CO2-EOR Projects Research Articles

ANALYSIS OF THE ECONOMIC PERFORMANCE OF PROJECT ADVANCED OIL RECOVERY WITH CO2 INJECTION (EOR/CCUS-CO2) THROUGH FINANCIAL INDICATORS: BRAZILIAN OVERVIEW

Enhanced Oil Recovery (EOR) is a method associated with Carbon Capture, Utilization, and Storage (CCUS) that consists of injecting CO2 into mature oil fields to store CO2 and enhance production. CO2-EOR projects are costly, requiring technical and economic evaluations to determine their viability. This research analyzes various economic parameters in CO2-EOR projects to develop a comprehensive model for assessing their economic viability in Brazil. The model was tested on projects developed by Silva et al. (2010) and included six scenarios of CO2-EOR in mature reservoirs. The results demonstrate the impact on well completion costs. The scenario with the highest oil production had one additional completion and, for this reason, did not show the best Net Present Value (NPV) result. This work also conducts a sensitivity analysis of parameters and reviews their impacts on NPV. Results indicate a significant impact of barrel price and minimum attractiveness rate on the NPV, while finding that the taxation of excess CO2 was not very significant. Low barrel prices (<$9) proved to be unfeasible with negative NPVs.

Optimization of co-injecting CH4 with CO2 to enhanced oil recovery and carbon storage: A machine-learning based case study on H59 block of Jilin Oilfield, China

During CO2-EOR implementation, CH4 in the reproduced CO2-rich mixture are included in the recycle injection gas. However, improper injection gas composition and operation parameters can reduce the flooding performance.In this study, a machine-learning assisted framework combining Convolutional Neural Networks-Gate Recurrent Unit (CNN-GRU) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) was proposed to obtain the optimal parameters for simultaneous maximum oil production, carbon storage efficiency, and net present value. A case study from the CO2-EOR project in the H59 block of Jilin oilfield, China, was carried out. Base cases were established to evaluate the effects of reinjection CH4 on flooding performance.Results show that the built CNN-GRU model has high prediction accuracy and computational efficiency, and thus can be used as an alternative tool to the reservoir simulator. The proposed framework can find the optimum parameters to improve oil recovery, carbon storage and net present value. The co-injection of CH4 and CO2 improves oil production, and carbon storage performance, but reduces the net present value. This work provides engineers with multiple strategies for decision-making to simultaneously promote flooding performance with CH4 being a co-injectant in CO2-EOR projects.

Machine learning insights to CO2-EOR and storage simulations through a five-spot pattern – a theoretical study

The utilization of CO2 flooding is a widely applied enhanced oil recovery (EOR) technique in mature onshore oil fields. As well as being able to increase oil production and recovery it offers the potential to provide long-term, geological storage for carbon in subsurface reservoirs, thereby contributing to the mitigation of carbon emissions originating from human activities. Substantial research efforts have provided some insight into the uncertainties associated with CO2-EOR projects, but further understanding and the development of more reliable methods are required to accurately predict the outcomes of the complex processes involved in CO2 reservoir flooding. In this study, four machine learning (ML) algorithms were developed to predict CO2 storage mass and cumulative oil production, using the CMG-GEM three-phase flow compositional reservoir simulator with nine reservoir input variables covering a range of uncertainties associated with oil zones. The Mahalanobis distance technique was applied for identifying and excluding outlier data points from the target variable distributions of training data groups. 520 and 439 data records were excluded from the CO2 storage quantity and cumulative oil production dataset, respectively, to generate more reliable predictions. Meticulous training and testing of the ML models revealed that, of the models evaluated, the LSSVM model generated the lowest prediction errors with the test dataset (RMSE = 0.7811 million mt for CO2 storage mass; RMSE = 10.1245 million barrels for cumulative oil production) demonstrating excellent generalization capabilities.

