Greenhouse gas mitigation strategies for the oil industry - bottom-up system analysis on the transition of the Colombian oil production and refining sector
Yanez Angarita, Edgar
DOI:
10.33612/diss.158071720
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Publication date: 2021
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Yanez Angarita, E. (2021). Greenhouse gas mitigation strategies for the oil industry - bottom-up system analysis on the transition of the Colombian oil production and refining sector. University of Groningen. https://doi.org/10.33612/diss.158071720
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1 INTRODUCTION
___________________________________________________________________________
1.1 CO2 EMISSIONS IN THE OIL INDUSTRY AND MITIGATION
OPTIONS
Crude oil is expected to stay dominant in the world energy matrix sector for the coming decades. The IEA predicts that the share of oil in the global primary energy demand might remain steady from 31% in 2018 to 29% in 2040, with an absolute increase of 25% to 5,626 Mtoe in 2040 1 (see Figure 1). The transport sector (road, train, aviation and shipping)
represents 49% of total oil demand and will increase up to 60% in 2040 achieving 79 Mbbl/d
1. Crude oil will most likely remain dominant in the transport sector, due mainly to its vast
infrastructure, large scale of production, low cost and high-energy-density fuels. 2.
Figure 1. Oil demand forecast by region and scenario defined by the IEA 1.
A target of net "zero" CO2 emissions by 2070, is essential to limit the global average
temperature rise to 1.8°C 1. Nevertheless, global CO2 emissions have seen a continuous
increase. Global energy-related CO2 emissions grew 1.7% in 2018 and flattened in 2019
mainly from the CO2 emissions reduction in power generation and lower coal consumption 3.
According to the International Energy Agency (IEA), the greenhouses-gas emissions (GHG) associated with energy production and use accounts for about two-thirds of the global emissions 5. Globally, 4% of total anthropogenic CO
2 emissions are released by the oil
refining sector.
Colombia is committed to reducing its greenhouse gas (GHG) emissions by 20% with respect to the projected Business-as-Usual (BAU) by 2030. This target could increase to 30% with the provision of international support 6. Colombia currently accounts for approximately 0.4%
of global emissions (Institute of Hydrology, Meteorology and Environmental Studies (IDEAM) et al., 2016). Regarding its risk (vulnerability) from climate change it was ranked 49th between 1998–2017 and 19th in 20178. Colombia is a net exporter of fossil fuels. In 2015
Colombia’s energy production accounted for 5.3 EJ, with a net export of 1.6 EJ of oil and 2.1 EJ of coal and a final domestic consumption of just 1.1 EJ. 9
The most updated GHG inventory for Colombia was issued in 2012, with 258 MtCO2-eq. This
inventory was dominated by the forestry (36%) and agricultural sectors (26%), followed by transportation (11%), manufacturing industries (11%), and mining and energy (10%). The industrial, mining and energy, and transportation sectors account for 39% of the total GHG emissions (see Figure 2), and have shown increases of 94%, 85%, and 53%, respectively, for the period from 1990–2012.
Figure 2. CO2 emissions in Colombia from the industrial, energy, and transport sectors
7. Categories in the legend follow the CO
2 emissions inventory guidelines from 10.
% of Total emissions, 39% 0% 10% 20% 30% 40% 50% 10 20 30 40 50 60 70 2012 Shar e o f the t ot al C O2 emissio ns CO 2 emissio ns [Mt C O2 ]
1 - Electricity & Heat (1A1a) 1 - Oil & Gas (1A1b, 1B2a; 1B2b) 1 - Iron & Steel (1A2a; 2C1) 1 - Transport (1A3) 1 - Energy-others industries (1A4; 1A1c; 1A2c; 1A2d; 1A2e, 1A2f) 2 - Cement (2A1) 2 - Other Industrial Process (2A2; 2B; 2C) % of Total emissions
Several technological options have been explored to reduce CO2 emissions from the oil
industry, such as energy efficiency, CO2 capture and biomass integration. In addition, the
operational energy efficiency in the oil and gas sector has increased on average by 1.3% per year 11. However, as the sector is moving to produce from more mature field to
unconventional reservoirs, the energy-intensive operations have also increased by one-third on average 12. EU refineries have an average energy consumption between 6.5% to 7% of
calorific value of the processed crude oil 13. This means the energy used as fuel at the
refineries represent around 50% of the refining operational cost 14. The energy efficiency
potential of energy-intensive industries (e.g. chemical and petrochemical, iron and steel, pulp and paper) has been frequently forecasted by many institutes including the International Energy Agency (IEA). However, the reliability of these estimates is somehow limited because of the lack of publicly available data of individual plants 15.
