Financial decision making about found oil and gas in Ghana : real options vs. traditional methods

This research is a study of the use of capital budgeting methods for investment decisions. It uses both the traditional methods and the newly introduced approach called the real options analysis to make a decision. The research elucidates how capital budgeting can be done when analysts encounter projects with high uncertainty and are capital intensive, for example oil and gas production. It then uses the oil and gas find in Ghana as a case study to support its argument. For a clear understanding a thorough literature review was done, which highlights the advantages and disadvantages of both methods. The revenue that the project will generate and the costs of production were obtained from the predictions by analysts from GNPC and compared to others experts’ opinion. It then applied both the traditional and real option valuation on the oil and gas find in Ghana to determine the project’s feasibility. Although, there are some short falls in real option analysis that are presented in this research, it is still helpful in valuing projects that are capital intensive with high volatility due to the strategic flexibility management possess in their decision making. It also suggests that traditional methods of evaluation should still be maintained and be used to value projects that have no options or those with options yet the options do not have significant impact on the project. The research points out the economic ripples the production of oil and gas will have on Ghana’s economy should the project be undertaken. These ripples include economic growth, massive job creation and reduction of the balance of trade deficit for the country. The long run effect is an eventually improvement of life of the citizens. It is also belief that the production of gas specifically can be used to generate electricity in Ghana which would enable the country to have a more stable and reliable power source necessary to attract more foreign direct investment.

MODEL UPDATING AND STRUCTURAL HEALTH MONITORING OF HORIZONTAL AXIS WIND TURBINES VIA ADVANCED SPINNING FINITE ELEMENTS AND STOCHASTIC SUBSPACE IDENTIFICATION METHODS

Wind energy has been one of the most growing sectors of the nation’s renewable energy portfolio for the past decade, and the same tendency is being projected for the upcoming years given the aggressive governmental policies for the reduction of fossil fuel dependency. Great technological expectation and outstanding commercial penetration has shown the so called Horizontal Axis Wind Turbines (HAWT) technologies. Given its great acceptance, size evolution of wind turbines over time has increased exponentially. However, safety and economical concerns have emerged as a result of the newly design tendencies for massive scale wind turbine structures presenting high slenderness ratios and complex shapes, typically located in remote areas (e.g. offshore wind farms). In this regard, safety operation requires not only having first-hand information regarding actual structural dynamic conditions under aerodynamic action, but also a deep understanding of the environmental factors in which these multibody rotating structures operate. Given the cyclo-stochastic patterns of the wind loading exerting pressure on a HAWT, a probabilistic framework is appropriate to characterize the risk of failure in terms of resistance and serviceability conditions, at any given time. Furthermore, sources of uncertainty such as material imperfections, buffeting and flutter, aeroelastic damping, gyroscopic effects, turbulence, among others, have pleaded for the use of a more sophisticated mathematical framework that could properly handle all these sources of indetermination. The attainable modeling complexity that arises as a result of these characterizations demands a data-driven experimental validation methodology to calibrate and corroborate the model. For this aim, System Identification (SI) techniques offer a spectrum of well-established numerical methods appropriated for stationary, deterministic, and data-driven numerical schemes, capable of predicting actual dynamic states (eigenrealizations) of traditional time-invariant dynamic systems. As a consequence, it is proposed a modified data-driven SI metric based on the so called Subspace Realization Theory, now adapted for stochastic non-stationary and timevarying systems, as is the case of HAWT’s complex aerodynamics. Simultaneously, this investigation explores the characterization of the turbine loading and response envelopes for critical failure modes of the structural components the wind turbine is made of. In the long run, both aerodynamic framework (theoretical model) and system identification (experimental model) will be merged in a numerical engine formulated as a search algorithm for model updating, also known as Adaptive Simulated Annealing (ASA) process. This iterative engine is based on a set of function minimizations computed by a metric called Modal Assurance Criterion (MAC). In summary, the Thesis is composed of four major parts: (1) development of an analytical aerodynamic framework that predicts interacted wind-structure stochastic loads on wind turbine components; (2) development of a novel tapered-swept-corved Spinning Finite Element (SFE) that includes dampedgyroscopic effects and axial-flexural-torsional coupling; (3) a novel data-driven structural health monitoring (SHM) algorithm via stochastic subspace identification methods; and (4) a numerical search (optimization) engine based on ASA and MAC capable of updating the SFE aerodynamic model.