Effect of reservoir heterogeneity on well placement prediction in CO2-EOR projects using machine learning surrogate models: Benchmarking of boosting-based algorithms

Rising Carbon Dioxide (CO2) levels from human activities are driving climate change. Carbon capture and storage (CCS) during enhanced oil recovery (EOR) in underground reservoirs offer both environmental and economic benefits. This method boosts oil production, cuts greenhouse gas emissions, and supports sustainable energy. Precise well placement in CO2-EOR is a crucial task for effective oil displacement, but traditional reservoir simulators are costly. This study explores and compares boosting algorithms, as fast surrogate models, to achieve accurate well placement during CO2-EOR in light oil carbonate reservoirs. The research considers various reservoir scenarios with different geological heterogeneity levels (i.e., homogeneous, moderately heterogeneous, and highly heterogeneous reservoirs). Various parameters, such as injection and production well locations, the distance between production and injection wells in an inverted five-spot pattern, pattern angle, and injection and production rates are explored using a compositional reservoir simulator to assess their impact on the well placement problem. A comprehensive analysis of various boosting algorithms, including AdaBoost, CatBoost, Gradient Boosting, LightGBM, and XGBoost is performed using the simulated dataset to assess their efficacy. The results demonstrate that LightGBM outperformed the other algorithms with the lowest Mean Absolute Error and Root Mean Square Error of 115.3 × 106 $ and 188.2 × 106 $, respectively. Additionally, it demonstrates exceptional speed, averaging 3 to 8 times faster than other boosting algorithms in the three reservoir scenarios. This superior performance coupled with its efficient runtime makes LightGBM the ideal choice for the study objectives. Moreover, the mass balance approach highlights the significant CO2 storage efficiency, emphasizing the effectiveness of CO2-EOR in storing CO2 in underground heterogeneous reservoirs.

Numerical analysis of water-alternating-CO2 flooding for CO2-EOR and storage projects in residual oil zones

Residual oil zones (ROZs) have high residual oil saturation, which can be produced using CO2 miscible flooding. At the same time, these zones are good candidates for CO2 sequestration. To evaluate the coupled CO2-EOR and storage performance in ROZs for Water-Alternating-CO2 (WAG) flooding, a multi-compositional CO2 miscible model with molecular diffusion was developed. The effects of formation parameters (porosity, permeability, temperature), operation parameters (bottom hole pressure, WAG ratio, pore volume of injected water), and diffusion coefficient on the coupled CO2-EOR and storage were investigated. Five points from the CO2 sequestration curve and the oil recovery factor curve were selected to help better analyze coupled CO2-EOR and storage. The results demonstrate that enhanced performance is observed when formation permeability is higher and a larger volume of water is injected. On the other hand, the performance diminishes with increasing porosity, molecular diffusion of gas, and the WAG ratio. When the temperature is around 100 °C, coupled CO2-EOR and storage performance is the worst. To achieve optimal miscible flooding, it is recommended to maintain the bottom hole pressure (BHP) of the injection well above 1.2 minimum miscibility pressure (MMP), while ensuring that the BHP of the production well remains sufficiently high. Furthermore, the tapered WAG flooding strategy proves to be profitable for enhanced oil recovery, as compared to a WAG ratio of 0.5:1, although it may not be as effective for CO2 sequestration.

Economics of Combined CO2-EOR and CCS Projects Based on the Example of a Polish Multilayered Oil Field

Economics of Combined CO2-EOR and CCS Projects Based on the Example of a Polish Multilayered Oil Field

A novel analytical technique for determining inflow control devices flow area in CO2-EOR and CCUS projects

CO2-EOR is one of the principal techniques for enhanced oil recovery (EOR). The CO2 injection not only promotes oil recovery but also leads to greenhouse gas discharge reduction. Nonetheless, a key challenge in the CO2 flooding process is a premature CO2 breakthrough from highly permeable zones. In recent years, Inflow Control Devices, ICDs, have been used as a potential solution to mitigate an early gas breakthrough. The key and important parameter in ICDs installation is obtaining its opening flow area. The common ways to obtain the ICD flow area such as utilizing optimization algorithms are very complicated and time-consuming, and further these methods are not analytical. The aim of this work is to solve the mentioned challenges—postpone the breakthrough time in gas injection and present an easy, fast, and analytical technique for obtaining ICDs flow area. This paper presents a new analytical method for obtaining inflow control devices flow area for injection wells in an oil reservoir under CO2-EOR in order to balance the injected CO2 front movement in all layers. Then, in order to compare the advantages and disadvantages of the presented technique with other methods such as optimization algorithms, a case study has been done on a real reservoir model under CO2 injection. Later, the results of studied scenarios in the case studied are given and compared. The results show that by utilizing the proposed method recovery factor is raised by improving sweep efficiency, and the breakthrough time is more postponed compared to the other methods about 400 days. Further, the ICD flow area calculation takes 2 min by presented analytical techniques, but the optimization algorithm takes 4040 min to run the simulation model to find the ICD flow area. In the end, the findings of the presented analytical formula can help to set the ICD flow area very fast without the simulation and help researchers for a better quantitative understanding of parameters affecting the ICD flow area by the given formula such as reservoir permeability.

