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1 
Estimating uncertainties in integrated reservoir studiesZhang, Guohong 30 September 2004 (has links)
To make sound investment decisions, decision makers need accurate estimates of the uncertainties present in forecasts of reservoir performance. In this work I propose a method, the integrated mismatch method, that incorporates the misfit in the history match into the estimation of uncertainty in the prediction. I applied the integrated mismatch method, which overcomes some deficiencies of existing methods, to uncertainty estimation in two reservoir studies and compared results to estimations from existing methods. The integrated mismatch method tends to generate smaller ranges of uncertainty than many existing methods. When starting from nonoptimal reservoir models, in some cases the integrated mismatch method is able to bracket the true reserves value while other methods fail to bracket it. The results show that even starting from a nonoptimal reservoir model, but as long as the experimental designs encompass the true case parameters, the integrated mismatch method brackets the true reserves value. If the experimental designs do not encompass all the true case parameters, but the true reserves value is covered by the experiments, the integrated mismatch method may still bracket the true case. This applies if there is a strong correlation between mismatch and closeness to the true reserves value. The integrated mismatch method does not need a large number of simulation runs for the uncertainty analysis, while some other methods need hundreds of runs.

2 
Control of systems subject to uncertainty and constraintsVillota Cerna, Elizabeth Roxana 15 May 2009 (has links)
All practical control systems are subject to constraints, namely constraints aris¬ing from the actuator’s limited range and rate capacity (input constraints) or from imposed operational limits on plant variables (output constraints). A linear control system typically yields the desirable small signal performance. However, the presence of input constraints often causes undesirable large signal behavior and potential insta¬bility. An antiwindup control consists of a remedial solution that mitigates the eﬀect of input constraints on the closedloop without aﬀecting the small signal behavior. Conversely, an override control addresses the control problem involving output con¬straints and also follows the idea that large signal control objectives do not alter small signal performance. Importantly, these two remedial control methodologies must in¬corporate model uncertainty into their design to be considered reliable in practice. In this dissertation, shared principles of design for the remedial compensation problem are identiﬁed which simplify the picture when analyzing, comparing and synthesiz¬ing for the variety of existing remedial schemes. Two performance objectives, each one related to a diﬀerent type of remedial compensation, and a general structural representation associated with both remedial compensation problems will be consid¬ered. The eﬀect of remedial control on the closedloop will be evaluated in terms of two general frameworks which permit the uniﬁcation and comparison of all known remedial compensation schemes. The diﬀerence systems describing the performance objectives will be further employed for comparison of remedial compensation schemes under uncertainty considerations and also for synthesis of compensators. On the ba¬sis of the diﬀerence systems and the general structure for remedial compensation, systematic remedial compensation synthesis algorithms for antiwindup and override compensation will be given and compared. Successful application of the proposed robust remedial control synthesis algorithms will be demonstrated via simulation.

3 
Infill location determination and assessment of corresponding uncertaintySenel, Ozgur 15 May 2009 (has links)
Accurate prediction of infill well production is crucial since the expected amount
of incremental production is used in the decisionmaking process to choose the best infill
locations. Making a good decision requires taking into account all possible outcomes and
so it is necessary to quantify the uncertainty in forecasts. Many researchers have
addressed the infill well location selection problem previously. Some of them used
optimization algorithms, others presented empirical methods and some of them tried to
solve this problem with statistical approaches. In this study, a reservoir simulation based
approach was used to select infill well locations. I used multiple reservoir realizations to
take different possible outcomes into consideration, generated probabilistic distributions
of incremental field production and, finally, used descriptive statistical analysis to
evaluate results. I quantified the uncertainty associated with infill location selection in
terms of incremental field production and validated the approach on a synthetic reservoir
model. Results of this work gave us the possible infill locations, which have a mean
higher than the minimum economic limit, with a range of expected incremental
production.

4 
Estimating uncertainties in integrated reservoir studiesZhang, Guohong 30 September 2004 (has links)
To make sound investment decisions, decision makers need accurate estimates of the uncertainties present in forecasts of reservoir performance. In this work I propose a method, the integrated mismatch method, that incorporates the misfit in the history match into the estimation of uncertainty in the prediction. I applied the integrated mismatch method, which overcomes some deficiencies of existing methods, to uncertainty estimation in two reservoir studies and compared results to estimations from existing methods. The integrated mismatch method tends to generate smaller ranges of uncertainty than many existing methods. When starting from nonoptimal reservoir models, in some cases the integrated mismatch method is able to bracket the true reserves value while other methods fail to bracket it. The results show that even starting from a nonoptimal reservoir model, but as long as the experimental designs encompass the true case parameters, the integrated mismatch method brackets the true reserves value. If the experimental designs do not encompass all the true case parameters, but the true reserves value is covered by the experiments, the integrated mismatch method may still bracket the true case. This applies if there is a strong correlation between mismatch and closeness to the true reserves value. The integrated mismatch method does not need a large number of simulation runs for the uncertainty analysis, while some other methods need hundreds of runs.

