Robust Optimization for Sequential Field Development Planning

To achieve high profitability from an oil field, optimizing the field development strategy (e.g., well type, well placement, drilling schedule) before committing to a decision is critically important. The profitability at a given control setting is predicted by running a reservoir simulation model, while determining a robust optimal strategy generally requires many expensive simulations. In this work, we focus on developing practical and efficient methodologies to solving reservoir optimization problems for which the actions that can be controlled are discrete and sequential (e.g., drilling sequence of wells). The type of optimization problems I address must take into account both geological uncertainty and the reduction in uncertainty resulting from observations. As the actions are discrete and sequential, the process can be characterized as sequential decision- making under uncertainty, where past decisions may affect both the possibility of the future choices of actions and the possibility of future uncertainty reduction. This thesis tackles the challenges in sequential optimization by considering three main issues: 1) optimizing discrete-control variables, 2) dealing with geological uncertainty in robust optimization, and 3) accounting for future learning when making optimal decisions. As the first contribution of this work, we develop a practical online-learning method- ology derived from A* search for solving reservoir optimization problems with discrete sets of actions. Sequential decision making can be formulated as finding the path with the maximum reward in a decision tree. To efficiently compute an optimal or near- optimal path, heuristics from relaxed problems are first used to estimate the maximum value constrained to past decisions, and then online-learning techniques are applied to improve the estimation accuracy by learning the errors of the initial approximations ob- tained from previous decision steps. In this way, an accurate estimate of the maximized value can be inexpensively obtained, thereby guiding the search toward the optimal so- lution efficiently. This approach allows for optimization of either a complete strategy with all available actions taken sequentially or only the first few actions at a reduced cost by limiting the search depth. The second contribution is related to robust optimization when an ensemble of reservoir models is used to characterize geological uncertainty. Instead of computing the expectation of an objective function using ensemble-based average value, we develop various bias-correction methods applied to the reservoir mean model to estimate the expected value efficiently without sacrificing accuracy. The key point of this approach is that the bias between the objective-function value obtained from the mean model and the average objective-function value over an ensemble can be corrected by only using information from distinct controls and model realizations. During the optimization process, we only require simulations of the mean model to estimate the expected value using the bias-corrected mean model. This methodology can significantly improve the efficiency of robust optimization and allows for fairly general optimization methods. In the last contribution of this thesis, we address the problem of making optimal decisions while considering the possibility of learning through future actions, i.e., op- portunities to improve the optimal strategy resulting from future uncertainty reduction. To efficiently account for the impact of future information on optimal decisions, we sim- plify the value of information analysis through key information that would help make better future decisions and the key actions that would result in obtaining that informa- tion. In other words, we focus on the use of key observations to reduce the uncertainty in key reservoir features for optimization problems, rather than using all observations to reduce all uncertainties. Moreover, by using supervised-learning algorithms, we can identify the optimal observation subset for key uncertainty reduction automatically and evaluate the information’s reliability simultaneously. This allows direct computation of the posterior probability distribution of key uncertainty based on Bayes’ rule, avoiding the necessity of expensive data assimilation algorithms to update the entire reservoir modeDoktorgradsavhandlin

A Behavioral and Neural Evaluation of Prospective Decision-Making under Risk

Making the best choice when faced with a chain of decisions requires a person to judge both anticipated outcomes and future actions. Although economic decision-making models account for both risk and reward in single-choice contexts, there is a dearth of similar knowledge about sequential choice. Classical utility-based models assume that decision-makers select and follow an optimal predetermined strategy, regardless of the particular order in which options are presented. An alternative model involves continuously reevaluating decision utilities, without prescribing a specific future set of choices. Here, using behavioral and functional magnetic resonance imaging (fMRI) data, we studied human subjects in a sequential choice task and use these data to compare alternative decision models of valuation and strategy selection. We provide evidence that subjects adopt a model of reevaluating decision utilities, in which available strategies are continuously updated and combined in assessing action values. We validate this model by using simultaneously acquired fMRI data to show that sequential choice evokes a pattern of neural response consistent with a tracking of anticipated distribution of future reward, as expected in such a model. Thus, brain activity evoked at each decision point reflects the expected mean, variance, and skewness of possible payoffs, consistent with the idea that sequential choice evokes a prospective evaluation of both available strategies and possible outcomes

The role of presentation format on decision-makers' behaviour in accounting

The recent increase in researching presentation format area is resulting in an increase in awareness of the importance of presentation format on decision-makers' behaviour. This paper presents a synthesis of prior research on presentation format in the accounting literature which could be used as bases and references for future research. It reviews and evaluates existing accounting literature that examines the linkages of presentation format on decision-makers behaviour. Finally, future research opportunities in this area are made

Optimal treatment allocations in space and time for on-line control of an emerging infectious disease

A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America

Modeling economic systems as locally-constructive sequential games

Real-world economies are open-ended dynamic systems consisting of heterogeneous interacting participants. Human participants are decision-makers who strategically take into account the past actions and potential future actions of other participants. All participants are forced to be locally constructive, meaning their actions at any given time must be based on their local states; and participant actions at any given time affect future local states. Taken together, these essential properties imply real-world economies are locally-constructive sequential games. This paper discusses a modeling approach, Agent-based Computational Economics, that permits researchers to study economic systems from this point of view. ACE modeling principles and objectives are first concisely presented and explained. The remainder of the paper then highlights challenging issues and edgier explorations that ACE researchers are currently pursuing