Aiding the decision process by automated reservoir simulation and economic modelling

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Aiding the decision process by automated reservoir simulation and economic modelling. / Rainer, Clemens.
2019.

Publikationen: Thesis / Studienabschlussarbeiten und HabilitationsschriftenMasterarbeit

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@mastersthesis{89912808053d41ec89b4bc7243f41c6a,
title = "Aiding the decision process by automated reservoir simulation and economic modelling",
abstract = "For oil and gas field developments the economic success depends on a number of parameters, such as geologic uncertainties, operational risks and price forecasts. To be able to make a sound and quality decision it is necessary to analyse various alternatives and be able to compare the results with each other. Every undertaking during the life cycle bears the risk of failure and uncertain results, connected correlated likelihoods and a range of uncertain variables. For a synthetic reservoir all possible results are simulated and evaluated according to their probability to happen and the economic impact over field life. A holistic approach is presented here to evaluate the possible outcomes of a well intervention by combining reservoir simulation with a fully stochastic economic model. As the number of the varying parameters increases, so do the possible results and become a limiting factor for performing complete evaluations. For this reason, a program is developed to automate setting up numerical simulation models and export the production forecast in a standardised format. The subsequent economic evaluation assesses the likelihood and economic variables of every forecast with the help of a fully probabilistic model. The result is a risk weighted expected monetary value, which allows picking the optimal time for an intervention depending on the assumed probability of success. The objectives are met by a computerised method to automate the workflow and remove the bottleneck of manual setup and analysis of every forecast. This enables rapid modification of models and investigating variations deemed too time consuming or typically simplified by approximations.",
keywords = "Erd{\"o}lwirtschaft, Lagerst{\"a}ttensimulation, Erfolgswahrscheinlichkeit, Monte Carlo Methode, Entscheidungsbaum, Erwartungswert, Automatisierungssoftware, Stochastik, petroleum economics, reservoir engineering, numerical simulation, automation, decision analysis, probability of success, stochastic, Monte Carlo, conditional probability",
author = "Clemens Rainer",
note = "no embargo",
year = "2019",
language = "English",
school = "Montanuniversitaet Leoben (000)",

}

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TY - THES

T1 - Aiding the decision process by automated reservoir simulation and economic modelling

AU - Rainer, Clemens

N1 - no embargo

PY - 2019

Y1 - 2019

N2 - For oil and gas field developments the economic success depends on a number of parameters, such as geologic uncertainties, operational risks and price forecasts. To be able to make a sound and quality decision it is necessary to analyse various alternatives and be able to compare the results with each other. Every undertaking during the life cycle bears the risk of failure and uncertain results, connected correlated likelihoods and a range of uncertain variables. For a synthetic reservoir all possible results are simulated and evaluated according to their probability to happen and the economic impact over field life. A holistic approach is presented here to evaluate the possible outcomes of a well intervention by combining reservoir simulation with a fully stochastic economic model. As the number of the varying parameters increases, so do the possible results and become a limiting factor for performing complete evaluations. For this reason, a program is developed to automate setting up numerical simulation models and export the production forecast in a standardised format. The subsequent economic evaluation assesses the likelihood and economic variables of every forecast with the help of a fully probabilistic model. The result is a risk weighted expected monetary value, which allows picking the optimal time for an intervention depending on the assumed probability of success. The objectives are met by a computerised method to automate the workflow and remove the bottleneck of manual setup and analysis of every forecast. This enables rapid modification of models and investigating variations deemed too time consuming or typically simplified by approximations.

AB - For oil and gas field developments the economic success depends on a number of parameters, such as geologic uncertainties, operational risks and price forecasts. To be able to make a sound and quality decision it is necessary to analyse various alternatives and be able to compare the results with each other. Every undertaking during the life cycle bears the risk of failure and uncertain results, connected correlated likelihoods and a range of uncertain variables. For a synthetic reservoir all possible results are simulated and evaluated according to their probability to happen and the economic impact over field life. A holistic approach is presented here to evaluate the possible outcomes of a well intervention by combining reservoir simulation with a fully stochastic economic model. As the number of the varying parameters increases, so do the possible results and become a limiting factor for performing complete evaluations. For this reason, a program is developed to automate setting up numerical simulation models and export the production forecast in a standardised format. The subsequent economic evaluation assesses the likelihood and economic variables of every forecast with the help of a fully probabilistic model. The result is a risk weighted expected monetary value, which allows picking the optimal time for an intervention depending on the assumed probability of success. The objectives are met by a computerised method to automate the workflow and remove the bottleneck of manual setup and analysis of every forecast. This enables rapid modification of models and investigating variations deemed too time consuming or typically simplified by approximations.

KW - Erdölwirtschaft

KW - Lagerstättensimulation

KW - Erfolgswahrscheinlichkeit

KW - Monte Carlo Methode

KW - Entscheidungsbaum

KW - Erwartungswert

KW - Automatisierungssoftware

KW - Stochastik

KW - petroleum economics

KW - reservoir engineering

KW - numerical simulation

KW - automation

KW - decision analysis

KW - probability of success

KW - stochastic

KW - Monte Carlo

KW - conditional probability

M3 - Master's Thesis

ER -