Handling Risk and Uncertainty in Portfolio Production Forecasting

Subject. The article analyzes the expected effect of a portfolio of projects in the face of risk and uncertainty, when using real options. Objectives. The purpose is to offer a more objective formula to assess the expected impact of a portfolio of projects for real investment objects under risk and uncertainty, using real options, and provide recommendations for improving the portfolio efficiency. Methods. The study draws on methods of real options and evaluation of investment projects through the real option value, the cash flow discounting method, synthesis, and mathematical modeling. Results. We systematized the main types of real options and developed a formula for calculating the expected effect of project portfolio implementation. The said formula shows that considering the additional long-term costs embedded in a portfolio of real options, which are associated with the use of these real options, and, therefore, reducing the overall risk of projects and the entire portfolio, permit to improve the objectivity of such calculations. Conclusions. When analyzing real options that have real assets as underlying instruments, it is often impossible to apply the computational formulae for financial options, as they differ significantly. The systematization of the main types of real options helps expand the range of application of management solutions. The offered formula enables to improve the efficiency of project insurance under risk and uncertainty and to use additional opportunities for effective development of the company.

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Managing Drilling Risk and Uncertainty Standard in Well Placement

10.2523/13142-ms ◽

2009 ◽

Author(s):

Benny Poedjono ◽

Erhan Isevcan ◽

Guy Joseph Lombardo ◽

John Richard Walker ◽

Simon McCulloch

Keyword(s):

Risk And Uncertainty ◽

Well Placement

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Neurologists’ tolerance to risk and uncertainty in the practical management of multiple sclerosis

10.26226/morressier.59a3e8b7d462b8028d895621 ◽

2017 ◽

Author(s):

Gustavo Saposnik

Keyword(s):

Multiple Sclerosis ◽

Risk And Uncertainty ◽

Practical Management

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Integrated Subsurface to Surface Modeling to Assess Reservoir Uncertainty Quantification to A Carbonate Reservoir

Proc. Indon. Petrol. Assoc., Digital Technical Conference, 2020 ◽

10.29118/ipa20-se-381 ◽

2020 ◽

Author(s):

A. Chaterine

Keyword(s):

Uncertainty Quantification ◽

Gas Production ◽

Carbonate Reservoir ◽

Production Volume ◽

Production Forecast ◽

Uncertainty Range ◽

Field Development ◽

Production Forecasting ◽

Production Scheme ◽

Rock Compressibility

This study accommodates subsurface uncertainties analysis and quantifies the effects on surface production volume to propose the optimal future field development. The problem of well productivity is sometimes only viewed from the surface components themselves, where in fact the subsurface component often has a significant effect on these production figures. In order to track the relationship between surface and subsurface, a model that integrates both must be created. The methods covered integrated asset modeling, probability forecasting, uncertainty quantification, sensitivity analysis, and optimization forecast. Subsurface uncertainties examined were : reservoir closure, regional segmentation, fluid contact, and SCAL properties. As the Integrated Asset Modeling is successfully conducted and a matched model is obtained for the gas-producing carbonate reservoir, highlights of the method are the following: 1) Up to ± 75% uncertainty range of reservoir parameters yields various production forecasting scenario using BHP control with the best case obtained is 335 BSCF of gas production and 254.4 MSTB of oil production, 2) SCAL properties and pseudo-faults are the most sensitive subsurface uncertainty that gives major impact to the production scheme, 3) EOS modeling and rock compressibility modeling must be evaluated seriously as those contribute significantly to condensate production and the field’s revenue, and 4) a proposed optimum production scenario for future development of the field with 151.6 BSCF gas and 414.4 MSTB oil that yields a total NPV of 218.7 MMUSD. The approach and methods implemented has been proven to result in more accurate production forecast and reduce the project cost as the effect of uncertainty reduction.

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