Carbon capture and storage (CCS) is not only considered a primary strategy to curb CO2
emissions, but also as essential to meeting the temperature rise target 16. Berghout et al.17
indicated that 80-90% of GHG emissions could be reduced using CCS at a refinery but the cost involved for CCS implementation is above 100 €/t CO2, making it an unattractive
pathway to GHG mitigation.
CO2 enhanced oil recovery (CO2-EOR) is the geological storage of CO2 in oil fields to reduce
the emissions in the industry and at the same time increase oil production at that oil field. However, CO2 capture and storage through CO2-EOR must be assessed regarding its
potential and CO2 avoidance cost. The IEA estimates that by 2050 a cumulative of 60, 240,
and 360 Gt CO2 could be stored through conventional, advanced EOR+ and maximum
storage EOR+ processes, respectively 18.
Biomass as feedstock has the advantage that upon final use of the biofuels the carbon released has previously been absorbed during their growth through the photosynthesis process. These fuels can provide low net fuel-cycle emissions or even negative values if co-produced CO2 is captured and stored underground, as described by Hailey et al.2. By 2050,
biofuels are expected to provide only 20% of the global transport energy demand according to 19, assuming 50% of the total biomass energy potential would be used for biofuel
costs of bio-refineries infrastructure, low yields, low production volumes, technology-scaling challenges, lower oil prices, and high logistics costs. Co-processing of bio-oil in the refinery has been proposed as an alternative to cope with these challenges 20. This option might takes
advantage of the existing infrastructure and logistics that could be retrofitted for bio-oil co-processing.
1.2 RESEARCH GAPS
Despite the oil industry being a well-developed industry using cutting-edge technology, there are still several topics to be assessed regarding CO2 emission mitigation potentials.
Historically, the industry has been focused on productivity, cost, and developing technology for tailor-based product processing and reaching crude oil underground and under the sea. CO2 mitigation results are usually a consequence of energy efficiency measures to reduce
energy consumption and in turn production cost and also from increasing performance on processing units. A comprehensive analysis of technological alternatives for CO2 mitigation
throughout the oil industry value-chain is, however, lacking.
Most literature studies deal with specific mitigation options or for particular processing units, without considering the complete energy system. Furthermore, real operational data are scarce in the literature. Despite several mitigation options being proposed for the oil industry, challenges remain for a more precise estimation of the mitigation potential. Aspects such as the interaction and combined deployment of mitigation options, a chain perspective analysis for the oil sector, use of real operational data, scale, potential integration with renewables sources (e.g. biomass), facilities lifespan, techno-economic constraints, and infrastructure should be considered to build an industrial-feasible portfolio of mitigation opportunities. Several studies have addressed the potential for improvements in energy efficiency and GHG mitigation in the oil sector, focusing mostly on refineries. Analyses of the potential for energy saving technologies and measures to conserve energy and reduce GHG emissions throughout the value chain (oil extraction, transport, and refining) are, however, lacking. In some oil industry scenarios (Brandt et al., 2010; Brandt and Farrell, 2007), extraction
processes are expected to represent the most energy-intensive processes in the value chain in the near future. Furthermore, estimates available in the literature for the energy efficiency
potential of energy-intensive industries are currently limited by a lack of publicly available plant-level data 15.
As oil production decreases throughout the life span of oil wells, secondary recovery techniques are considered into the oil extraction system. CO2 enhanced oil recovery (CO2
-EOR) is a promising option as it allows for the use and storage of captured CO2 while
integrating CO2 sources and sinks of the value chain. The role and potential of the CCS-EOR
industry as a mitigation strategy for the Colombian oil industry have not yet been fully explored. However, the techno-economic potential of CO2 captured at the primary sources in
the sector has not been assessed under a project integration of sources and sinks within the entire value chain. This research would allow identifying a more precise potential for CO2
capture and storage for the sector as well as the economic constraints represented by the cost of capture, transport, injection, and recycle of CO2, but also the scale and time frame of the
EOR-project.
