Gas field scheduling

Woodside Offshore Petroleum is the operator in the development of new gas fields in Australia's North West Shelf project. Sequencing the development of new gas fields in this project is a key determinant of its return on investment. This development sequence has constraints imposed by infrastructure and contractual obligations as well as natural features. The determination of an optimal or very good solution may involve a number of techniques from operations research. The study group attempted several approaches to the problem, principal amongst them being mathematical programming and dynamic programming. A few other heuristic approaches were also considered. The mathematical programming approach was able to yield solutions to small instances of the problem. The group was able to identify several avenues for further research and work on the problem is ongoing

Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End

In this work, we have attempted to solve two problems concerning the planning and scheduling of crude oil operations: first, on the upstream production planning of crude oil from offshore sources and second, on the scheduling of downstream processing of crude oil at the refinery front-end. The first part is on the offshore oilfield infrastructures planning under both exogenous uncertainty and endogeneous decision-dependent uncertainty. A model representative of the oilfield that is able to select the best routes to obtain the desired objective function is considered. The methodology used is by firstly developing a deterministic model andmodeling it with GAMS, followed by a stochastic one. The results obtained show a high accuracy representation in which the uncertainties in both the exogenous and endogeneous uncertainties in planning are accounted for. The stochastic model is a more thorough representation of the problem because it considers all the uncertainties along with the associated probabilities. Having validated the model formulation and solution obtained with results for standard problems reported in the literature, we believe that the model can be a tool to assist upper-level management in preliminary decision-making on an optimal plan for crude oil production from an offshore operation. The second part is onthe scheduling of crude oiloperations at a refinery front-end. A technique for obtaining globally optimal schedules for the flow of crude is developed. Acontinuous time model based on transfer events is used to represent the scheduling problem and this model is a nonconvex MINLP model which presents multiple local optima. We implement a branch-and-contract algorithm that aims at reducing the size of the search region. In order to obtain a global optimum solution of the problem, an outer-approximation algorithm is proposed, whereby lower and upper bounds on the global optimum are generated, which are converged to a specified tolerance. The solution obtained from the LB-MILP model, i.e., the decision variables (binary variables), was used to obtain a feasible solution for model UB-NLP. This solution is the upper bound solution. The application of the proposed algorithm shows significant reduction in the computational effort involved in solving the problem. Slack variables are introduced to overcome the integer infeasibility problem. The optimization model is developed using GAMS and an optimal solution is found with no logical constraints conflicts or error. The main contribution on this work in the first part is to conduct an extensive study onthe implementation ofthe model formulation in Iyer et al. (1998). As well, in the second part, we are focused on investigating effective implementation strategies of the model formulation and solution strategy in Karuppiah et al. (2008) using our choice of the modeling platform GAMS and the best numerical solvers that are available. Hence, most of the exposition on the model formulation and solution algorithms are taken directly from the original papers so as to provide the readers with the most accurate information possible. V

Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End

In this work, we have attempted to solve two problems concerning the planning and scheduling of crude oil operations: first, on the upstream production planning of crude oil from offshore sources and second, on the scheduling of downstream processing of crude oil at the refinery front-end. The first part is on the offshore oilfield infrastructures planning under both exogenous uncertainty and endogeneous decision-dependent uncertainty. A model representative of the oilfield that is able to select the best routes to obtain the desired objective function is considered. The methodology used is by firstly developing a deterministic model and modeling it with GAMS, followed by a stochastic one. The results obtained show a high accuracy representation in which the uncertainties in both the exogenous and endogeneous uncertainties in planning are accounted for. The stochastic model is a more thorough representation of the problem because it considers all the uncertainties along with the associated probabilities. Having validated the model formulation and solution obtained, we believe that the model can be a useful basic tool to assist upper-level management in deciding on an optimal plan for crude oil production from an offshore operation. The second part is on the scheduling of crude oil operations at a refinery front-end. A technique for obtaining globally optimal schedules for the flow of crude is developed. A continuous time model based on transfer events is used to represent the scheduling problem and this model is a nonconvex MINLP model which presents multiple local optima. We implement a branch-and-contract algorithm that aims at reducing the size of the search region. In order to obtain a global optimum solution of the problem, an outer-approximation algorithm is proposed, whereby lower and upper bounds on the global optimum are generated, which are converged to a specified tolerance. The solution obtained from the LB–MILP model, i.e., the decision variables (binary variables), was used to obtain a feasible solution for model UB–NLP. This solution is the upper bound solution. The application of the proposed algorithm shows significant reduction in the computational effort involved in solving the problem. Slack variables are introduced to overcome the integer infeasibility problem. The optimization model is developed using GAMS and an optimal solution is found with no logical constraints conflicts or error. The main contribution on this work in the first part is to conduct an extensive study on the implementation of the model formulation in Iyer et al. (1998). As well, in the second part, we are focused on investigating effective implementation strategies of the model formulation and solution strategy in Karuppiah et al. (2008) using our choice of the modeling platform GAMS and the best numerical solvers that are available. Hence, most of the exposition on the model formulation and solution algorithms are taken directly from the original papers so as to provide the readers with the most accurate information possible

State of the Art in the Optimisation of Wind Turbine Performance Using CFD

Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained