9 research outputs found

    Design optimization of oilfield subsea infrastructures with manifold placement and pipeline layout

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    This work presents a practical and effective optimization method to design subsea production networks, which accounts for the number of manifolds and platforms, their location, well assignment to these gathering systems, and pipeline diameter. It brings a fast solution that can be easily implemented as a tool for layout design optimization and simulation-based analysis. The proposed model comprises reservoir dynamics and multiphase flow, relying on multidimensional piecewise linearization to formulate the layout design problem as a MILP. Besides design validation, reservoir simulation serves the purpose of defining boundaries for optimization variables and parameters that characterize pressure decrease, reservoir dynamics and well production over time. Pressure drop in pipelines are modeled by piecewise-linear functions that approximate multiphase flow simulators. The resulting optimization model and approximation methodology were applied to a real oilfield with the aim of assessing their effectiveness.Este trabalho apresenta um método de otimização prático e eficaz para o projeto de redes de produção submarinas em campos de petróleo offshore, o que compreende o número de coletores, sejam manifolds ou plataformas, sua localização, atribuição de poços a esses coletores e diâmetro de dutos que interligam todos os elementos da rede. Ele traz uma solução rápida que pode ser facilmente implementada como uma ferramenta para otimização de layout e de estudos baseados em simulação. O modelo proposto compreende a dinâmica do reservatório e fluxo multifásico em dutos, baseando-se na linearização multidimensional por partes para formular o problema de otimização de layout como programação inteira linear mista. Além da validação da solução ótima obtida pelo método, a simulação de reservatórios define limites para as variáveis e parâmetros do modelo que caracterizam a perda de carga, a dinâmica do reservatório e a produção de óleo dos poços ao longo do tempo. A perda de carga nas tubulações é modelada por funções lineares por partes que aproximam resultados obtidos pelos simuladores de fluxo multifásicos. O modelo de otimização foi aplicado a um verdadeiro campo de petróleo offshore com o objetivo de avaliar sua efetividade

    Eduardo Camponogara

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    Multiphase flow modelling for enhanced oil and gas drilling and production

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    From the exploration to the abandonment of an oil and gas discovery, operators and engineers are constantly faced with the challenge of achieving the best commercial potential of oil fields. Although the petroleum engineering community has significantly contributed towards maximising the potential of discovered prospects, the approach adopted so far has been compartmentalised with little (heuristics-based) or no quality integration. The highly interconnected nature of the decision factors affecting the management of any field requires increased implementation of Computer-Aided Process Engineering (CAPE) methods, thus presenting a task for which chemical engineers have the background to make useful contributions. Drilling and production are the two primary challenging operations of oilfield activities, which span through different time horizons with both fast and slow-paced dynamics. These attributes of these systems make the application of modelling, simulation, and optimisation tasks difficult. This PhD project aims to improve field planning and development decisions from a Process Systems Engineering (PSE) perspective via numerical (fluid dynamics) simulations and modelbased deterministic optimisation of drilling and production operations, respectively. Also demonstrated in this work is the importance of deterministic optimisation as a reliable alternative to classical heuristic methods. From a drilling operation perspective, this project focuses on the application of Computational Fluid Dynamics (CFD) as a tool to understand the intricacies of cuttings transport (during wellbore cleaning) with drilling fluids of non-Newtonian rheology. Simulations of two-phase solid-liquid flows in an annular domain are carried out, with a detailed analysis on the impact of several drilling parameters (drill pipe eccentricity, inclination angle, drill pipe rotation, bit penetration rate, fluid rheology, and particle properties) on the cuttings concentration, pressure drop profiles, axial fluid, and solid velocities. The influence of the flow regime (laminar and turbulent) on cuttings transport efficiency is also examined using the Eulerian-Eulerian and Lagrangian-Eulerian modelling methods. With experimentally validated simulations, this aspect of the PhD project provides new understanding on the interdependence of these parameters; thus facilitating industrial wellbore cleaning operations. The second part of this project applies mathematical optimisation techniques via reduced-order modelling strategies for the enhancement of petroleum recovery under complex constraints that characterise production operations. The motivation for this aspect of the project stems from the observation that previous PSE-based contributions aimed at enhancing field profitability, often apply over-simplifications of the actual process or neglect some key performance indices due to problem complexity. However, this project focuses on a more detailed computational integration and optimisation of the models describing the whole field development process from the reservoir to the surface facilities to ensure optimal field operations. Nonlinear Programs (NLPs), Mixed-Integer Linear Programs (MILPs), and Mixed-Integer Nonlinear Programs (MINLPs) are formulated for this purpose and solved using high-fidelity simulators and algorithms in open-source and commercial solvers. Compared to previous studies, more flow physics are incorporated and rapid computations obtained, thus enabling real-time decision support for enhanced production in the oil and gas industry