Research on bio-based fuel production has veered towards pyrolysis as the technology is commercially available, requires relatively low investment, and has adequate scaling capacity
23,24. Several factors have, however, affected the deployment of drop-in fuels produced by
pyrolysis/hydrothermal liquefaction, such as the high cost of bio-refinery infrastructure, low yields, low production volumes, low quality and limited stability, technology-scaling
challenges, low petroleum prices, and high logistics costs. Co-processing of bio-oil in refineries has been proposed as an alternative to cope with these challenges 20.
Nevertheless, several technical issues and economic aspects should be resolved with respect to the biomass conversion processes and the refinery units under consideration. There are two key parameters for assessing feedstock suitability for co-processing – production volumes and ease of integration with the refinery process. As described by Bezergianni et al. 25, most
of these studies focus on stand-alone biofuel production, while studies on the implementation of co-processing for so-called hybrid fuels (simultaneously processing of bio-oils and
petroleum fraction) are scarce. The latter has focused on the chemistry and catalytic
processes of the transformation of biomass to biofuels in conventional refineries, as shown by Melero et al.26, and kinetics and energy balance in FCC by Cruz et al 27, which did not
include operating conditions, type of catalyst, and blending ration in the analysis. However, little attention has been given to the techno-economics analysis (TEA) of co-processing
alternatives. As stated by the IEA 24, the next step for the promotion and use of drop-in fuels
requires the techno-economic assessment of different co-processing combinations of feedstock/reactor to determine the economic viability of refinery integration. Several TEA studies 27–36 are focusing on individual bio-oil co-processing on a specific refinery process
unit, but without key aspects such as bio-oil production technique, biofuel production cost or even comparison between hydrotreatment (HDT) and fluid catalytic cracking (FCC)
processes.
Few studies have assessed the GHG emission reduction potential for combined mitigation options at the aggregate or sector level. Technological measures such as energy efficiency, CCS, bioenergy and fuel switching are part of the mitigation portfolio for industry.
Boulamanti and Moya estimated the potential for the chemical industry in Europe by 2050 by focussing on the best available and innovative technologies. Fais et al. proposed different technological portfolios to estimate CO2 reduction potential for the UK industry, but without
given insights on suitable strategies and associated investment cost. Johansson et al. assessed the CO2 mitigation potential for the oil industry in Europe without determining deployment
pathways or the combined mitigation potential. That study acknowledged that there might be an effect on the CO2 reduction potential when different options are implemented in tandem.
Berghout et. al. assessed combined deployment pathways from measures such as energy efficiency measurements (EEM), CCS, biomass gasification, and pyrolysis-oil-based
biofuels. Nevertheless, those studies are constraint by the level of detail and also the options covered. Therefore, the challenge remains on the integration of the combined mitigation options.
The level of details of most current studies is limited 37. For shorter-term decisions on
investment and policy under an inside-out strategy of the companies, more detailed analysis is highly desired. Therefore, this thesis focuses on a bottom-up techno-economic analysis and potential estimation of GHG mitigation for existing oil facilities using operational data from and detailed insights into the operating units and mitigation options interactions. In summary, estimating the CO2 mitigation potential and avoidance cost in the oil industry still lacks a
comprehensive system analysis of the said industry. Current mitigation technologies are focused on process optimization (e.g. energy efficiency) and CCS. Therefore, it should be extended to a wider technology portfolio, including renewable energy, bio-feedstock, among
others and to assess their interaction on potential deployment pathways. Field data from the process-chain should be incorporated into the analysis for a more precise estimation of the CO2 mitigation potential.
1.3 THESIS OBJECTIVE AND RESEARCH QUESTIONS
This thesis aims to determine the techno-economic and CO2 mitigation potential for
decarbonization pathways for the crude oil industry. It considers a broad portfolio of mitigation options throughout the operational-chain as energy efficiency measurements (EEM), carbon capture and storage (CCS), enhanced oil recovery using CO2 (CO2-EOR),
electrification, low-carbon energy and bio-feedstocks, using a bottom-up approach taking the Colombian oil industry as a case study. This aim will be addressed by the following sub-questions:
1. What are the promising technological options, their potential and mitigation cost for decarbonizing the oil industry?
2. In which way can potential deployment pathways be developed for a decarbonization strategy of the oil industry?
3. What is an effective design for a methodological approach to assess and quantify mitigation options and decarbonization pathways for existing industrial facilities? Table 1 gives an overview of the chapters of this thesis in which these research questions are addressed.
