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Managing E and P assets from a portfolio perspective

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Abstract

Modern portfolio analysis tools give decision-makers in the oil and gas industry analytical support and specific guidance to the intuitive sense that the best balance for the business lies in a combination of tactics and simultaneous actions on multiple projects. Because of the complexity of considering several projects or tactics simultaneously, decision-makers tend to treat their projects and tactics as independent of anything else in the business. Yet all of the projects in the business interact with one another. Project interactions arise from factors such as price-resource sharing, performance targets, commercial and market interactions, and technical risk. Knowledge of how projects interact and how the aggregation of all projects sum to meet balanced business requirements should guide decisions. A portfolio perspective helps the decision-maker understand the total impact on business balance resulting from a single decision. With portfolio tools, the decision-maker is ultimately able to frame options in terms of the probability of meeting a suite of balanced performance targets.
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... The risk-variance stochastic models used to derive efficient frontiers that evolved to effectively apply this theory continue to be widely applied and developed in the optimization of portfolios of stock market and other financial assets. The same principles have been applied to real gas and oil portfolios since the 1990s with the recognition that establishing efficient frontiers (Howell et al., 1998;Adams et al., 2000;Ball and Savage, 1999;Orman and Duggan, 1999) using downside risk measures such as semi-standard deviation (SSD) leads to valuable insight and aids decision making. Wood (2000) emphasized the benefits of using expected monetary value (EMV) (Grayson, 1962(Grayson, , 1967, with risk applied individually at the asset level, the risked-asset values then combined to derive the portfolio, combined with SSD to provide the portfolio-level risk metric. ...
... Simulation data sets can approximate such relationships and the results of simulation models are therefore useful in complementing the results of a linear optimizer and verifying that they are realistic solutions in terms of value and risk. Until the early 2000's optimization of oil and gas asset portfolios was performed, with useful results using rank and cut and simplex algorithms complemented by simulation data sets to provide the risk analysis dimension at the portfolio level (Howell et al., 1998;Orman and Duggan, 1999;Wood, 2000). Non-linear optimizers and evolutionary algorithms (see below) have contributed since the 1990s to improve the way in which nonlinear relationships and multiple objectives are now addressed. ...
... When valuing gas and oil assets it is financial value metrics that sit at the top of the list in terms of a portfolio models output, whether it is deterministic or stochastic. Financial value metrics typically of interest to portfolio decision makers are operating cash flow, post-tax cash flow, pre-tax and post-tax net income, at the asset and/or, more likely, the corporate level, together with various balance sheet metrics related to debt and equity, and specific cost measures, particularly those related to capital investment (capex) requirements (Howell et al., 1998;Wood, 2001). In addition, nonfinancial metrics are also measured and evaluated, such as gas and oil production (daily or annual rates), gas and oil reserves volumes at particular points in time specified to different levels of confidence and rates at which those reserves and production are replaced. ...
... Two methods of measuring downside risk are used in this study and both are established methods for measuring portfolio-level risk for gas and oil portfolios (Wood, 2002): the mean deviation of values less than the specified target in a stochastic distribution, as used by Howell et al. (1998); the semi-standard deviation (SSD) of values less than the specified target in a stochastic distribution (e.g. as used by Orman and Duggan, 1999). These downside measures of risk are preferable to using uncertainty measures (e.g., standard deviation), because they do not penalise the distribution for being positively skewed on the high side, an occurrence that is positive opportunity not risk, and they are valid no matter how-skewed or irregularly-formed the stochastic distribution. ...