    Ensuring the Safe Production of Natural Gas

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    Growing demand for natural gas in the U.S. has led to an increase in hydraulic fracturing in the Marcellus Shale region of PA. The goal of this project was to recommend best practices to the U.S. Department of Energy for hydraulic fracturing. First, industry practices for well drilling, cementing, and casing were analyzed. A System-Theoretic Process Analysis was used to identify weaknesses that could lead to loss of wellbore integrity; and a blowout preventer system was designed to mitigate this hazard. Second, contaminants in hydraulic fracturing fluids were identified and a mobile onsite wastewater treatment system using reverse osmosis was designed to remove fracturing chemicals, radium, and solids. Lastly, recommendations were made to improve the safety of natural gas recovery

    Immiscible displacement of trapped oil through experimental and data mining techniques.

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    Extensive experimental and data mining techniques have been applied to investigate the potential and competitiveness of gases used in immiscible gas-enhanced oil recovery (EOR) processes. Methane (CH4), Nitrogen (N2), Air (21%O2/N2) and Carbon Dioxide (CO2) are some of the gases injected in reservoirs to displace trapped oil from reservoir pores. The EOR screening process has been well-documented in the literature. However, for immiscible gas EOR technology, very few resources are available for evaluating the selection and performance criteria for commonly-injected EOR gases; immiscible EOR gases are usually lumped up in published screening models, and the gases are reportedly selected based on availability and accessibility, rather than on technical criteria such as displacement efficiency. Furthermore, available experimental studies have investigated EOR gases only separately. This research has been able to fill these gaps and more, through rigorous data mining and gas experiments processes. The methodology utilised empirical approaches set in three phases. Phase I applied data mining techniques to 10,850 data from 484 EOR field projects, to identify twenty-four EOR geological and engineering quantities, and objective functions. Phase II utilised Phase I outcomes to design and execute a set of rigorous gas experiments, involving 1,920 experimental runs (comprising five reservoir analogous core samples, eight gases, eight isobars and six isotherms), to generate and analyse 15,360 experimental data points. Several established and modified constitutive equations were used to model gas responses to EOR geological and engineering quantities. In Phase III, Phase I and Phase II results were coupled for the purpose of knowledge validation and application. This research's outcomes have contributed to reservoir engineering practice and knowledge in providing useful information on EOR gases' competitiveness. Results from Phase I indicate that immiscible gas EOR can be unbundled through data mining and clustering techniques. A novel screening model has been developed for immiscible gas EOR that incorporates sensitivity and criticality markers for each petrophysical quantity investigated. It has been demonstrated in Phase II that, in a heterogeneous system, CH4 is the most competitive gas for ten geological and engineering quantities and objective functions, such as Volumetric Rate, Interstitial Velocity, and Well Density. Similarly, CO2 is most competitive for ten other quantities investigated, such as Mobility and Interstitial Momentum. N2 is the most competitive for the cost of injected gas per area coverage. Air is second-best for several objective functions. Suffice to state that at some structural settings and operational conditions (such as porosity, pore size, surface area and temperature), the competitiveness ranking of the gases switches position. Such was observed between N2 and CO2 in low porosity (4% and 3%) core samples. EOR gas mixtures and non EOR gases - such as 20% CH4/N2, He, and Ar - were added to the experiments to investigate the relationship between gas flow and gas properties. It was observed that the structural variability (heterogeneity) of the system distorts the correlation between gas properties, such as molecular weight, and the performance criteria of the respective gases. The results from Phase I and II couple significantly in Phase III. Based on well number and placement, it has been demonstrated that the well placement of CH4, CO2, and Air favours a negative pore size gradient, while N2 favours a positive gradient. The economic analysis demonstrates that CO2 incurs the least cumulative injectant cost and the highest capital expenditure cost (CAPAX). The three Phases validate the field and laboratory well density profile. CH4 requires the least well density (0.2 well/acre, 1.0 well/cm2) compared to CO2 (0.7 well/acre, 2.0 well/cm2). In some analyses, it was discovered that gas mixture, such as 20%CH4/N2, performs better than when the individual component gas acted alone. Single-phase and two-phase relationships have been analytically and experimentally coupled. The experimental findings at low pressure could also lend utility to the gas separation, fluidised bed, and catalytic reaction processes and industry