Table 1. Structure of the thesis
Chapter Topic RQ 1 RQ 2 RQ 3
2 Unravelling the potential of energy efficiency in the Colombian oil industry +++ + ++
3 Rapid screening and probabilistic estimation of the potential for CO2-EOR
and associated geological CO2 storage in Colombian petroleum basins ++
4 Exploring the potential of carbon capture and storage-enhanced oil
recovery as a mitigation strategy in the Colombian oil industry +++ + ++
5 Assessing bio-oil co-processing routes as CO2-mitigation strategies in oil
refineries. +++ + ++
6 Fully integrated CO2 mitigation strategy for an existing refinery: Case
study in Colombia + +++ +++
1.4 THESIS OUTLINE
Chapter 2 investigates the potential for improvements in energy efficiency, and their
implications for CO2 abatement, in the Colombian oil industry value chain. It also assesses
the potential cost of conserved energy and mitigated CO2-eq. The oil value chain in Colombia
was based on facilities operated by Ecopetrol for the three main stages: production, transport, and refining. Each stage is further disaggregated into relevant process units. A bottom-up approach was used to identify energy efficiency measures based on an assessment of specific operational data at the process unit level. Several options are considered to improve the energy efficiency such as high energy efficiency practices and equipment, cogeneration, waste heat recovery, methane recovery, flaring and venting reductions. In total, 20 measures and technologies were identified and applied in 48 cases throughout the oil value-chain. The oil value-chain include the stages of extraction, transportation and refinery. The main processes in the value chain have been analysed, including mass, energy, and emissions balances for annual operation under regular conditions. The cost of conserved energy (CCE) and cost of mitigated CO2-eq (CCO2-eq) were calculated and ranked to identify cost-effective
measures through the conservative supply curve (CSC). The findings in this chapters offer a better understanding of the critical stages for energy and GHG savings potentials, as well as investment cost and revenue from a full value chain perspective, based on operational data processing.
Chapter 3 introduces a rapid screening and probabilistic method to estimate the potential of
CO2-EOR and geological storage potential when there is a lack of information using
Colombia as a case study. Estimating the oil recovery potential using CO2 (CO2-EOR) at the
country-level is resource-intensive at a level that is not usually available. The aim of this chapter is therefore two-fold. First to provide an initial estimate of the potential for CO2-EOR
and CO2 storage in Colombia. Second, to assess the impact of using deterministic and
stochastic approaches in the estimation of these potentials at a country level. The
methodological approach is based on a simplified technical screening of the oil fields and estimation of their CO2 storage potential and oil recovery using deterministic and stochastic
methods. This method follows four steps. First, identifying and ranking reservoirs with the most apparent oil recovery potential through CO2-EOR following a set of technical criteria.
Second, a screening methodology is adapted and used to identify the most suitable candidates for CO2-EOR in the short/medium term.
Third, estimating the CO2-EOR and CO2 storage potential using both a deterministic and a
stochastic calculation. Lastly, the probabilistic and deterministic results are compared. The deterministic approach is based on expert insight and information found in literature reviews. The stochastic approach uses statistical data from two different databases to run a Monte Carlo simulation. This chapter therefore provides not only insights into the potential for oil recovery and storage of CO2 in Colombia, but also into the implications of the parameters
assumed in this type of screening study at the regional or country level.
Chapter 4 presents a techno-economic assessment of the potential of carbon capture and
storage (CCS) using CO2-EOR technology for reducing GHG emissions in the Colombian oil
value chain. For this purpose a source-sink matching process was carried out, including CO2
capture potentials in the petroleum, cement, power generation, and bioethanol industries and CO2 storage is oil fields suitable for EOR. The oil company Ecopetrol S.A. was taken as a
case study as it represents the complete chain of the oil industry in Colombia, with
approximately 70% of the crude oil produced and 100% of the oil transported and refined in the country. The method consists of the following steps. First, an inventory was made of the CO2 emissions in the industrial sectors and the capture potential in the selected industrial
sources was quantified. Second, a matching of CO2 sources and sinks was carried out at the
cluster level, using the identified industrial emission points and suitable oil fields selected in chapter 2. Third, potential routes for CO2 transport were identified, by using dedicated gas
pipelines between the sources and sinks identified by the matching. Finally, the economic feasibility was evaluated for each selected CCS-EOR project, using the estimated CO2 costs
for the capture, transport, and oil recovery stages.