Proxy Model Development for the Optimization of Water Alternating CO2 Gas for Enhanced Oil Recovery

Optimization studies are an important task in reservoir engineering practices such as production optimization and EOR (Enhanced Oil Recovery) assessments. However, they are extensive studies with many simulations that require huge computational effort and resources. In terms of EOR, CO2 injection is one of the most common methods employed due to a high recovery potential and environmental benefits. To assess the feasibility of CO2-EOR projects, a reservoir design study must be conducted before optimization is performed. Some studies have demonstrated the advantages of employing proxy models to perform this task in terms of saving huge amounts of computer memory space and time. In this study, proxy models were developed to solve a multi-objective optimization problem using NSGA-II (Non-dominated Sorting Genetic Algorithm II) in two selected reservoir models. The study was performed for a CO2-WAG (Water Alternating Gas) application, where gas and water injection rates and half-cycle lengths were assessed to maximize the oil recovery and CO2 stored in the reservoir. One model represents a simple geological model (the Egg Model), while the other represents a complex model (the Gullfaks Model). In this study, the good performance of the proxy models generated accurate results that could be improved by increasing the amount of sampling and segmenting the behavior of the reservoir model (depending on the complexity of the reservoir model). The developed proxies have an average error of less than 2% (compared with simulation results) and are concluded to be robust based on the blind test results. It has also been found that to reach the maximum oil recovery using CO2-WAG, the maximum gas injection rate with the minimum water injection rate is required. However, this configuration may result in a reduction in the total CO2 stored in the reservoir.

Interpretable knowledge-guided framework for modeling minimum miscible pressure of CO2-oil system in CO2-EOR projects

Carbon dioxide enhanced oil recovery (CO2-EOR) is a promising application for carbon capture, utilization and storage (CCUS). Accurate modeling of CO2-oil minimum miscible pressure (MMP) is crucial for CO2-EOR projects. In this study, a knowledge-guided framework for an extreme gradient boosting machine (XGBoost) and interpretable tabular learning architecture (TabNet), called KXGB and KTabNet, respectively, are developed to model the MMP. The proposed models are strengthened using a large MMP database of 421 samples collected from literature. Domain knowledge is integrated into intelligent models to prevent data-driven models from producing predictions that violated the domain knowledge. The Shapley Additive Explanations (SHAP) method is used to explain the proposed model to ensure the credibility of petroleum engineers. To further verify the model’s effectiveness, the same experimental strategy is employed to compare the proposed models with existing machine-learning (ML) methods. The results show that KXGB is the most recommended superior solution for modeling MMP owing to its outstanding performance and simplicity of optimization. The correlation coefficient, root mean square error, and mean absolute error are 0.9833, 0.7637 and 0.55, respectively. However, KTabNet has great potential. Although its accuracy is slightly lower than that of the former, its strong representation ability and decision transparency may be favorable for future research. This study also demonstrate that the proposed framework conforms to certain theoretical rules and has a reasonable domain of applicability. To the best of our knowledge, this is the first study on integration of domain knowledge into MMP modeling methods. The experience and insights obtained from this study can guide CO2-EOR projects and other tabular data modeling in the oil and gas industry.