Article
Asset portfolio modeling and optimization are critical activities for upstream (exploration and production) gas and oil companies in order for decision makers to establish the combined value of their assets and to select assets for further development, divestment and/or acquisition. However, it is an activity that is typically not conducted in a standardized and systematic way, with many companies relying on simple deterministic discounted cash flow asset-value-roll-up analysis, but missing vital insight to the subtle, but significant characteristics of their portfolios. A more systematic, multi-stage stochastic methodology is proposed to reveal detailed characterization of gas and oil asset portfolios in terms of value, risk and timing. The non-linear nature of risk is taken into account in an approach to risk analysis that begins at the asset level and progresses through to the pre-corporate rolled-up asset portfolio to post-tax portfolio factoring in the corporate financial dimension. The proposed methodology emphasizes the importance of considering financial and non-financial metrics (i.e. production, reserves and timing) over each year of a planning horizon. In addition, those same metrics summed over all the years of a planning horizon, expressed in terms of risked value and downside risk of the portfolio failing to achieve certain strategic targets identifies feasible envelopes for possible asset combinations. The downside risk measures apply important modifications to standard risk-variance analysis, introducing flexibility into the approach to suit diverse strategic objectives of potential portfolio holders. Further analysis of those risk versus risked value feasible envelopes reveals the efficient frontiers representing the asset combinations that achieve the highest value for specific levels of downside risk. Characterizing a portfolio of gas and oil assets with such a methodology helps to frame multi-objective optimization algorithms tailored to suit the unique characteristics of each asset portfolio. Excel spreadsheets driven by visual basic for applications (VBA) macros offer the advantages of flexibility, transparency and customization to characterize asset portfolios with the methodology proposed. A small portfolio involving eleven exploration, appraisal, development and production gas and oil assets (Portfolio X) is presented to illustrate the benefits of the proposed approach to gas and oil asset portfolio characterization. The diversity in character of conventional and unconventional upstream gas assets makes a portfolio approach to their understanding extremely worthwhile.
... 136. Anderson, R.N., Geochemical and Geophysical Structure in the Moodus Drillhole, and the Mass Flux Associated with Low Angle Thrust Fault, Scientific Drilling, p. [238][239][240][241][242][243][244][245][246][247][248][249][250][251][252]1990. 137. ...
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MY VITA as of June 1, 2020, with newest Patent Numbers issued for 5 new Continuation Patents.
... Quantifying risk, particular the downside risk of a portfolio value falling below a specified target value of a strategic goal, is central to modern portfolio methods. This risk is measured in a number of ways, e.g. the mean deviation of all value outcomes from a Monte Carlo simulation that fall below the target value (Howell et al. 1998) or the semi-standard deviation of value outcomes that fall below the target values (Orman & Duggan, 1999). These measures extend portfolio theory, which as originally applied to the financial sector sought to minimise risk by focusing on the lowest standard deviations of portfolio value distributions. ...
Article
Measuring and managing risks are essential as the exploration and production companies increase their use of portfolio management. Risks can be quantified by valuation. A three stage analytical approach based on expected monetary value (EMV) is proposed using Monte Carlo probability methods. The evaluations are based on cash flow, net income, balance sheet, return on equity and reserves performance factors. Risk is quantified in the portfolio management. The values of stock portfolios confirm to normal distributions. A portfolio monitoring process is therefore proposed. The risk analysis is done in detail.