Chapter 5 assess the CO2 mitigation potential of bio-oils co-processing in an conventional
medium-conversion oil refinery in Colombia. A comparative assessment is carried out of promising pathways through a techno-economic analysis (TEA). Thirteen pathways were assessed, including vegetable oils (VO), fast pyrolysis oil (FPO), hydrodeoxygenated oil (HDO), catalytic pyrolysis oil (CPO), hydrothermal liquefaction oil (HTLO) and Fischer-Tropsch fuels. The approach used in this study is divided into two sections. First, the identification of technological pathways for the bio-oil co-processing in the refinery and
second, the technical-economic analysis and CO2 mitigation potential for promising routes.
The identification of the bio-oil co-processing pathways was carried out based on a
qualitative-matching analysis of the properties of bio-oils versus key-restriction parameters from the refinery process units. This approach addresses the lack of conclusive information on the appropriate insertion points for bio-oils in the refinery and their constraints regarding bio-oils properties. Each pathway (PW) identifies a refinery process unit (RU) and a type of bio-oil for its co-processing. CO2 emissions for bio and fossil fuel were estimated throughout
the value-chain for each pathway. The process chain includes the stages of production, transport, refining/co-processing and final use. The estimation of CO2 emissions for the
upstream biomass and bio-oil production were based on LCA studies and CO2 specific
emissions reported in the literature. Emissions from the co-processing of bio-oils were calculated using the results of laboratory/pilot-scale tests, reported in the literature and proprietary process patents by Ecopetrol. The annually avoided GHG emissions !"!# (tCO 2-eq/y) and the GHG avoidance cost, $# (€/t CO2-eq) are used as the main technical economic
indicators in this chapter, respectively.
Chapter 6 provides an assessment of the strategy for deploying pathways for greenhouse gas
reduction in the largest medium-conversion oil refinery in Colombia. This chapter aims to asses a combination of individual CO2 mitigation options, based on a bottom-up analysis and
considering synergies, overlap, and negative interactions of different mitigation options for a more realistic insight into costs and constraints of CO2 mitigation. The methodology follow
these steps: 1) Inventory of key parameters of industrial plant of core process (e.g. CO2
emissions, capacity, energy flows); 2) Identification and data collection of GHG emissions mitigation options; 3) Identification of interactions between mitigation options (i.e. a
decrease in GHG reduction potential, cost synergies, economies of scale, lock-in effect); and 4) Assessment of GHG reduction potential and GHG avoidance cost of individual and combi mitigation options. The mitigation options were classified by the period to be deployed and the impact on current plant layout. The former includes short and medium-term measures based on the period to be deployed, less than five years or between 5 and 15 years,
respectively. The impact or complexity of the measure was classified as an add-on, retrofit, replacement or a new concept. The identification of promising deployment pathways combines mitigation options such as energy efficiency measures (EEM), CO2 capture and
technological measures (MTM) (import of low-carbon electricity, blue and green H2) in the
oil refinery. Each portfolio considers non-mutually exclusive options that differ with respect to several criteria such as CO2 avoidance cost, GHG emission reduction potential,
investment, technological maturity and their impact on the core process.
The integration of key aspects developed in previous chapters into an overall and consistent strategy highlight opportunities over time (e.g. lowest cost options for different time frames, breakdown of potentials, required infrastructure and investments), which point out synergies and competition between mitigation options for a more adjusted potential. This analysis allows a risk and uncertainty assessment which underpins decision-making to build and deploy a roadmap to a sector level for a country. The various components of the
decarbonization strategy in total comprise an extensive ambition to reduce GHG emissions for the oil industry, based on the Colombia case study. Some of the key options can lead to fundamental changes in operations and even in the primary energy carriers used (in particular biomass) over time.
Chapter 7 synthesizes the main findings and conclusions from chapter 2-6 and provides
