Expert-based development of a standard in CO2 sequestration monitoring technology

Bureau of Economic Geolog

Oil Security Short- and Long-Term Policies

Increasing oil security represents one of the most important policy actions, especially within IEA countries. Short and long term mechanisms could help such goal. On the short term side, revision of IEA emergency response oil stock system has been discussed. The attention is mainly focused on three issues: the high costs of stock management for private industries, the possible use of strategic reserves to smooth price when no high supply disruption has taken, the extension of IEA emergency system to non-OECD countries. The main actions specifically proposed by the European Commission are: an harmonisation of national storage systems, with the institution of public and private agency, a wider co-ordinated use of security stocks, and an increase in the physical amount of oil stocks. Long term measures for enhancing oil supply security can be seen on the demand-side and the supply-side. Main demand-side policies could be the following: energy saving and efficiency, investments in research and technology, and reduction of oil price inelasticity especially for transport sector. Main supply-side policies can be summarized into co-operation and institutional promotion for supply diversification of suppliers/routes. Main factors that could affect described policies could be the liberalization of international trade even in the energy sector and the increasing role of oil demand from developing countries.Oil, Security, Energy

Strategic and Tactical Crude Oil Supply Chain: Mathematical Programming Models

Crude oil industry very fast became a strategic industry. Then, optimization of the Crude Oil Supply Chain (COSC) models has created new challenges. This fact motivated me to study the COSC mathematical programming models. We start with a systematic literature review to identify promising avenues. Afterwards, we elaborate three concert models to fill identified gaps in the COSC context, which are (i) joint venture formation, (ii) integrated upstream, and (iii) environmentally conscious design

Carbon Capture; Transport and Storage in Europe: A Problematic Energy Bridge to Nowhere?

This paper is a follow up of the SECURE-project, financed by the European Commission to study “Security of Energy Considering its Uncertainties, Risks and Economic Implications”. It addresses the perspectives of, and the obstacles to a CCTS-roll out, as stipulated in some of the scenarios. Our main hypothesis is that given the substantial technical and institutional uncertainties, the lack of a clear political commitment, and the available alternatives of low-carbon technologies, CCTS is unlikely to play an important role in the future energy mix; it is even less likely to be an “energy bridge” into a low-carbon energy futureCarbon Capture, Transport, Storage

Potential sinks for geologic storage of carbon dioxide generated by power plants in North and South Carolina

Duke Energy Progress Energy Santee Cooper Power SCANA CorporationBureau of Economic Geolog

Planning and optimising petroleum supply chain

The purpose of this paper is to develop and implement an integrated framework for planning and optimising petroleum supply chain (SC). The framework consists of two stages; first-stage is to address mathematically the strategic planning and the optimisation of the extracted oil which is needed within the petroleum supply chain. While the second stage focuses on the operational planning of the refinery area using a combined discrete and continues simulation modelling techniques. The simulation model considered the following factors: Input Rate, Oil Quality, Distillation Capacity and Number of Failed Separators which are analysed against the performance measures: Total Products and Equipment Utilisation. The results obtained from the experiment are analysed statistically using SPSS Program

A Comprehensive Techno-Economic Framework for Shale Gas Exploitation and Distribution in the United States

Over the past years, shale gas has turned into one of the most significant sources of energy in the United States. Technological advancements have provided the energy industry with the necessary tools to allow the economic exploitation of an enormous volume of natural gas trapped in shale formations. This has boosted the domestic gas production and generated a boom in other sectors of the economy in the country. However, major challenges are involved in the development of shale gas resources. A drastic decline of wells’ productivity, the costs involved in the gas production and distribution facets, and the volatile behavior of the energy market represent some of the complexities faced by a gas operator. In this context, the utilization of a comprehensive frameworks to analyze and develop long-term strategies can represent a meaningful supporting tool for shale gas operators. The main objective of this research work is the development and implementation a novel techno-economic framework for the optimal exploitation and delivery of shale gas in the United States. The proposed framework is based on an interdisciplinary approach that combines data driven techniques, petroleum engineering practices, reservoir simulations and mathematical programming methods. Data analysis algorithms are implemented to guide the decision-making processes involved in the unconventional reservoir and define the predominant trends of certain exogenous parameters of the system. Petroleum engineering practices and reservoir simulation models are required for a realistic description of the formations and the proper definition of strategies to extract the gas from the shale rock. Finally, the mathematical programming is required for describing the surface facilities design and operations to ensure the allocation of the shale gas in the different commercialization points. The output of this framework will provide the optimal operations and infrastructure by maximizing the net present value (NPV). To demonstrate the efficacy of the proposed decision-making structure, a case study based on the liquid-rich region of the Marcellus play is considered in this work. The application of the proposed framework depicts the influence of reservoir complexities and external factors in establishing optimal strategic decisions for the exploitation, processing and allocation of shale gas. The coordination of the different facets including the drilling and completion activities and the design and operation of the surface facilities has a key role in maintaining the economy of a shale gas venture above its economic threshold

