Decision-Making for Well Placement Optimization in Oil Field Development

Well placement is a method to improve oil recovery by drilling new infill wells in a reservoir. Drilling new wells is a critical yet very challenging task in field development, because the optimal well locations are rarely known and difficult to decide in practice due to complex reservoir and depletion situations. This dissertation focuses on the development of mathematical optimization techniques to assist decision-making for well planning and placement. The following topics are included in this dissertation. 1. To study and develop two stochastic approximated gradient-based approaches: the ensemble based optimization method (EnOpt) and the fixed-gain simultaneous perturbation stochastic approximation (FSP) for well placement; Evaluate the performance and effectiveness of these two methods on case studies. 2. To develop an efficient method to decide optimal number of wells and the corresponding locations, evaluate the performance on study cases. 3. To handle geological uncertainty and decision-making risk, propose a new workflow for multi-objective well placement optimization. 4. To ensure an efficient decision-making and a fast turnaround time, the use of engineering prior knowledge and a few acceleration routines are discussed in the context of optimization. All approaches are evaluated on synthetic reservoir models, some are performed on real field-like cases. This dissertation provides various optimization methods with an enhanced capability of addressing geological uncertainty for well placement in oilfield development. However, it should also be noted that while the techniques proposed in this dissertation are applicable to a diverse set of reservoirs with no known limitations, the additional value of this dissertation lies in its ability to address well placement needs for highly complex reservoirs. For reservoirs that lack the complexity seen, for example, in deepwater basins, conventional well placement methods may be sufficient

An Evolutionary Approach to Multistage Portfolio Optimization

Portfolio optimization is an important problem in quantitative finance due to its application in asset management and corporate financial decision making. This involves quantitatively selecting the optimal portfolio for an investor given their asset return distribution assumptions, investment objectives and constraints. Analytical portfolio optimization methods suffer from limitations in terms of the problem specification and modelling assumptions that can be used. Therefore, a heuristic approach is taken where Monte Carlo simulations generate the investment scenarios and' a problem specific evolutionary algorithm is used to find the optimal portfolio asset allocations. Asset allocation is known to be the most important determinant of a portfolio's investment performance and also affects its risk/return characteristics. The inclusion of equity options in an equity portfolio should enable an investor to improve their efficient frontier due to options having a nonlinear payoff. Therefore, a research area of significant importance to equity investors, in which little research has been carried out, is the optimal asset allocation in equity options for an equity investor. A purpose of my thesis is to carry out an original analysis of the impact of allowing the purchase of put options and/or sale of call options for an equity investor. An investigation is also carried out into the effect ofchanging the investor's risk measure on the optimal asset allocation. A dynamic investment strategy obtained through multistage portfolio optimization has the potential to result in a superior investment strategy to that obtained from a single period portfolio optimization. Therefore, a novel analysis of the degree of the benefits of a dynamic investment strategy for an equity portfolio is performed. In particular, the ability of a dynamic investment strategy to mimic the effects ofthe inclusion ofequity options in an equity portfolio is investigated. The portfolio optimization problem is solved using evolutionary algorithms, due to their ability incorporate methods from a wide range of heuristic algorithms. Initially, it is shown how the problem specific parts ofmy evolutionary algorithm have been designed to solve my original portfolio optimization problem. Due to developments in evolutionary algorithms and the variety of design structures possible, a purpose of my thesis is to investigate the suitability of alternative algorithm design structures. A comparison is made of the performance of two existing algorithms, firstly the single objective stepping stone island model, where each island represents a different risk aversion parameter, and secondly the multi-objective Non-Dominated Sorting Genetic Algorithm2. Innovative hybrids of these algorithms which also incorporate features from multi-objective evolutionary algorithms, multiple population models and local search heuristics are then proposed. . A novel way is developed for solving the portfolio optimization by dividing my problem solution into two parts and then applying a multi-objective cooperative coevolution evolutionary algorithm. The first solution part consists of the asset allocation weights within the equity portfolio while the second solution part consists 'ofthe asset allocation weights within the equity options and the asset allocation weights between the different asset classes. An original portfolio optimization multiobjective evolutionary algorithm that uses an island model to represent different risk measures is also proposed.Imperial Users onl

Simulation optimization: A comprehensive review on theory and applications

For several decades, simulation has been used as a descriptive tool by the operations research community in the modeling and analysis of a wide variety of complex real systems. With recent developments in simulation optimization and advances in computing technology, it now becomes feasible to use simulation as a prescriptive tool in decision support systems. In this paper, we present a comprehensive survey on techniques for simulation optimization with emphasis given on recent developments. We classify the existing techniques according to problem characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). We discuss the major advantages and possible drawbacks of the different techniques. A comprehensive bibliography and future research directions are also provided in the paper. © "IIE"

Consumer load modeling and fair mechanisms in the efficient transactive energy market