Residual Trapping of CO2 and Enhanced Oil Recovery in Oil-Wet Sandstone Core – A Three-Phase Pore-Scale Analysis Using NMR

HypothesisCombining Carbon Geo-sequestration (CGS) with Enhanced Oil Recovery (EOR) in three-phase flow oil reservoirs is an effective and economically attractive technology to mitigate global warming caused by emission of anthropologic CO2 from fossil fuel combustion, as it offsets some of the costs of CO2 extraction. The targeted oil reservoirs display oil-wet characteristics, primarily because of the strong affinity of polar molecules present in oil; a mixture of a large number of chemical compounds to the rock's surface; and oil-wet surfaces that drastically reduce storage capacity of geological trapping mechanisms and increase the vertical migration of CO2. Thus, it is essential to characterise the effect of wettability of rock on pore-scale physics in a pore network that dominate three-phase flow, which in turn control overall reservoir‐scale fluid dynamics and; thus, determine residual CO2 trapping and the success of CO2‐EOR projects. ExperimentalTo fully examine this, we present the first in situ NMR T1-T2 2D images and transverse relaxation time (T2) to quantify residual CO2 (capillary) trapping for an oil-wet core at reservoir conditions, representative of virgin oil reservoirs at around 1000-meter depth. We also systematically measure how much additional oil can be produced, where we compared the results with measurements performed on an analogous water-wet core. ResultsThe results show a significant residual CO2 saturation (12%) can be stored, which is similar to the values found in analogue two-phase flow for oil-wet sandstone samples, although some details are different and displacements are significantly complex, and a substantial amount of oil (51%) was recovered. In contrast, results of similar tests conducted on an identical analogous water-wet core show a higher CO2 trapping (20%), where oil production was substantially higher (77%). We have also presented the oil displacement efficiency (η) versus CO2 injection (PV), showing the percentage oil displacement efficiency of water-wet (0.6%) to be significantly higher than that of analogue oil-wet core (0.4%). Interpretation of the findingThese results significantly improve geo-storage integrity; i.e. capacities and containment security, over reservoir-scale implementations, in addition to having a substantial impact on outlining CO2-EOR projects in term of budgets and storage capacities for CO2-EOR and CO2 geo-sequestration.

Study Describes Challenges, Opportunities of CO2 EOR in China

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 209468, “CCUS In China: Challenges and Opportunities,” by Hu Guo, China University of Petroleum; Xiuqin Lyu, Sinopac Northwest Oil Field Company; and En Meng, China University of Petroleum, et al. The paper has not been peer reviewed. One of the most attractive carbon capture, usage, and storage (CCUS) applications in China is that of carbon dioxide (CO2) enhanced oil recovery with captured CO2 (CCS EOR). CO2 EOR with captured CO2 presents an important path for China. The complete paper reviews the progress of CCUS technology in China. The current challenges of CCS EOR include high capture costs, small scale, low incremental oil recovery, and huge capital input. The costs can be significantly reduced when the scale is enlarged to the commercial level and transportation costs are further reduced by pipelines or trains. Importance of CCUS At the time of writing, 49 CCUS pilots or field tests had been conducted or were under construction in China. CCUS demonstration projects were small-scale, and no projects involving more than 1.0 million tons of CO2 per year (mtpa) were conducted. 1.3 million tons of CO2 were estimated to be injected in 2020 for EOR use. The authors reference a study that mentions nine CO2 EOR projects. Among these, the Jilin oilfield project was notable for its size of 2.0 million cumulative tons of CO2 injected. CO2-enhanced coalbed methane was also an attractive option for China, and several field tests were conducted by CNOOC and its partners. Another notable CCUS demonstration project involved the Shaanxi Jinjie power plant. This was the largest coal-fired power postcombustion (PC) CO2-capture project, with a capture capacity of 0.15 mtpa. The project began in November 2019. In January 2021, equipment installation was completed. By June of 2021, 168 trial operations had been passed. The captured CO2 will be used for EOR. This demonstration project will gain knowledge to reduce PC CO2 emissions, a great challenge for China because the majority of electricity is based on coal combustion. Gas processing and power plants rank first for the US in terms of CCUS project numbers. Power plants also rank first in the number of CCUS projects for China, but the number of natural-gas-processing projects was much less.

