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A viable business case has posed a challenge for CCUS (carbon capture, utilization and storage) projects and the aspirations of sustainability. With its vision to further expand its EOR portfolio and positively contribute to sustainability through CO2 injection, ADNOC Onshore started to contemplate new ways of implementing carbon capture in a more profitable and environmentally responsible manner. A CCUS Study was initiated to identify the optimum trade-off between key variables, such as injected CO2 purity, in order to minimize carbon footprint and cost without altering reservoir oil recovery. This paper presents an innovative study to understand optimal design of a CO2 recovery plant in collaboration with Sustainability and CO2 capture experts. The team explored, in a synergized manner, the subsurface and surface aspects of CO2 capture and conducted reservoir simulations, techno-economic assessments of the surface facilities, reviews of the state-of-the-art CO2 technologies, as well as quantifying the carbon footprint reduction. The prevalent concept that higher CO2 purity results in a higher oil recovery needed to be validated. Therefore, several CO2 purity levels were investigated by extensive reservoir modelling and authenticated by lab experimental analysis. The study focused on a specific future project and estimated that capturing the CO2 breakthrough would segregate millions of tons of CO2 per year, approximately equivalent to the emissions of 5 billion car miles per year. The corresponding estimated value could be drastically decreased, if the system's designed CO2 purity is decreased. The main findings demonstrated that reducing the CO2 purity from 99% to 85% has inconsequential effect on the field recovery factor and negligible increase in Minimum Miscibility Pressure and required surface compression capacity. This can lead to a simplification of the carbon capture plant, with less equipment and footprint, and significant reduction in CAPEX (up to 40%), OPEX and GHG emissions. This collaborative effort proved that a profitable business case is achievable from CCUS project, provided that the right parameters are assessed and optimized. The cost and energy footprint of the carbon capture facilities are driven by the concentration of CO2 in the feedstock and the purity of the CO2 required for injection purposes. With an attractive economical model, future CCUS projects, such as the project under study, with its environmental contribution will become viable financial options. Looking for such options is critical particularly for projects targeting the replacement of current hydrocarbon injection with CO2 injection in the existing developments, while expanding CO2 injection into new development areas. This will significantly reduce the country’s global carbon footprint, as CO2 producing industries will be able to offset their carbon emissions footprint by sending their CO2 streams to ADNOC Onshore’ s facilities for injection and sequestration into subsurface reservoirs.
A viable business case has posed a challenge for CCUS (carbon capture, utilization and storage) projects and the aspirations of sustainability. With its vision to further expand its EOR portfolio and positively contribute to sustainability through CO2 injection, ADNOC Onshore started to contemplate new ways of implementing carbon capture in a more profitable and environmentally responsible manner. A CCUS Study was initiated to identify the optimum trade-off between key variables, such as injected CO2 purity, in order to minimize carbon footprint and cost without altering reservoir oil recovery. This paper presents an innovative study to understand optimal design of a CO2 recovery plant in collaboration with Sustainability and CO2 capture experts. The team explored, in a synergized manner, the subsurface and surface aspects of CO2 capture and conducted reservoir simulations, techno-economic assessments of the surface facilities, reviews of the state-of-the-art CO2 technologies, as well as quantifying the carbon footprint reduction. The prevalent concept that higher CO2 purity results in a higher oil recovery needed to be validated. Therefore, several CO2 purity levels were investigated by extensive reservoir modelling and authenticated by lab experimental analysis. The study focused on a specific future project and estimated that capturing the CO2 breakthrough would segregate millions of tons of CO2 per year, approximately equivalent to the emissions of 5 billion car miles per year. The corresponding estimated value could be drastically decreased, if the system's designed CO2 purity is decreased. The main findings demonstrated that reducing the CO2 purity from 99% to 85% has inconsequential effect on the field recovery factor and negligible increase in Minimum Miscibility Pressure and required surface compression capacity. This can lead to a simplification of the carbon capture plant, with less equipment and footprint, and significant reduction in CAPEX (up to 40%), OPEX and GHG emissions. This collaborative effort proved that a profitable business case is achievable from CCUS project, provided that the right parameters are assessed and optimized. The cost and energy footprint of the carbon capture facilities are driven by the concentration of CO2 in the feedstock and the purity of the CO2 required for injection purposes. With an attractive economical model, future CCUS projects, such as the project under study, with its environmental contribution will become viable financial options. Looking for such options is critical particularly for projects targeting the replacement of current hydrocarbon injection with CO2 injection in the existing developments, while expanding CO2 injection into new development areas. This will significantly reduce the country’s global carbon footprint, as CO2 producing industries will be able to offset their carbon emissions footprint by sending their CO2 streams to ADNOC Onshore’ s facilities for injection and sequestration into subsurface reservoirs.
Phase equilibrium calculations require experimental lab data to constrain component properties in an equation of state (EOS) model. These thermodynamics-based models generally perform well when it comes to predicting conventional PVT experiments but often fall short when it comes to predicting gas injection experiments, particularly for CO2 injection. We therefore seek to develop methods that can help provide a good initial estimate of the swelling curve, in cases where laboratory data are not available. Our company PVT database compromises more than 2,200 PVT studies, which enables us to pursue three different avenues for predicting the CO2 swelling curve. The first method relies on a machine-learning algorithm, which takes fluid composition and temperature as input. In general, we find that this solution does not preserve monotonicity of the pressure-dependent properties and it extrapolates poorly outside the parameter space used for training. As an example, it fails to predict the first-contact miscible pressure defined as the maximum pressure on the swelling curve. The second option involved correlating swelling pressure, swelling factor and swelling density as a function of the amount of injected gas. We find that all three curves are well-represented by a parabolic expression and we were able to correlate the coefficients as a function methane content in the reservoir fluid only. The resulting model predicts saturation pressure, swelling factor, and density of the swollen mixtures with an absolute average deviation of 4.8%, 2.3% and 1.7%, respectively, which is an excellent starting point for tuning an EOS model for EOR screening studies until experimental data becomes available. The third strategy involved tuning a separate EOS model to each of the 34 CO2 swelling studies and then attempt to correlate the EOS component properties. We compare the values of the tuned pseudo-component properties against some standard correlations such as Pedersen, Kesler-Lee, Riazi-Daubert and others. We find that the Pedersen correlations for critical pressure, critical temperature and acentric factor provide a more accurate initial guess than the other correlations tested. However, we observed that the tuned solution depended to some extent on the initial guess. We find that for our fluid systems, the default values for the critical volume of the pseudo-components need to be reduced by 15% to better predict the viscosity using the LBC model. Despite the slightly improved property estimation, we did not manage to find a clear trend for the binary interaction coefficient between CO2 and the plus fraction. Therefore, we would recommend predicting the CO2 swelling curve with the set of parabolic correlations.
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