The cost of construction projects depends on their size, complexity, and duration. Construction management applies effective management techniques to the planning, design, and construction of a project from conception to completion for the purpose of controlling time, cost and quality. A real options approach in construction projects, improves strategic thinking by helping planners recognize, design and use flexible alternatives to manage dynamic uncertainty. In order to manage uncertainty using this approach, it is necessary to value the real options. Real option models assume independence of option holder and the impacts of underlying uncertainties on performance and value. The current work proposes and initially tests whether project management reduces the value of real options. The example of resource allocation is used to test this hypothesis. Based on the results, it is concluded that project management reduces the value of real options by reducing variance of the exercise signal and the difference between exercise conditions and the mean exercise signal.

Current techniques for production data integration into reservoir models can be broadly grouped into two categories: deterministic and Bayesian. The deterministic approach relies on imposing parameter smoothness constraints using spatial derivatives to ensure large-scale changes consistent with the low resolution of the production data. The Bayesian approach is based on prior estimates of model statistics such as parameter covariance and data errors and attempts to generate posterior models consistent with the static and dynamic data. Both approaches have been successful for field-scale applications although the computational costs associated with the two methods can vary widely. This is particularly the case for the Bayesian approach that utilizes a prior covariance matrix that can be large and full. To date, no systematic study has been carried out to examine the scaling properties and relative merits of the methods. The main purpose of this work is twofold. First, we systematically investigate the scaling of the computational costs for the deterministic and the Bayesian approaches for realistic field-scale applications. Our results indicate that the deterministic approach exhibits a linear increase in the CPU time with model size compared to a quadratic increase for the Bayesian approach. Second, we propose a fast and robust adaptation of the Bayesian formulation that preserves the statistical foundation of the Bayesian method and at the same time has a scaling property similar to that of the deterministic approach. This can lead to orders of magnitude savings in computation time for model sizes greater than 100,000 grid blocks. We demonstrate the power and utility of our proposed method using synthetic examples and a field example from the Goldsmith field, a carbonate reservoir in west Texas. The use of the new efficient Bayesian formulation along with the Randomized Maximum Likelihood method allows straightforward assessment of uncertainty. The former provides computational efficiency and the latter avoids rejection of expensive conditioned realizations.

Estimating original hydrocarbons in place (OHIP) in a reservoir is fundamentally important to estimating reserves and potential profitability. Quantifying the uncertainties in OHIP estimates can improve reservoir development and investment decision-making for individual reservoirs and can lead to improved portfolio performance. Two traditional methods for estimating OHIP are volumetric and material balance methods. Probabilistic estimates of OHIP are commonly generated prior to significant production from a reservoir by combining volumetric analysis with Monte Carlo methods. Material balance is routinely used to analyze reservoir performance and estimate OHIP. Although material balance has uncertainties due to errors in pressure and other parameters, probabilistic estimates are seldom done. In this thesis I use a Bayesian formulation to integrate volumetric and material balance analyses and to quantify uncertainty in the combined OHIP estimates. Specifically, I apply Bayes?? rule to the Havlena and Odeh material balance equation to estimate original oil in place, N, and relative gas-cap size, m, for a gas-cap drive oil reservoir. The paper considers uncertainty and correlation in the volumetric estimates of N and m (reflected in the prior probability distribution), as well as uncertainty in the pressure data (reflected in the likelihood distribution). Approximation of the covariance of the posterior distribution allows quantification of uncertainty in the estimates of N and m resulting from the combined volumetric and material balance analyses. Several example applications to illustrate the value of this integrated approach are presented. Material balance data reduce the uncertainty in the volumetric estimate, and the volumetric data reduce the considerable non-uniqueness of the material balance solution, resulting in more accurate OHIP estimates than from the separate analyses. One of the advantages over reservoir simulation is that, with the smaller number of parameters in this approach, we can easily sample the entire posterior distribution, resulting in more complete quantification of uncertainty. The approach can also detect underestimation of uncertainty in either volumetric data or material balance data, indicated by insufficient overlap of the prior and likelihood distributions. When this occurs, the volumetric and material balance analyses should be revisited and the uncertainties of each reevaluated.

This study presents an analysis of several recently published methods for quantifying the uncertainty in economic evaluations due to uncertainty in future oil prices. Conventional price forecasting methods used in the industry typically underestimate the range of uncertainty in oil and gas price forecasts. These forecasts traditionally consider pessimistic, most-likely, and optimistic cases in an attempt to quantify economic uncertainty. The recently developed alternative methods have their unique strengths as well as weaknesses that may affect their applicability in particular situations. While stochastic methods can improve the assessment of price uncertainty they can also be tedious to implement. The inverted hockey stick method is found to be an easily applied alternative to the stochastic methods. However, the primary basis for validating this method has been found to be unreliable. In this study, a consistent and reliable validation of uncertainty estimates predicted by the inverted hockey stick method is presented. Verifying the reliability of this model will ensure reliable quantification of economic uncertainty. Although we cannot eliminate uncertainty from investment evaluations, we can better quantify the uncertainty by accurately predicting the volatility in future oil and gas prices. Reliably quantifying economic uncertainty will enable operators to make better decisions and allocate their capital with increased efficiency.

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Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 144-155). Available also in a digital version from Dissertation Abstracts.

Thesis (M.S.)--Mississippi State University. Department of Mechanical Engineering. / Title from title screen. Includes bibliographical references.

Thesis (M.S.)--University of South Florida, 2009. / Title from PDF of title page. Document formatted into pages; contains 52 pages. Includes bibliographical references.

Thesis (M.S.) in Spatial Information Science and Engineering--University of Maine, 2002. / Includes vita. Includes bibliographical references (leaves 101-106).

Includes bibliographical references (p. 185-196).