5 
Control of systems subject to uncertainty and constraintsVillota Cerna, Elizabeth Roxana 15 May 2009 (has links)
All practical control systems are subject to constraints, namely constraints aris¬ing from the actuator’s limited range and rate capacity (input constraints) or from imposed operational limits on plant variables (output constraints). A linear control system typically yields the desirable small signal performance. However, the presence of input constraints often causes undesirable large signal behavior and potential insta¬bility. An antiwindup control consists of a remedial solution that mitigates the eﬀect of input constraints on the closedloop without aﬀecting the small signal behavior. Conversely, an override control addresses the control problem involving output con¬straints and also follows the idea that large signal control objectives do not alter small signal performance. Importantly, these two remedial control methodologies must in¬corporate model uncertainty into their design to be considered reliable in practice. In this dissertation, shared principles of design for the remedial compensation problem are identiﬁed which simplify the picture when analyzing, comparing and synthesiz¬ing for the variety of existing remedial schemes. Two performance objectives, each one related to a diﬀerent type of remedial compensation, and a general structural representation associated with both remedial compensation problems will be consid¬ered. The eﬀect of remedial control on the closedloop will be evaluated in terms of two general frameworks which permit the uniﬁcation and comparison of all known remedial compensation schemes. The diﬀerence systems describing the performance objectives will be further employed for comparison of remedial compensation schemes under uncertainty considerations and also for synthesis of compensators. On the ba¬sis of the diﬀerence systems and the general structure for remedial compensation, systematic remedial compensation synthesis algorithms for antiwindup and override compensation will be given and compared. Successful application of the proposed robust remedial control synthesis algorithms will be demonstrated via simulation.

6 
Uncertainty in the Global Mean for Improved Geostatistical ModelingVillalba Matamoros, Martha Emelly Unknown Date
No description available.

7 
Uncertainty in the Global Mean for Improved Geostatistical ModelingVillalba Matamoros, Martha Emelly 11 1900 (has links)
Analysis of uncertainty in ore reserves impacts investment decisions, mine planning and sampling. Uncertainty is evaluated by geostatistical simulation and is affected by the amount of data and the modeling parameters. Incomplete uncertainty is given because the parameter uncertainty is ignored. Also, greater spatial continuity leads to more uncertainty. This increase is unreasonable in earth science. To address these problems, two approaches are proposed. The first approach is based on multiGaussian simulation where many realizations are performed at translated and/or rotated configurations and conditioned to the data. Variable configurations give different mean values that define uncertainty. The second approach is based on a stochastic trend; this approach randomizes the trend coefficients accounting for the fitted coefficients correlation. Variable set of coefficients provide different mean values. Furthermore, a methodology to account for parameter uncertainty is proposed. The uncertainty in the mean is transferred through simulation to deliver a more complete uncertainty. / Mining Engineering

8 
Crossscale model validation with aleatory and epistemic uncertaintyBlumer, Joel David 08 June 2015 (has links)
Nearly every decision must be made with a degree of uncertainty regarding the outcome. Decision making based on modeling and simulation predictions needs to incorporate and aggregate uncertain evidence. To validate multiscale simulation models, it may be necessary to consider evidence collected at a length scale that is different from the one at which a model predicts. In addition, traditional methods of uncertainty analysis do not distinguish between two types of uncertainty: uncertainty due to inherently random inputs, and uncertainty due to lack of information about the inputs. This thesis examines and applies a Bayesian approach for model parameter validation that uses generalized interval probability to separate these two types of uncertainty. A generalized interval Bayes’ rule (GIBR) is used to combine the evidence and update belief in the validity of parameters. The sensitivity of completeness and soundness for interval range estimation in GIBR is investigated. Several approaches to represent complete ignorance of probabilities’ values are tested. The result from the GIBR method is verified using Monte Carlo simulations. The method is first applied to validate the parameter set for a molecular dynamics simulation of defect formation due to radiation. Evidence is supplied by the comparison with physical experiments. Because the simulation includes variables whose effects are not directly observable, an expanded form of GIBR is implemented to incorporate the uncertainty associated with measurement in belief update. In a second example, the proposed method is applied to combining the evidence from two models of crystal plasticity at different length scales.

9 
Preordeninge van teorieë geïnduseer deur semantiese informasieBurger, Isabella Cornelia 03 June 2014 (has links)
Ph.D. (Mathematics) / Please refer to full text to view abstract

10 
Numerical simulation of backward erosion piping in heterogeneous fieldsLiang, Yue, Yeh, TianChyi Jim, Wang, YuLi, Liu, Mingwei, Wang, Junjie, Hao, Yonghong 04 1900 (has links)
Backward erosion piping (BEP) is one of the major causes of seepage failures in levees. Seepage fields dictate the BEP behaviors and are influenced by the heterogeneity of soil properties. To investigate the effects of the heterogeneity on the seepage failures, we develop a numerical algorithm and conduct simulations to study BEP progressions in geologic media with spatially stochastic parameters. Specifically, the void ratio e, the hydraulic conductivity k, and the ratio of the particle contents r of the media are represented as the stochastic variables. They are characterized by means and variances, the spatial correlation structures, and the cross correlation between variables. Results of the simulations reveal that the heterogeneity accelerates the development of preferential flow paths, which profoundly increase the likelihood of seepage failures. To account for unknown heterogeneity, we define the probability of the seepage instability (PI) to evaluate the failure potential of a given site. Using MonteCarlo simulation (MCS), we demonstrate that the PI value is significantly influenced by the mean and the variance of ln k and its spatial correlation scales. But the other parameters, such as means and variances of e and r, and their cross correlation, have minor impacts. Based on PI analyses, we introduce a risk rating system to classify the field into different regions according to risk levels. This rating system is useful for seepage failures prevention and assists decision making when BEP occurs.

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