Investment decision on carbon capture and utilization (CCU) technologies—A real option model based on technology learning effect

Carbon Capture and Utilization (CCU) technologies are crucial to achieving carbon neutrality targets. However, assessing the investment value and timing comprehensively is still challenging for CCU due to uncertainties in technologies and markets from a long-term perspective. In order to assist decision making, this work develops a new real option investment decision model based on the technology learning effect. In particular, the component-based two-factor technology learning curve approach is proposed to predict the future costs for each component. To verify this model, it is used to analyze two CCU processes, including the enhanced oil recovery using CO2 (CO2-EOR) and methanol synthesis from captured CO2 (CO2-MET). Results show that the proposed model can effectively predict the technology cost curve and find the optimal investment decision for the two applications considering various uncertainties. It is also found that the oil price at least needs to be over 80 $/barrel for CO2-EOR and the methanol price needs to be over 580 $/ton for CO2-MET respectively to trigger immediate investment. Comparatively, investing in CO2-MET projects is more economical than in CO2-EOR projects.

Inter-well connectivity detection in CO2 WAG projects using statistical recurrent unit models

Routine well-wise injection and production measurements contain significant information on subsurface structure and properties. Data-driven technology that interprets surface data into subsurface structure or properties can assist operators in making informed decisions by providing a better understanding of field assets. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process.SRU is a special type of recurrent neural network (RNN) that allows for better characterization of temporal trends, by learning various statistics of the input at different time scales. In our application, the complete states (injection rate, pressure and cumulative injection) at injectors and pressure states at producers are fed to SRU as the input and the phase rates at producers are treated as the output. Once the SRU is trained and validated, it is then used to assess the connectivity of each injector to any producer using permutation variable importance method, wherein inputs corresponding to an injector are shuffled and the increase in prediction error at a given producer is recorded as the importance (connectivity metric) of the injector to the producer. This method is tested in both synthetic and field-scale cases.The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. This significantly improves confidence in our data-driven procedure.The novelty of this work is that it is purely data-driven method and can directly interpret routine surface measurements to intuitive subsurface knowledge. Furthermore, the streamline-based validation procedure provides physics-based backing to the results obtained from data analytics. The study results in a reliable and efficient data analytics framework that is well-suited for large field applications.

A comprehensive techno-eco-assessment of CO2 enhanced oil recovery projects using a machine-learning assisted workflow

Carbon dioxide enhanced oil recovery (CO2-EOR) projects not only extract residual oil but also sequestrate CO2 in the depleted reservoirs. This study develops a machine-learning-based workflow to co-optimize the hydrocarbon recovery, CO2 sequestration volume and project net present value (NPV) simultaneously. Considering the trade-off relationships among the objective functions, support vector regression with Gaussian kernel (Gaussian- SVR) proxies are coupled with multi-objective particle swarm optimization (PSO) protocol and generate Pareto optimal solutions. Taking advantage of the high computational efficacy of the proxy model, economic uncertainties introduced by tax credits, capital costs and oil price are investigated by this study. The results indicate that the tax incentive policy (Section 45Q) plays a vital role in enhancing the economic returns of CO2-EOR projects, especially under the depression of crude oil market. The proposed workflow has been successfully implemented to optimize a water alternative CO2 (CO2-WAG) injection project in a depleted oil sand in the US. The optimization results yield an incremental oil production of 15.8 MM STB and 1.37 MM metric tons of CO2 storage in a 20-year development strategy, with the highest project NPV to be 205.6 MM US dollars.

Technical and Economic Evaluation of CO2 Capture and Reinjection Process in the CO2 EOR and Storage Project of Xinjiang Oilfield

CO2 capture and reinjection process (CCRP) can reduce the used CO2 amount and improve the CO2 storage efficiency in CO2 EOR projects. To select the best CCRP is an important aspect. Based on the involved equipment units of the CCRP, a novel techno-economic model of CCRP for produced gas in CO2 EOR and storage project was established. Five kinds of CO2 capture processes are covered, including the chemical absorption using amine solution (MDEA), pressure swing adsorption (PSA), low-temperature fractionation (LTF), membrane separation (MS), and direct reinjection mixed with purchased CO2 (DRM). The evaluation indicators of CCRP such as the cost, energy consumption, and CO2 capture efficiency and purity can be calculated. Taking the pilot project of CO2 EOR and storage in XinJiang oilfield China as an example, a sensitivity evaluation of CCRP was conducted based on the assumed gas production scale and the predicted yearly gas production. Finally, the DRM process was selected as the main CCRP associated with the PSA process as an assistant option. The established model of CCRP can be a useful tool to optimize the CO2 recycling process and assess the CO2 emission reduction performance of the CCUS project.

