The Calculus Of Common Sense

Grayson, C. Jackson

doi:10.2118/309-ms

Cited by 3 publications

(2 citation statements)

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“…Decision-making methods have been used in the industry since 1962 (Grayson 1962). These methods have been advantageous because of the highly uncertain nature of many activities in the industry.…”

Section: Decision-making Evaluation Processes For Surfactant Floodingmentioning

confidence: 99%

Decision Making Under Uncertainty: Applying the Least-Squares Monte Carlo Method in Surfactant-Flooding Implementation

2013

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This study introduces a decision-making evaluation method for flexibility in surfactant flooding. The method aims to capture the effects of uncertainty in the time series for both technical and economic parameters and produce a near-optimal policy with respect to these uncertainties as they vary with time. The evaluation method used was the least-squares Monte Carlo (LSM) method, which is best-suited for evaluating flexibility in project implementation. The decision analyzed was that of finding the best time to start surfactant flooding during the lifetime of a field under uncertainty. The study was conducted on two reservoir models: a 3D homogeneous model and a 2D heterogeneous model. The technical uncertainties considered were the residual oil saturation (ROS) to the surfactant flood, surfactant adsorption, and reservoir heterogeneity, and the main economic uncertain parameters considered were oil price, surfactant cost, and water-injection and -production costs. The results show that the LSM method provides a decision-making tool that was able to capture the value of flexibility in surfactant-flooding implementation and provides some insight into the effect of uncertainty on decision making, which can help mitigate adverse circumstances should they arise or capture the upside potential if circumstances prove beneficial. The results found that the optimal policy obtained was reliable and that heterogeneity and different well-placement patterns affect the value of flexibility and optimal policy for different reservoir models. Furthermore, possible extensions to enhance the LSM method were discussed.

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Section: Decision-making Evaluation Processes For Surfactant Floodingmentioning

confidence: 99%

Decision Making Under Uncertainty: Applying the Least-Squares Monte Carlo Method in Surfactant-Flooding Implementation

2013

View full text Add to dashboard Cite

show abstract

“…Decisions making methods have been used in the industry since 1960 (Grayson 1960). These methods have proven advantageous due to the highly uncertain nature of many activities in the industry.…”

Section: Decision Making Evaluation Processes For Surfactant Floodingmentioning

confidence: 99%

Decision Making Under Uncertainty in EOR: Applying the Least Squares MonteCarlo Method in Chemical EOR Implementation

Alkhatib

Babaei

King

2012

SPE Europec/Eage Annual Conference

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This study introduces a decision making evaluation method for flexibility in chemical EOR. The method aims to capture the effects of dynamic uncertainties both technical and economic and produce a near-optimal policy with respect to these uncertainties as they vary with time. The evaluation method used was the Least-Squares MonteCarlo(LSM) method which is best suited for evaluating flexibility in project implementation. The decision analysed was that of finding the best time to initiate surfactant flooding during the life time of a field under uncertainty. The study was conducted on two reservoir models: 3-D homogeneous model and a 2-D heterogeneous model. The technical uncertainties considered were the residual oil saturation to the surfactant flood, surfactant adsorption and reservoir heterogeneity while the main economic uncertain parameters considered were oil price, surfactant cost and water injection and production costs. The results show that the LSM method provides a decision making tool that was able to capture the value of flexibility in surfactant flooding implementation. The LSM method provides great insight into the effect of uncertainty on decision making which can help mitigate adverse circumstances should they arise. The results found that the optimal policy obtained was reliable and that heterogeneity does affect the optimal policy. It was also possible to consider the value of information using this method.

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Decisions and Uncertainty Management: Expertise Matters

Malhotra

Lee

Khurana

2004

SPE Asia Pacific Oil and Gas Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe oil industry loses an estimated US$30 billion or about 25% of its annual upstream expenditure due to poor decision outcomes 1 . This provides a strong incentive to study decision improvement initiatives. While there has been a significant improvement in decision-making, it has been slow and in small steps. Perhaps a paradigm shift maybe necessary to cope with an environment that is likely to become even harsher in future. We believe industry decision experts have overworked the technical and economic factors while largely ignoring individual expertise, which is the third key factor in improving decision-outcomes under conditions of uncertainty in the real world environment. Most failure stories can be largely ascribed to the lack of application of appropriate expertise in judgements. This paper questions the common industry practice of placing decision-making responsibility according to organizational hierarchy rather than expertise. In this first part of a continuing study, relying on two experiments, the paper illustrates two points. First, that decision-makers invariably superimpose their experience and expertise onto the results of normative tools and adjust them. This makes it imperative that they possess the requisite expertise. Secondly, we argue it is possible to develop a methodology to distinguish experts from intermediates and novices in the oil and gas industry. We assert that while years of experience help build expertise, by itself it is an unreliable indicator of the level of expertise.

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The Calculus Of Common Sense

Cited by 3 publications

References 0 publications

Decision Making Under Uncertainty: Applying the Least-Squares Monte Carlo Method in Surfactant-Flooding Implementation

Decision Making Under Uncertainty: Applying the Least-Squares Monte Carlo Method in Surfactant-Flooding Implementation

Decision Making Under Uncertainty in EOR: Applying the Least Squares MonteCarlo Method in Chemical EOR Implementation

Decisions and Uncertainty Management: Expertise Matters

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