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringSanjoy DasTwo significant and closely related issues pertaining to the grid-constrained transactive distribution system market are investigated in this research. At first, the problem of spatial fairness in the allocation of energy among energy consumers is addressed, where consumer agents that are located at large distances from the substation – in terms of grid layout, are charged at higher rates than those close to it. This phenomenon, arising from the grid’s voltage and flow limits is aggravated during demand peaks. Using the Jain’s index to quantify fairness, two auction mechanisms are proposed. Both approaches are bilevel, with aggregators acting as interface agents between the consumers and the upstream distribution system operator (DSO). Furthermore, in spite of maximizing social welfare, neither mechanism makes use of the agents’ utility functions. The first mechanism is cost-setting, with the DSO determining unit costs. It implements the Jain’s index as a second term to the social welfare. Next, a power setting auction mechanism is put forth where the DSO’s role is to allocate energy in response to market equilibrium unit costs established at each aggregator from an iterative bidding process among its consumers. The Augmented Lagrangian Multigradient Approach (ALMA), which is based on vector gradient descent, is proposed in this research for implementation at the upper level. The mechanism’s lower level comprises of multiple auctions realized by the aggregators. The quasi-concavity of the Jain’s index is theoretically established, and it has been shown that ALMA converges to the Pareto front representing tradeoffs between social welfare and fairness. The effectiveness of both mechanisms is established through simulations carried out using a modified IEEE 37-bus system platform. The issue of extracting patterns of energy usage from time series energy use profiles of individual consumers is the focus of the second phase of this research. Two novel approaches for non-intrusive load disaggregation based on non-negative matrix factorization (NMF), are proposed. Both algorithms distinguish between fixed and shiftable load classes, with the latter being characterized by binary OFF and ON cycles. Fixed loads are represented as linear combinations of a set of basis vectors that are learned by NMF. One approach imposes L0 normed constraints on each shiftable load using a new method called binary load decomposition. The other approach models shiftable loads as Gaussian mixture models (GMM), therefore using expectation-maximization for unsupervised learning. This hybrid NMF-GMM algorithm enjoys the theoretical advantage of being interpretable as a maximum-likelihood procedure within a probabilistic framework. Numerical studies with real load profiles demonstrate that both algorithms can effectively disaggregate total loads into energy used by individual appliances. Using disaggregated loads, a maximum-margin regression approach to derive more elaborate, temperature-dependent utility functions of the consumers, is proposed. The research concludes by identifying the various ways gleaning such information can lead to more effective auction mechanisms for multi-period operation

Simulated Annealing

The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine

International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

Efficient Optimization and Robust Value Quantification of Enhanced Oil Recovery Strategies

With an increasing demand for hydrocarbon reservoir produces such as oil, etc., and difficulties in finding green oil fields, the use of Enhanced Oil Recovery (EOR) methods such as polymer, Smart water, and solvent flooding for further development of existing fields can not be overemphasized. For reservoir profitability and reduced environmental impact, it is crucial to consider appropriate well control settings of EOR methods for given reservoir characterization. Moreover, finding appropriate well settings requires solving a constrained optimization problem with suitable numerical solution methods. Conventionally, the solution method requires many iterations involving several computationally demanding function evaluations before convergence to the appropriate near optimum. The major subject of this thesis is to develop an efficient and accurate solution method for constrained optimization problems associated with EOR methods for their value quantifications and ranking in the face of reservoir uncertainties. The first contribution of the thesis develops a solution method based on the inexact line search method (with Ensemble Based Optimization (EnOpt) for approximate gradient computation) for robust constrained optimization problems associated with polymer, Smart water, and solvent flooding. Here, the objective function is the expectation of the Net Present Value (NPV) function over given geological realizations. For a given set of well settings, the NPV function is defined based on the EOR simulation model, which follows from an appropriate extension of the black-oil model. The developed solution method is used to find the economic benefits and also the ranking of EOR methods for different oil reservoirs developed to mimic North Sea reservoirs. Performing the entire optimization routine in a transformed domain along with truncations has been a common practice for handling simple linear constraints in reservoir optimization. Aside from the fact that this method has a negative impact on the quality of gradient computation, it is complicated to use for non-linear constraints. The second contribution of this thesis proposes a technique based on the exterior penalty method for handling general linear and non-linear constraints in reservoir optimization problems to improve gradient computation quality by the EnOpt method for efficient and improved optimization algorithm. Because of the computationally expensive NPV function due to the costly reservoir simulation of EOR methods, the solution method for the underlying EOR optimization problem becomes inefficient, especially for large reservoir problems. To speedup the overall computation of the solution method, this thesis introduces a novel full order model (FOM)-based certified adaptive machine learning optimization procedures to locally approximate the expensive NPV function. A supervised feedforward deep neural network (DNN) algorithm is employed to locally create surrogate model. In the FOM-based optimization algorithm of this study, several FOM NPV function evaluations are required by the EnOpt method to approximate the gradient function at each (outer) iteration until convergence. To limit the number FOM-based evaluations, we consider building surrogate models locally to replace the FOM based NPV function at each outer iteration and proceed with an inner optimization routine until convergence. We adapt the surrogate model using some FOM-based criterion where necessary until convergence. The demonstration of methodology for polymer optimization problem on a benchmark model results in an improved optimum and found to be more efficient compared to using the full order model optimization procedures