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

Cascading Effects of Fuel Network Interdiction

This thesis develops the Fuel Interdiction and Resulting Cascading Effects (FI&RCE) model. The study details the development and experimental testing of a framework for assessing the interdiction of a refined petroleum production and distribution network. FI&RCE uses a maximum flow mathematical programming formulation that models the transit of fuels from points of importation and refinement through a polyduct distribution network for delivery across a range of end user locations. The automated model accommodates networks of varying size and complexity. FI&RCE allows for parameters and factor settings that enable robust experimentation through implementation in MATLAB 2014 and the commercial solver CPLEX (Version 12.5). Experimental design allows the investigation of interdiction or disruption on supply and network infrastructure locations in order to support the strategic analytical needs of the user. Given a target set, FI&RCE provides measured responses for the resulting fuel availability and a valuation of economic loss. The value of economic loss feeds a Leontief based input-output model that assesses the cascading effects in the studied economy by implementing a mathematical program that optimizes the remaining industrial outputs. FI&RCE demonstrates a framework to investigate the military and cascading effects of a fuel interdiction campaign plan using a realistic case study

A MULTI-COMMODITY NETWORK FLOW APPROACH FOR SEQUENCING REFINED PRODUCTS IN PIPELINE SYSTEMS

In the oil industry, there is a special class of pipelines used for the transportation of refined products. The problem of sequencing the inputs to be pumped through this type of pipeline seeks to generate the optimal sequence of batches of products and their destination as well as the amount of product to be pumped such that the total operational cost of the system, or another operational objective, is optimized while satisfying the product demands according to the requirements set by the customers. This dissertation introduces a new modeling approach and proposes a solution methodology for this problem capable of dealing with the topology of all the scenarios reported in the literature so far. The system representation is based on a 1-0 multi commodity network flow formulation that models the dynamics of the system, including aspects such as conservation of product flow constraints at the depots, travel time of products from the refinery to their depot destination and what happens upstream and downstream the line whenever a product is being received at a given depot while another one is being injected into the line at the refinery. It is assumed that the products are already available at the refinery and their demand at each depot is deterministic and known beforehand. The model provides the sequence, the amounts, the destination and the trazability of the shipped batches of different products from their sources to their destinations during the entire horizon planning period while seeking the optimization of pumping and inventory holding costs satisfying the time window constraints. A survey for the available literature is presented. Given the problem structure, a decomposition based solution procedure is explored with the intention of exploiting the network structure using the network simplex method. A branch and bound algorithm that exploits the dynamics of the system assigning priorities for branching to a selected set of variables is proposed and its computational results for the solution, obtained via GAMS/CPLEX, of the formulation for random instances of the problem of different sizes are presented. Future research directions on this field are proposed

Utilizing Pipeline Quality and Facility Sustainability to Optimize Crude Oil Supply Chains

In this paper, the distribution center (DC) model shown in Shapiros Modeling the Supply Chain is modified to show optimal locations to place small and large refineries based on transportation distances, refinery building costs, and the costs associated with refinery sustainability and pipeline quality. Though this model was originally used to determine the optimal locations to place distribution centers based on transportation distances and the size of the distribution centers, this model was modified to allow the use of different costs associated with the quality condition of the pipeline and the costs of sustaining an environmentally friendly facility. The case used to prove the model is the Indonesian oil industry due to how an increase in efficiency and excess capacity could provide another viable country to supply oil to the United States. The outputs of this paper are efficiency frontiers that show how the costs of pipeline quality and facility sustainability affect the overall costs of the Indonesian oil industry and a model that can be used to evaluate the oil industries in other countries