Experimental and microscopic simulation study on CO2 diffusion in an oil-water liquid system

CO2 diffusion is important in CO2 enhanced oil recovery and CO2 capture and storage. However, most reported studies only focused on diffusion in single phase. In this study, a new model considering CO2 diffusion in multi-phase was established, and a new mathematical model is proposed based on the diffusion stage division. Diffusion experiments were conducted to determine the diffusion coefficients. Then, microscopic simulations were conducted to investigate CO2 diffusion in porous media using the determined coefficients. According to the results, the coupling between the experimental pressure and model indicated the accuracy of the model. The pressure and temperature were key parameters for controlling the coefficients. Water distribution had a critical impact on CO2 diffusion in porous media. CO2 diffusion shows worse performance in models with severe water block effect. The findings of this study can help guide the design of CO2 EOR and CCS projects.

Development of an Integrated Carbon Capture, Utilization, and Storage Hub in the United States

The Integrated Midcontinent Stacked Carbon Storage Hub (IMSCS-HUB) is a first of a kind carbon capture, utilization, and storage (CCUS) project in the midwestern United States. The project is supported under the US Department of Energy’s Carbon Storage Assurance Facility Enterprise (CarbonSAFE) program. The IMSCS-HUB will collect 15 million metric tonnes (MT) per year or more in Iowa and Nebraska and transport it to saline and CO2 enhanced oil recovery (EOR) storage sites in western Nebraska, Kansas, and Texas. Sources include multiple ethanol plants (approximately 1.8 MT/Year), coal fired electric plants (approximately 12.6 MT/Year), and natural gas fired electric plants (approximately 0.6 MT/Year). The passage of an updated tax credit for storage in February 2018 provides a strong economic driver for all of the sources in the hub to develop a hub project. The tax credit provides approximately $50/T for CO2 stored in saline storage units and $35/T for CO2 stored as part of CO2-EOR production. In addition, the inclusion of many bio-energy sources such as ethanol plants allows for early implementation of the project by taking advantage of inexpensive capture ($10/MT to $12/MT) and subsidies from California’s Low Carbon Fuel Standard program. Capture cost estimates for the electric utilities is approximately $33/MT. Transport costs range from $0.6/MT to $5.40/MT. Storage costs range from $3/MT to $4/MT. The hub partners include Battelle, Advanced Resources International, Archer Daniels Midland, Nebraska Public Power District, and others. The region of the project stretching from eastern Iowa to southwestern Nebraska acts as a source corridor, where sources add CO2 to the transport network. The transportation network will consist of a common carrier trunk line designed to connect the largest sources. Smaller sources will be connected through spur lines. The final routing will be selected using the location of existing rights away and environmental factors such as resource locations the critical species habitats. SimCCS and other tools will be used to finalize the routes. The region of the project from southwestern Nebraska to southwest Kansas in a storage corridor where CO2 may be stored in either saline projects or CO2-EOR projects. South of Kansas the CO2 will be transported to Texas for utilization in CO2-EOR. Two saline storage sites and two EOR opportunities have been identified as anchor projects for the storage corridor. The anchor sites are at the northern and southern sides of the storage corridor and will act to validate the storage potential of the entire corridor. In Nebraska saline storage is being planned in Perkins county and CO2-EOR is being studied at Sleepy Hollow Field in Red Willow county. In Kansas, saline storage and CO2- EOR are being planned for Patterson-Heinitz-Hartland (Patterson) Field in Kearney County. Multiple storage formations occur vertically at each site allowing for stacked storage minimizing the surface footprint for monitoring and maximizes the use of storage resources at each site. Data from new tests wells or seismic surveys for each of the sites has been used to create detailed geologic and reservoir models. Modeling for each of the anchor saline sites indicates at least a 50 million tonne storage capacity. The CO2-EOR projects are expected to utilize 2.2 MT and 3.9 MT of CO2 and produce 4.3 and 9.8 million barrels of oil for Sleepy Hollow and Patterson Fields, respectively. The inclusion of CO2-EOR along with saline storage adds an additional economic driver for the project with an estimated revenue of $128/MT and $154/MT of CO2 utilized. The results of the first two phases of the project demonstrate the feasibility of the midcontinent hub concept that couples a diverse set of redundant sources to multiple saline and CO2-EOR projects. The results show that the economics and geology are favorable for early implementation of commercial scale CCUS. The existence of low carbon fuel standard subsidies, storage tax credits, and revenue from produced oil act as economic drivers to support the hub in addition to the climate benefit gained by storing CO2 the hub supports local agriculture and oil production helping to create public support. This presentation will discuss the project development and project economics and how they relate to both stacked saline storage and CO2-EOR in the development of a storage hub. The development of a hub project allows the transport of CO2 from areas such as Iowa that have poor storage potential to areas such as western Nebraska, Kansas, and Texas that have high storage potential. This is critical to the implementation of CCUS in the midcontinent US. The presentation will also discuss the advantage of including redundant sources and sinks to develop a CO2 market that can sustain itself as a commercial enterprise after government funding ends.

