Risk and Uncertainty Management-Best Practices and Misapplications for Cost 
and Schedule Estimates

Peterson, Susan K.; Wardt, John de; Murtha, J.A.

doi:10.2523/97269-ms

Cited by 2 publications

(2 citation statements)

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“…Peterson et al (2005) stated that a thorough risk management process combines two defined formsqualitative analysis and quantitative analysis. Risk is also any factor that might prevent the project from obtaining its objective(s).…”

Section: Risk Analysismentioning

confidence: 99%

Balancing Project Schedule & Cost on Sour Gas Development Project Case Study

Asmoro¹,

Putri²

2012

All Days

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The gas field development project is a challenging phase, since wells need to be drilled to assess the oil/gas reserves and production facilities need to be built. The ultimate objective of the project is to deliver the gas sales to selected buyers as per agreed schedule and price. As result, the project schedule and cost are defined to achieve the objective.Project schedule and cost are known to work in equilibrium. Where one of these dimensions is restricted or extended, the other dimension will then also need to be either extended/increased or restricted/reduced in some way. Given that quality must not be compromised, this paper aims to describe how to get the optimum balance of project schedule and cost on an onshore gas processing plant project located in north-east Indonesia with 90 MMSCFD feed gas capacity. The feed gas has a high content of CO2 and H2S.The schedule of the development project was initially divided into 2 phases: Early Production using sweet gas facilities will be constructed in 22 months and Complete Phase 1 with sour gas facilities will be constructed in 26 months. The project cost was developed by referring to third party cost estimates as part of FEED, to the most recent quotation from the vendor, and to the project cost owner. The Government had to approve the CAPEX of the project as part of the PSC scheme.Monte Carlo simulation was used to calculate risks related to schedule and cost estimates and furthermore to establish realistic ones. The simulation showed that p75 falls within 28 months and MUSD 200 for project phase-1 completion. However, according to its commitment to the gas buyer, the company has to deliver gas within a maximum period of 22 months, which makes this project very challenging.

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Section: Risk Analysismentioning

confidence: 99%

Balancing Project Schedule & Cost on Sour Gas Development Project Case Study

Asmoro¹,

Putri²

2012

All Days

View full text Add to dashboard Cite

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“…Studies advocating the use of probabilistic modeling have been instrumental in the acceptance of these techniques in the upstream oil and gas industry (e.g., Rose, 2001;Corre et al, 2000;Komlosi, 2001;Begg and Bratvold, 2002;Garcia et al, 2003). However, few studies have (1) explored the effect of dependencies between key value drivers on economic evaluations of E&P assets (Peterson et al, 2005) or (2) discussed the limitations of the application of fixed price decks. The analysis executed in this study demonstrates how these complications can be practically addressed and quantifies their impact on the commercial attractiveness of an E&P asset.…”

Section: Introductionmentioning

confidence: 98%

Enhanced Economic Modeling by Correlated Stochastic Models of E&P Costs and Hydrocarbon Prices: The Limitations of Fixed Price Decks and the Versatility of Least-Squares Monte Carlo Simulation

Willigers¹

2009

EUROPEC/EAGE Conference and Exhibition

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As decision-making processes in the E&P industry increasingly rely on probabilistic economic models, determining the accuracy of its methodologies becomes more problematic. Enhancements can be achieved by (1) better understanding the interdependencies between different sources of uncertainty and (2) the abandonment of fixed time series of either hydrocarbon prices or capital expenditures.Historical market data of hydrocarbon prices, steel prices, and daily rig rental rates can be used to establish the correlation between different sources of market risk. Uncertainties can be defined as "fixed" or "dynamic." Fixed uncertainties relate to factors that do not change over time, such as many geological parameters during the early stages of exploration. Most uncertainties that relate to market risk are dynamic, that is, they keep developing over time. For example, not only is the realized price of oil uncertain until the moment the oil has been sold, but the expectation of future oil prices changes. The recognition of this Bayesian property of hydrocarbon prices significantly affects projects with multiple decision points. The forecasted hydrocarbon price at a future decision point is a function of the simulated realized price at that given decision point. Traditional decision tree models apply the same series of static price decks at each decision point and therefore do not accurately reflect the impact of the evolving market outlook during the development of a project.The stochastic model developed in this study accounts for (1) the correlation between different uncertainties and (2) Bayesian price-cost forecasts. The versatility of the Least-Squares Monte Carlo simulation technique is demonstrated by a real option valuation of an asset subjected to a complex tax regime and two future stage-gate decision points. IntroductionOver the past decade, the upstream oil and gas industry has slowly adapted the application of probabilistic or stochastic modeling techniques in place of traditional deterministic economic analysis (Bickel and Bratvold, 2007). Probabilistic modeling dictates that in contrast to single-point estimates for key model variables, a probability-weighted spectrum of potential outcomes is analyzed in an economic cash flow model. The fundamental notion of probabilistic modeling is that given a range of feasible outcomes, some are more likely to occur than others. Probabilistic modeling allows for a much improved understanding of the potential range of outcomes and how certain developments impact the economic performance of a project (Schuyler, 1998;Rose, 2001;Willigers, 2008a).A common approach in risk modeling is to identify the key uncertainties, decisions, and value drivers of an asset and to model several discrete probability-weighted outcomes in a probability or decision tree. This process can also approximate continuous probability distributions. The spectrum of feasible price scenarios is typically approximated using a combination of probability-weighted price decks. Though this approach provides a ...

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Risk and Uncertainty Management-Best Practices and Misapplications for Cost and Schedule Estimates

Cited by 2 publications

References 0 publications

Balancing Project Schedule & Cost on Sour Gas Development Project Case Study

Balancing Project Schedule & Cost on Sour Gas Development Project Case Study

Enhanced Economic Modeling by Correlated Stochastic Models of E&P Costs and Hydrocarbon Prices: The Limitations of Fixed Price Decks and the Versatility of Least-Squares Monte Carlo Simulation

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