under a single network and allows capacity expansions for the CCU facilities to account for the variations in the sources and capture and utilization requirements over time. The framework has been described with a superstructure representation in Figure 1. For the given yearly CO₂ reduction targets and utilization product demands, the objective of the optimization model is to minimize the net cost involved with the CCU network over a planning horizon. The net cost is the difference between the cost of carbon capture and utilization and the revenue from selling the utilization products over the given period. The portfolio optimization of the CCU network involves several strategic and operational decisions as follows:

i) The amount of carbon to be captured from each source during each planning period to meet the CO₂ reduction target of that period is estimated.

ii) Feasible capture technique, and capture technology and material used are selected for each source such that the annual investment and operating costs of carbon capture are minimal.

iii) Capacity expansions are added to the capture facilities over time if necessary.

iv) The utilization technologies are selected to obtain the maximum possible revenue from selling the utilization products and by-products. This decision also depends on the product demand for the given period if present.

A multiperiod deterministic mixed-integer non-linear programming (MINLP) model has been proposed to optimize the strategic CCU deployment in the oil and gas industries. The nonlinearity arises from the cost models, and it makes the model intractable for large-size problems where multiple CCU technologies are considered for long planning horizons. Thus, the non-linear functions have been relaxed and the resulting mixed-integer programming (MIP) model has been solved using CPLEX version 12.7 to generate tight dual bounds, which are then used to guide the MINLP solvers. BARON version 17.4 and DICOPT version 2 are used as the global and local solvers in the study.

The major emission sources in the oil and gas industries like acid gas removal units, stationary combustion sources, flares, cracking units, catalytic reforming units, and hydrogen production units have been considered for the study. The sources have different carbon emission rates that vary over time. Post-combustion, pre-combustion, and oxy-combustion techniques using different capture technologies and capture materials and 15 utilization techniques to produce 8 different products have been included in the framework. The planning period was 25 years. The results aid in understanding aspects that should be considered while implementing CCU in the oil and gas industries. This presentation will introduce the model and showcase the findings through case studies.

References

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