Integrated Uncertainty Assessment For Project Evaluation and Risk Analysis

Corre, B.; Vincent, Gilles; Feraudy, V. de; Thore, P.

doi:10.3997/2214-4609.201405752

Cited by 3 publications

(3 citation statements)

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“…Geometric uncertainty can be sampled by adding geometric perturbations to an existing reference model (Caumon et al 2007;Corre et al 2000;Lecour et al 2001) or creating several models after perturbing data (Lindsay et al 2013;Wellmann et al 2010). As the very existence of some objects is also uncertain in many cases, it is also useful to consider object-based stochastic simulation.…”

Section: Object Uncertaintymentioning

confidence: 99%

Geological Objects and Physical Parameter Fields in the Subsurface: A Review

Caumon

2018

Handbook of Mathematical Geosciences

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Geologists and geophysicists often approach the study of the Earth using different and complementary perspectives. To simplify, geologists like to define and study objects and make hypotheses about their origin, whereas geophysicists often see the earth as a large, mostly unknown multivariate parameter field controlling complex physical processes. This chapter discusses some strategies to combine both approaches. In particular, I review some practical and theoretical frameworks associating petrophysical heterogeneities to the geometry and the history of geological objects. These frameworks open interesting perspectives to define prior parameter space in geophysical inverse problems, which can be consequential in under-constrained cases.

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Section: Object Uncertaintymentioning

confidence: 99%

Geological Objects and Physical Parameter Fields in the Subsurface: A Review

Caumon

2018

Handbook of Mathematical Geosciences

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“…Papers by Jones 36 , Narayanan 37 and Corre 17 provide examples and more in-depth discussion. The latter reference shows how the techniques can be applied to time-varying responses, such as cumulative production.…”

Section: Generating Simple Surrogatesmentioning

confidence: 99%

“…The literature addresses various elements (technology and/or implementation) of the approach outlined above. Simpson 2 and Jonkman 3 provide two recent overviews and a sampling is provided throughout this paper [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] . With a few exceptions, most articles tend to focus on either one technology (e.g.…”

Section: Introductionmentioning

confidence: 99%

Improving Investment Decisions Using a Stochastic Integrated Asset Model

Begg

Bratvold

Campbell

2001

SPE Annual Technical Conference and Exhibition

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This paper addresses the need for a holistic, integrated approach to assessing the impacts of uncertainty on oil and gas investment decision-making. We argue that this cannot be accomplished effectively by just adding a capability to deal with uncertainty to classical, rigorous models of all the components that contribute to an investment decision evaluation. Further, we suggest that such an approach, if feasible, is not desirable. Instead, we propose the concept of a Stochastic Integrated Asset Model (SIAM) embedded in a decision support system. This approach involves trading-off some technical rigor for a more complete and accurate assessment of the impacts of uncertainty on the investment decision-making process. The main elements of the system are: simplified component models for each domain; Monte Carlo simulation engine; modeling language for customization, incorporation of interdependencies between components, implementation of decision logic and updating information as a result of learning. We illustrate how such a system identifies which uncertainties impact the decision the most; values the acquisition of information (data, technical analysis) and encourages flexibility in go-forward plans to mitigate and/or exploit uncertainties. Further applications are to the optimization of development plans, real options valuation and the generation of consistent, risked cash flows for input to portfolio analysis. We believe that application of such a system results in a true value-driven focus to the work of multidisciplinary asset teams through its ability to integrate the technical and business aspects of decisions.

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Managing Reservoir Uncertainty in Gas Field Development Using Experimental Design

Itotoi¹,

Ojeke²,

Nnamdi³

et al. 2010

All Days

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In a new field development, enormous time and energy are put in to identify and quantify both static and dynamic reservoir uncertainties. This could be very challenging when information is limited and not readily available. These data uncertainties vary from project to project depending on the quantity of reliable reservoir data acquired during the exploration and appraisal stage. The first step towards managing reservoir uncertainty in field development is to identify and quantify the key reservoir parameters that affect hydrocarbon in place and recovery. These parameters or factors are so many that their combinational effects result in multiple cases of development options, which will require many simulation runs. Stochastic and statistical data analysis coupled with Experimental Design(ED) have been used in recent times to reduce time and energy expended in managing key reservoir uncertainty parameters. The case study presented in this paper gives an illustration of a successful ED application in a new gas field development. A detailed workflow was created to identify and quantify the uncertainties and risks in the dynamic model using probabilistic approach. This resulted in Probabilistic field forecasts that were further used to test surface development options in order to determine the optimal field development strategy that is robust to multiple realisations of reservoir uncertainties

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Integrated Uncertainty Assessment For Project Evaluation and Risk Analysis

Cited by 3 publications

References 0 publications

Geological Objects and Physical Parameter Fields in the Subsurface: A Review

Geological Objects and Physical Parameter Fields in the Subsurface: A Review

Improving Investment Decisions Using a Stochastic Integrated Asset Model

Managing Reservoir Uncertainty in Gas Field Development Using Experimental Design

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