Significance of Enhanced Oil Recovery in Carbon Dioxide Emission Reduction

Limiting the increase in CO2 concentrations in the atmosphere, and at the same time, meeting the increased energy demand can be achieved by applying carbon capture, utilization and storage (CCUS) technologies, which hold potential as the bridge for energy and emission-intensive industries to decarbonization goals. At the moment, the only profitable industrial large-scale carbon sequestration projects are large-scale carbon dioxide enhanced oil recovery (CO2-EOR) projects. This paper gives a general overview of the indirect and direct use of captured CO2 in CCUS with a special focus on worldwide large-scale CO2-EOR projects and their lifecycle emissions. On the basis of scientific papers and technical reports, data from 23 contemporary large-scale CO2-EOR projects in different project stages were aggregated, pointing out all the specificities of the projects. The specificities of individual projects, along with the lack of standardized methodologies specific for estimating the full lifecycle emissions resulting from CO2-EOR projects, pose a challenge and contribute to uncertainties and wide flexibilities when estimating emissions from CO2-EOR projects, making the cross-referencing of CO2-EOR projects and its comparison to other climate-mitigation strategies rather difficult. Pointing out the mentioned project’s differentiations and aggregating data on the basis of an overview of large-scale CO2-EOR projects gives useful information for future work on the topic of a CO2-EOR project’s lifecycle emissions.

Could China meet its emission reduction goal by CO2-EOR

PurposeThis paper aims to discuss opportunities for pairing the carbon dioxide (CO2) points of supply from stationary sources such as power plants, steel and cement production, coal to liquid plants and refineries, with potential oil reservoirs in China.Design/methodology/approachThis study builds a linear optimization model to analyze the tradeoffs in developing CO2-enhance oil recovery (EOR) projects in China for a range of policy options to match points of supply with the points of demand (oil fields). The model works on optimizing CO2 application costs by meeting four principal components; CO2 storage, CO2 capture, transport costs and additional oil recovery.FindingsThis study reveals new opportunities and economic sources to feed CO2-EOR applications and offers reasonable options to supply CO2 for potential points of demand. Furthermore, power plants and coal to liquid industries had the most significant and economic contributions to potential CO2-EOR projects in China. Total annual emission reduction is expected to be 10% (based on 10 Gton annual emissions). The emission reductions and potential CO2 storage from the different industries as follow; 94% from power plants, 4% from biofuel and 2% from coal to liquid plants.Social implicationsCarbon capture and storage (CCS) is one practice aiming to reduce the amounts of anthropogenic emissions of carbon dioxide emitted into the atmosphere and reduce the related social costs. However, given the relatively high cost associated with this practice, coupling it with EOR could offer a significant financial incentive to facilitate the development of CCS projects and meet climate change objectives.Originality/valueThe model used in this study can be straightforwardly adapted to any geographic location where industry and policymakers are looking to simultaneously reduce CO2 emissions while increasing hydrocarbon recovery. The model is highly adaptable to local values in the parameters considered and to include additional local considerations such as geographic variation in capture costs, taxes and premiums to be placed on CO2 capture in so-called “non-attainment zones” where pollution capture make could make a project politically and economically viable. Regardless of how and where this model is applied, it is apparent that CO2 from industrial sources has substantial potential value as a coproduct that offsets its sequestration costs using existing, commercially available CO2-EOR technology, once sources and sinks are optimally paired.

Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.