The Design of a Carbon Tax

We consider the design of a tax on greenhouse gas emissions for a developed country such as the United States. We consider three sets of issues: the optimal tax base, issues relating to the rate (including the use of the revenues and rate changes over time) and trade. We show that a well-designed carbon tax can capture about 80% of U.S. emissions by taxing fewer than 3,000 taxpayers and up to almost 90% with a modest additional cost. We recommend full or partial delegation of rate setting authority to an agency to ensure that rates reflect new information about the costs of carbon emissions and of abatement. Adjustments should be made to the income tax to ensure that a carbon tax is revenue neutral and distributionally neutral. Finally, we propose an origin-based system for trade with countries that have an adequate carbon tax and a system of border taxes for imports from countries without a carbon tax. We suggest a system that imposes presumptive border tax adjustments with the ability of an individual firm to prove that a different rate should apply. The presumptive tax could be based either on average emissions for production of the item by the exporting country or by the importing country.

Advancing Carbon Sequestration through Smart Proxy Modeling: Leveraging Domain Expertise and Machine Learning for Efficient Reservoir Simulation

Geological carbon sequestration (GCS) offers a promising solution to effectively manage extra carbon, mitigating the impact of climate change. This doctoral research introduces a cutting-edge Smart Proxy Modeling-based framework, integrating artificial neural networks (ANNs) and domain expertise, to re-engineer and empower numerical reservoir simulation for efficient modeling of CO2 sequestration and demonstrate predictive conformance and replicative capabilities of smart proxy modeling. Creating well-performing proxy models requires extensive human intervention and trial-and-error processes. Additionally, a large training database is essential to ANN model for complex tasks such as deep saline aquifer CO2 sequestration since it is used as the neural network\u27s input and output data. One major limitation in CCS programs is the lack of real field data due to a lack of field applications and issues with confidentiality. Considering these drawbacks, and due to high-dimensional nonlinearity, heterogeneity, and coupling of multiple physical processes associated with numerical reservoir simulation, novel research to handle these complexities as it allows for the creation of possible CO2 sequestration scenarios that may be used as a training set. This study addresses several types of static and dynamic realistic and practical field-base data augmentation techniques ranging from spatial complexity, spatio-temporal complexity, and heterogeneity of reservoir characteristics. By incorporating domain-expertise-based feature generation, this framework honors precise representation of reservoir overcoming computational challenges associated with numerical reservoir tools. The developed ANN accurately replicated fluid flow behavior, resulting in significant computational savings compared to traditional numerical simulation models. The results showed that all the ML models achieved very good accuracies and high efficiency. The findings revealed that the quality of the path between the focal cell and injection wells emerged as the most crucial factor in both CO2 saturation and pressure estimation models. These insights significantly contribute to our understanding of CO2 plume monitoring, paving the way for breakthroughs in investigating reservoir behavior at a minimal computational cost. The study\u27s commitment to replicating numerical reservoir simulation results underscores the model\u27s potential to contribute valuable insights into the behavior and performance of CO2 sequestration systems, as a complimentary tool to numerical reservoir simulation when there is no measured data available from the field. The transformative nature of this research has vast implications for advancing carbon storage modeling technologies. By addressing the computational limitations of traditional numerical reservoir models and harnessing the synergy between machine learning and domain expertise, this work provides a practical workflow for efficient decision-making in sequestration projects