FORECASTING OF PETROLEUM CONSUMPTION IN BRAZIL USING THE INTENSITY OF ENERGY TECHNIQUE

The purpose of this paper is to forecast petroleum consumption in Brazil for the year 2000 based upon logistic models, learning models, and translog models using the technique of intensity of energy use. The models employ a time series of 30 years for projection. An investigation of the evolution of petroleum consumption profile was made based upon three characteristic effects: structural, content and scale effects. Evaluation of forecasting models presented good results, with the translog model showing the best performance in terms of accuracy. The learning and translog models indicated that GDP is the main determinant for petroleum consumption evolution in the future, defining a range of 64 000 and 109 000 thousand of tonnes of oil equivalent on two defined GDP growth scenarios.21995896

LONG-RANGE METAL CONSUMPTION FORECASTS USING INNOVATIVE METHODS - THE CASE OF ALUMINUM IN BRAZIL TO THE YEAR 2000

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Identifying potential impacts of bonding instruments on offshore oil projects

The present paper deals with the potential financial impacts of different bonding instruments on offshore oil projects. Three types of performance bond instruments (corporate surety, leasing-specific abandonment account, and cash) were tested and analyzed for three offshore oil-producing fields under a hypothetical bonding regime. Sensitivity analysis of 'net present' and 'government take' values indicates corporate surety bonds cause fewer impacts yielding significantly better payoffs. Several related issues are discussed considering government and industry perspectives. (C) 2001 Published by Elsevier Science Ltd.271435

Estimation of volatility of selected oil production projects

In oil project valuation and investment decision-making, volatility is a key parameter, but it is difficult to estimate. From a traditional investment viewpoint, volatility reduces project value because it increases its discount rate via a higher risk premium. Contrarily, according to the real-option pricing theory, volatility may aggregate value to the project, since the downside potential is limited whereas the upside is theoretically unbounded. However, the estimation of project volatility is very complicated since there is not a historical series of project values. In such cases, many analysts assume that oil price volatility is equal to that of project. In order to overcome such problems, in this paper an alternative numerical method based on present value of future cash flows and Monte Carlo simulation is proposed to estimate the volatility of projects. This method is applied to estimate the volatility of 12 deep-water offshore oil projects considering that oil price will evolve according to one of two stochastic processes: Geometric Brownian Motion and Mean-Reverting Motion. Results indicate that the volatility of commodity usually undervalue that of project. For the set of offshore projects analyzed in this paper, project volatility is at least 79% higher than that of oil prices and increases dramatically in those cases of high capital expenditures and low price. (c) 2006 Elsevier B.V. All rights reserved.544173212913

Investment decision in oil and gas projects using real option and risk tolerance models

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)This paper presents a model for valuation and decision making, integrating discounted cash flow, real-options pricing and preference theory, aiming to cover the following questions: i) what is the current value of a project?; ii) what is the optimal investment rule?; iii) what is the optimal working interest? The traditional model suggests that, when the project value is above its investment cost, the corporation should invest immediately and incur in 100% working interest. The real option pricing suggests that the corporation should only invest if the project's current value is at least 1.85 times investment cost. The preference theory suggests funding only 44.38% working interest, and partners must acquire the remaining 55.62%. These tools must be integrated in order to allow a more realistic treatment of risk. In general, when the uncertainty (volatility) of cash flow components increases, the two models give more divergent results.O TEXTO COMPLETO DESTE ARTIGO, ESTARÁ DISPONÍVEL À PARTIR DE FEVEREIRO DE 2015.141671323Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)CEPETRO/UNICAMPConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)PETROBRASCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

LATIN-AMERICA METAL CONSUMPTION - RECENT TRENDS AND DETERMINANTS

This paper explores the main features of changing trends in Latin America metal consumption over the 37 year period 1950-1987. Among other LDC regions or countries, L. America is characterized by growing consumption shares for some major metals. Trends in apparent consumption and intensity of use are examined for five metals (aluminum, copper, lead, tin and zinc) in three countries (Argentina, Brazil and Mexico), which account for more than 70% of the consumption in Latin America. The effects of changes in material composition of product, product composition of income, and gross domestic product on post 1974 metal consumption in Brazil are analyzed for these five metals.15435137

Quantifying the value of technological, environmental and financial gain in decision models for offshore oil exploration

This paper suggests a framework to improve the quality of investment decisions in petroleum exploration. The proposed model enables the decision-maker to consider explicitly three major objectives when evaluating new petroleum ventures-financial, environmental and technological gain. The MultiAttribute Utility Theory (MAUT) provides a logical mean of decision for conflicting objectives. The MAUT is based on the risk preference of the firm, combining the objectives in the unique additive or multiplicative model. A high-dimensional sensitivity analysis technique is used for evaluating the weights of multicriteria decision models. The main advantage of this approach is that it allows a better simultaneous change of the weights and provides indications for a robustness control. The weights are obtained by a random process and are hierarchically adjusted using the analyst preferences. This paper proposes a study case comparing the application of this methodology in exploration projects located at five different offshore oil provinces in the Brazilian Continental Shelf. The MAUT methodology presented in this work demonstrates that, in some mature areas, the advantages of exploration are restricted only to financial gain. On the other hand, other seemingly less attractive areas, such as deep horizons in deep-water basins, may represent attractive targets for new exploration as a result of the interaction of technological advancement, and financial and market factors. The proposed approach allows the investigation of sensitivity for several options and alternatives. It also provides a rational tool for managers to make decisions according to firm preferences and objectives in complex oil prospects. (C) 2001 Elsevier Science B.V. All rights reserved.3241731SI11512

A method to estimate block values through competitive bidding

The bidding process is a mechanism that has been widely used by different countries to optimally distribute their oil exploratory acreages. One of the big challenges for both companies and government agencies is the estimation of the block values. Considering that the bid value is by and large a fraction of the estimated unknown reserve, the objective of this article is to reach a set of proxies of unknown values of the blocks through the successful bids. The estimation value of the block is calculated through a stochastic simulation of bid fractions using a compound probability distribution. The model was tested and validated using the public data available from the Brazilian seven licensing rounds. For these competitive bids, area wide-spread in 22 sedimentary basins were offered to more than for oil companies that retained 610 blocks, paying $1.4 billion as a cash bonus. The model output was restricted to the Campos Basin because it is one of the most attractive areas for oil and gas opportunities, concentrating approximately 80% of the Brazilian national oil production with a supply of 1.8 million bbl/day. The simulation model indicated that this approach can be used as an auxiliary decision framework by oil companies for new investments and bidding strategies as well as by the regulatory agency to evaluate bid performance in different world regions and geological settings possessing similar competitive bidding schemes.9210SI12931314Programa de Recursos Humanos-Brazilian National Petroleum AgencyCenter for Petroleum Studies, CEPETRO/UNICAMPNational Research Counci

SERFIT - AN ALGORITHM TO FORECAST MINERAL TRENDS

A PASCAL computer program, named SERFIT, facilitates the identification of trend model for long-term forecasting and the estimation of model parameters. Model identification is achieved through the computation of slope characteristics from mineral data time series. The trend models generated by the program are: linear, normal, lognormal, and modified exponentials: simple-modified exponential, logistic, derivative logistic, Gompertz, and derivative Gompertz. Parameters of the family of modified exponential models are estimated using Mitscherlich's regression, which is based upon the maximum likelihood method and provides a probability structure for the models. SERFIT is demonstrated on U.S. petroleum production and world copper consumption data.21570371