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Roger retired as Emeritus Professor and Sr Research Scientist after 42 years at Columbia University's Lamont Doherty Earth Observatory and Earth and Data Science Institutes. Roger has led teams that have developed the next generation of machine learning control systems for smart hydraulic fracturing, 4D seismic reservoir simulation and smart energy management of electricity, water, steam and occupancy tracking for skyscrapers. In 2020, he was elected to the EU Academy of Sciences. Over his research career, Roger brought in more than a Quarter Billion $$ in National Science Foundation, Department of Energy, and Office of Naval Research grants and contracts, including research ship-time, drillship, platform and drilling costs. He also co-founded AKW Analytics, Bell Geospace, 4D Technologies, CALM Energy and vPatch companies. He co-invented 23 patents, with 8 more pending. Software and licenses for these “Computational Learning Systems©” are available from Columbia Technology Ventures https://techventures.columbia.edu/industry/start-licensing-process . Roger has had technical, business, computational, and working collaborations with Baker Hughes, Boeing, BBN, Booz, BP, Shell, Pennzoil, Con Edison, Range Resources, and Western Geophysical, and has run consortiums with many other companies. Roger has been in residence at Baker Hughes, Con Edison Control Centers, Earthquake Research Institute of University of Tokyo, FedEx, Finmeccanica, GE, IBM Research, Kansas Geological Survey, KBR, Lockheed Martin, Rudin Management, Schlumberger, Sinclair, Texas Energy Center, Urban Utility Center of NYC, USGS, University of California Berkeley and University of Hawaii Manoa. Roger has written 44 books, edited 4 others, published more than 750 peer reviewed scientific and engineering papers, and written and produced 15 technical videos. While at Columbia, Roger graduated 9 Ph.D. students, many of whom are now leaders in national scientific and engineering communities like the National Environmental Research Laboratory, University of Illinois, Chicago, the US Geological Survey as well the international business communities like Head of IBM Research Brazil, CNRS Marcelles FR, IT director of the Commodities Trading Floor at Citibank, and Head of Risk Management Software Development for PEMCO. Roger’s team was recently chosen as the winning University Program in General Electric’s Ecomagination Innovation Challenge, winning a $1.2 million prize. While at the Lamont-Doherty Earth Observatory of Columbia, he founded the Borehole Research, Global Basins Research Network, 4-D Seismic Reservoir Simulation, Portfolio Management and Energy Research Groups. Roger has been Chief Scientist or Logging Scientist of more than 30 oceanographic and Deep-Sea Drilling cruises, including in 1979, when he led the first U.S. research ship to visit the Peoples Republic of China in the modern era. The Wall Street Journal recently characterized him as “a computer-imaging pioneer”. However, perhaps his most difficult assignment ever was as “Head of Umpires” at West Side Baseball Little League in Manhattan, NY. Email: anderson@ldeo.columbia.edu Cell: 713-398-7430
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Research Proposal
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The use of Distributed Electric Energy Storage (DEES) for the real time support and optimization of the electric generation, transmission and distribution (G,T&D) system has been limited to date to pumped hydro, primarily due to a lack of cost-effective options and/or sufficient value bases, as well as actual field experience. Recent developments in advanced energy storage technology, including a number of demonstration and commercial projects, are providing new opportunities to use energy storage in grid stabilization, grid operation support, distribution power quality, and load shifting applications. Our team proposes to characterize the leading DEES markets for New York State, including a projection of the respective capacities and range of values. We will then drill down into the detailed cost, benefit, risk, and uncertainty benefits for use of an exemplary DEES technology, a 10 MW sodium-sulfur (NAS) super-battery in an urban substation in a critical Load Pocket of New York City.
Research Proposal
Full-text available
The use of Distributed Electric Energy Storage (DEES) for the real time support and optimization of the electric generation, transmission and distribution (GT&D) system has been limited to date to pumped hydro, primarily due to a lack of cost-effective options and/or sufficient value bases, as well as actual field experience. Recent developments in advanced energy storage technology, including a number of demonstration and commercial projects, are providing new opportunities to use energy storage in grid stabilization, grid operation support, distribution power quality, and load shifting applications.
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Muitas empresas atuam no mercado por meio de diferentes unidades de negócio, ou seja, com segmentos distintos de produtos. Assim, torna-se importante verificar como cada unidade de negócio comporta-se frente às demais unidades e quais agregam mais valor para a empresa como um todo. Este trabalho visa analisar o risco e retorno de cada unidade e seu efeito no risco e retorno de uma empresa de bens de capital, que atua com três unidades de negócio, utilizando a teoria do portfólio proposta por Markowitz. Verificou-se que a unidade de máquinas ferramenta, isoladamente, é a que apresenta a melhor relação risco-retorno. No contexto de formação de carteiras, foi possível verificar qual a proporção de investimento que deveria ser alocada a cada unidade de negócio de forma a obter a melhor relação risco e retorno.
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