    Data-driven prognostics for critical electronic assemblies and electromechanical components

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    The industrial digitalisation enables the adoption of robust, data-driven maintenance strategies that increase safety and reliability of critical assets such as electronics. And yet, an implementation of data-driven methods which primarily address the industrialisation of diagnostic and prognostic strategies is opposed by various, application specific challenges. This thesis collates such restricting factors encountered within the oil and gas industry, in particular for the critical electrical systems and components in upstream deep drilling tools. A fleet-level, tuned machine learning approach is presented that classifies the operational state (no-failure/ failure) of downhole tool printed circuit board assemblies. It supports maintenance decision making under varying levels of failure costs and fleet reliability scenarios. Applied within a maintenance scheme it has the potential to minimise non-productive time while increasing operational reliability. Likewise, a tailored and efficient deep learning data pipeline is proposed for a component-level forecast of the end of life of electromagnetic relays. It is evaluated using high resolution life-cycle data which has been collected as a part of this thesis. In combination with a failure analysis, the proposed method improves the prognostics capabilities compared to traditional methods which have been proposed so far in order to assess the operational health of electromagnetic relays. Two case studies underpin the need for tailored prognostic methods in order to provide viable solutions that can de-risk deep drilling operations. In consequence, the proposed approaches alleviate the pressure on current maintenance strategies which can no longer meet the stringent reliability requirements of upstream assets

    ECOS 2012

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    The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology

    Best Available Techniques (BAT) Reference Document for the Management of Waste from Extractive Industries in accordance with Directive 2006/21/EC

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    This document, Best Available Techniques Reference Document for the Management of Waste from Extractive Industries, in accordance with Directive 2006/21/EC, abbreviated as MWEI BREF, is a review of the Reference Document for Management of Tailings and Waste-Rock in Mining Activities (MTWR BREF). This review is the result of an exchange of information between experts from EU Member States, industries concerned, non-governmental organisations promoting environmental protection and the European Commission. The reviewed document presents up-dated data and information on the management of waste from extractive industries, including information on BAT, associated monitoring, and developments in them. It is published by the European Commission pursuant Article 21(3) of Directive 2006/21/EC on the management of waste from extractive industries. This document presents data and information on the following: - General information and key figures on extractive industries in Europe, extractive waste generation, extractive waste facilities and key environmental issues (Chapter 1). - Applied processes and techniques for the management of extractive waste (Chapter 2). - Emission and consumption levels resulting from the management of extractive waste (Chapter 3). - Techniques to consider in the determination of Best Available Techniques (Chapter 4). This includes generic management and waste hierarchy techniques, risk-specific techniques to ensure safety, techniques for the prevention or minimisation of water status deterioration, techniques for the prevention or minimisation of air and soil pollution and other risk-specific techniques. - Best available techniques conclusions (Chapter 5). - Emerging techniques (Chapter 6). This includes techniques that were reported at different levels of technology readiness. - Remarks and recommendations for future work (Chapter 7).JRC.B.5-Circular Economy and Industrial Leadershi
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