The Uinta Express pipeline: a comprehensive research report conducted by students enrolled in CvEEN 3100 technical communications

reportThe Uinta Express Pipeline is a proposed common carrier pipeline which would transport waxy crude oil extracted from the Uinta Basin in northeastern Utah to area refineries in North Salt Lake City. The proposed project would consist of a 12-inch, buried, insulated, carbon steel pipeline supported by numerous ancillary facilities along its approximately 135-mile long route. Tesoro Refining and Marketing LLC, the principle organization sponsoring research and development of the Uinta Express Pipeline, claims that once operational it will have the capacity to transport up to 60,000 barrels of unrefined waxy crude oil daily, thereby removing an estimated 250 semi tanker trucks from Utah's highways each day. This Report, compiled by University of Utah students enrolled in CvEEN 3100: Technical Communications, thoroughly interrogates the proposed pipeline with current research and specification data. Students enrolled in CvEEN 3100 during the Fall 2014 semester identified various aspects of the proposed project that presented the most significant challenges from a civil and environmental perspective. Students worked in teams to compile feasibility reports, which comprise the individual chapters. Teams coordinated with one another to ensure that research content, images, and technical data discussed in one chapter did not overlap with material in other chapters

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

Global optimisation of large-scale quadratic programs: application to short-term planning of industrial refinery-petrochemical complexes

This thesis is driven by an industrial problem arising in the short-term planning of an integrated refinery-petrochemical complex (IRPC) in Colombia. The IRPC of interest is composed of 60 industrial plants and a tank farm for crude mixing and fuel blending consisting of 30 additional units. It considers both domestic and imported crude oil supply, as well as refined product imports such as low sulphur diesel and alkylate. This gives rise to a large-scale mixed-integer quadratically constrained quadratic program (MIQCQP) comprising about 7,000 equality constraints with over 35,000 bilinear terms and 280 binary variables describing operating modes for the process units. Four realistic planning scenarios are recreated to study the performance of the algorithms developed through the thesis and compare them to commercial solvers. Local solvers such as SBB and DICOPT cannot reliably solve such large-scale MIQCQPs. Usually, it is challenging to even reach a feasible solution with these solvers, and a heuristic procedure is required to initialize the search. On the other hand, global solvers such as ANTIGONE and BARON determine a feasible solution for all the scenarios analysed, but they are unable to close the relaxation gap to less than 40% on average after 10h of CPU runtime. Overall, this industrial-size problem is thus intractable to global optimality in a monolithic way. The first main contribution of the thesis is a deterministic global optimisation algorithm based on cluster decomposition (CL) that divides the network into groups of process units according to their functionality. The algorithm runs through the sequences of clusters and proceeds by alternating between: (i) the (global) solution of a mixed-integer linear program (MILP), obtained by relaxing the bilinear terms based on their piecewise McCormick envelopes and a dynamic partition of their variable ranges, in order to determine an upper bound on the maximal profit; and (ii) the local solution of a quadratically-constrained quadratic program (QCQP), after fixing the binary variables and initializing the continuous variables to the relaxed MILP solution point, in order to determine a feasible solution (lower bound on the maximal profit). Applied to the base case scenario, the CL approach reaches a best solution of 2.964 MMUSD/day and a relaxation gap of 7.5%, a remarkable result for such challenging MIQCQP problem. The CL approach also vastly outperforms both ANTIGONE (2.634 MMUSD/day, 32% optimality gap) and BARON (2.687 MMUSD/day, 40% optimality gap). The second main contribution is a spatial Lagrangean decomposition, which entails decomposing the IRPC short-term planning problem into a collection of smaller subproblems that can be solved independently to determine an upper bound on the maximal profit. One advantage of this strategy is that each sub-problem can be solved to global optimality, potentially providing good initial points for the monolithic problem itself. It furthermore creates a virtual market for trading crude blends and intermediate refined–petrochemical streams and seeks an optimal trade-off in such a market, with the Lagrange multipliers acting as transfer prices. A decomposition over two to four is considered, which matches the crude management, refinery, petrochemical operations, and fuel blending sections of the IRPC. An optimality gap below 4% is achieved in all four scenarios considered, which is a significant improvement over the cluster decomposition algorithm.Open Acces

Advanced and novel modeling techniques for simulation, optimization and monitoring chemical engineering tasks with refinery and petrochemical unit applications

Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making. Advancements in computational ability have improved modeling software’s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulation’s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models. Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed. Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modeling’s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit