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

Optimization of crude oil operations scheduling by applying a two-stage stochastic programming approach with risk management

Producción CientíficaThis paper focuses on the problem of crude oil operations scheduling carried out in a system composed of a refinery and a marine terminal, considering uncertainty in the arrival date of the ships that supply the crudes. To tackle this problem, we develop a two-stage stochastic mixed-integer nonlinear programming (MINLP) model based on continuous-time representation. Furthermore, we extend the proposed model to include risk management by considering the Conditional Value-at-Risk (CVaR) measure as the objective function, and we analyze the solutions obtained for different risk levels. Finally, to evaluate the solution obtained, we calculate the Expected Value of Perfect Information (EVPI) and the Value of the Stochastic Solution (VSS) to assess whether two-stage stochastic programming model offers any advantage over simpler deterministic approaches.Gobierno de España - proyects a-CIDiT (PID2021-123654OB-C31) and InCo4In (PGC 2018-099312-B-C31)Junta de Castilla y León - EU-FEDER (CLU 2017-09, CL-EI-2021-07, UIC 233

Optimization of refinery preheat trains undergoing fouling: control, cleaning scheduling, retrofit and their integration

Crude refining is one of the most energy intensive industrial operations. The large amounts of crude processed, various sources of inefficiencies and tight profit margins promote improving energy recovery. The preheat train, a large heat exchanger network, partially recovers the energy of distillation products to heat the crude, but it suffers of the deposition of material over time – fouling – deteriorating its performance. This increases the operating cost, fuel consumption, carbon emissions and may reduce the production rate of the refinery. Fouling mitigation in the preheat train is essential for a profitable long term operation of the refinery. It aims to increase energy savings, and to reduce operating costs and carbon emissions. Current alternatives to mitigate fouling are based on heuristic approaches that oversimplify the representation of the phenomena and ignore many important interactions in the system, hence they fail to fully achieve the potential energy savings. On the other hand, predictive first principle models and mathematical programming offer a comprehensive way to mitigate fouling and optimize the performance of preheat trains overcoming previous limitations. In this thesis, a novel modelling and optimization framework for heat exchanger networks under fouling is proposed, and it is based on fundamental principles. The models developed were validated against plant data and other benchmark models, and they can predict with confidence the main effect of operating variables on the hydraulic and thermal performance of the exchangers and those of the network. The optimization of the preheat train, an MINLP problem, aims to minimize the operating cost by: 1) dynamic flow distribution control, 2) cleaning scheduling and 3) network retrofit. The framework developed allows considering these decisions individually or simultaneously, although it is demonstrated that an integrated approach exploits the synergies among decision levels and can reduce further the operating cost. An efficient formulation of the model disjunctions and time representation are developed for this optimization problem, as well as efficient solution strategies. To handle the combinatorial nature of the problem and the many binary decisions, a reformulation using complementarity constraints is proposed. Various realistic case studies are used to demonstrate the general applicability and benefits of the modelling and optimization framework. This is the first time that first principle predictive models are used to optimize various types of decisions simultaneously in industrial size heat exchanger networks. The optimization framework developed is taken further to an online application in a feedback loop. A multi-loop NMPC approach is designed to optimize the flow distribution and cleaning scheduling of preheat trains over two different time scales. Within this approach, dynamic parameter estimation problems are solved at frequent intervals to update the model parameters and cope with variability and uncertainty, while predictive first principle models are used to optimize the performance of the network over a future horizon. Applying this multi-loop optimization approach to a case study of a real refinery demonstrates the importance of considering process variability on deciding about optimal fouling mitigation approaches. Uncertainty and variability have been ignored in all previous model based fouling mitigation strategies, and this novel multi-loop NMPC approach offers a solution to it so that the economic savings are enhanced. In conclusion, the models and optimization algorithms developed in this thesis have the potential to reduce the operating cost and carbon emission of refining operations by mitigating fouling. They are based on accurate models and deterministic optimization that overcome the limitations of previous applications such as poor predictability, ignoring variability and dynamics, ignoring interactions in the system, and using inappropriate tools for decision making.Open Acces

A descriptive analysis of value creation at Statoil Mongstad and its supply chain

Value chain is a sequence of activities that flow from raw materials to delivery of product or service. Value chain in oil industry extends from exploration and production of crude oil and natural gas up to sales of refined products. Refineries play a key important in the supply chain of an oil company, as it is where crude oil is processed into refined products. The emphasis of this work is on Statoil Mongstad. Statoil Mongstad is a refinery located at Mongstad. In order to get overview of Statoil Mongstad’s value chain, this thesis describes and discusses Statoil Mongstad’s organisation structure, production processes, costing and pricing principles and policies, and finally its supply chain

A study in the financial valuation of a topping oil refinery

Oil refineries underpin modern day economics, finance and engineering – without their refined products the world would stand still, as vehicles would not have petrol, planes grounded without kerosene and homes not heated, without heating oil. In this thesis I study the refinery as a financial asset; it is not too dissimilar to a chemical plant, in this respect. There are a number of reasons for this research; over recent years there have been legal disputes based on a refiner's value, investors and entrepreneurs are interested in purchasing refineries, and finally the research in this arena is sparse. In this thesis I utilise knowledge and techniques within finance, optimisation, stochastic mathematics and commodities to build programs that obtain a financial value for an oil refinery. In chapter one I introduce the background of crude oil and the significance of the refinery in the oil value chain. In chapter two I construct a traditional discounted cash flow valuation often applied within practical finance. In chapter three I program an extensive piecewise non linear optimisation solution on the entire state space, leveraging off a simulation of the refined products using a set of single factor Schwartz (1997) stochastic equations often applied to commodities. In chapter four I program an optimisation using an approximation on crack spread option data with the aim of lowering the duration of solution found in chapter three; this is achieved by utilising a two-factor Hull & White sub-trinomial tree based numerical scheme; see Hull & White (1994) articles I & II for a thorough description. I obtain realistic and accurate numbers for a topping oil refinery using financial market contracts and other real data for the Vadinar refinery based in Gujurat India

Analysis of Disruptions in the Gulf of Mexico Oil and Gas Industry Supply Chain and Related Economic Impacts

Catastrophic events are human and economic tragedies in collaboration. Oil spills have enormous impacts on the local economy of the area and for the local labor markets. The Deepwater Horizon oil spill was caused by an explosion on semisubmersible drilling rig (Macondo) on April 20, 2010. Another regional disaster, Hurricane Katrina as it ripped over the core of the Gulf of Mexico producing zone, one of the most important oil and gas production region. With Geological complexities, continued of drilling and production in GoM increases the risk of having leak/spill. Therefore, the Econometrics methods, and Modeling to forecast impacts of potential disasters are utilized and conduct optimization modeling to capture key components for building reasonable supply chain models of actual situations for petroleum industry in order to make the best possible choices consequences of disaster in this dissertation,. The dynamic response of a different of industrial sectors in Louisiana to oil and gas disasters is considered. The likely magnitude of the net economic impact of a major oil spill (Macondo) will be determined in terms of jobs and wages with Vector Autoregressive method. Forecast the potential impacts of future changes in employment after disaster on economy will be studied. In the second part, the offsetting economic injection due to BP expenditures in the economy, will estimate by economic impact analysis method, which is Input-output models. Then the gross economic damage, which is created by BP oil spill will be calculated. The final results provide beneficial knowledge on determining the potential economic impact of future large-scale catastrophes and helpful for companies to react better to the economic impact of events. At the end, a mathematical framework will be presented for optimal network design of oil and gas supply chain with application for Louisiana Offshore Oil Port (LOOP); due to determine the optimal oil flow through the mid-stream/ downstream networks and its profit even if it is experiencing natural/ man-made damages. The outcome of this work is a new distributed decision support framework which is intended to help optimize the profit for critical energy zone and to boost economy under unpredictable situations

Discourse and sociotechnical transformation: the emergence of refinery information systems

This thesis considers the emergence and diffusion of British Petroleum's (BP) Refinery Information Systems (RIS). Insights from the associology of translation are coupled with the Foucauldian concepts of discourse and power /knowledge in order to analyse accounts of the system provided by organisational participants. The analysis suggests that a new form of managerialism, or "new commercial agenda" is being selectively deployed both within BP and within the wider commercial world. This transformed managerialism seeks to maintain control and heighten commercialism through a re- working of hierarchical relations within the organisation. Artefacts and practices of organisational life are revealed as prime vehicles for instantiating this new agenda and BP's Refinery Information Systems are thus seen to be both a condition and a consequence of the changes underway