Event-Targeting Model Calibration Used for History Matching Large Simulation Cases

Schulze-Riegert, Ralf; Selberg, Stig

doi:10.2118/106044-ms

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…The results can be visualized by plotting the contours of the misfit as a function of f g and f o for the North Jens and Bo areas as shown in Figure 17. Observe that there are multiple local minima of the misfit, which is typical for the solution of inverse problems in reservoir simulation [29,33,34]. With the choice for the initial values of f g = 0.1 and f o = 0.05 in Section 6, the Nelder-Mead iterations are unable to move closer to the values f g ≈ 0.2 and f o ≈ 1, which might minimize the misfit further, as suggested by Table 1.…”

Section: Characterizing the Misfit Using The Design Of Experiments Ap...mentioning

confidence: 99%

Impact of Gas Liberation Effects on the Performance of Low Permeability Reservoirs

et al. 2022

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Production from the North Sea reservoirs often results in a pressure decrease below the bubble point. The gas is liberated from oil, in the form of bubbles or as a continuous flowing phase. In such cases, the two phases, gas and oil, flow in the reservoir simultaneously, and the flow is governed by the values of relative permeabilities. Traditional core flooding in low permeability rocks is challenging, therefore we use a novel experimental approach to determine the oil relative permeabilities below the critical gas saturation. A mathematical model has been created to reconstruct both the gas and the oil relative permeabilities for the whole saturation range. Laboratory observations have shown that in low-permeable rocks the relative permeabilities may strongly decrease, even when the amount of the liberated gas is small. The goal of this work is to verify, on a specific example, whether the designed model for the relative permeabilities may explain the observed production behavior for a low-permeable chalk reservoir in the North Sea. We perform a sensitivity study using the parameters of relative permeabilities and analyze the corresponding differences in well productivities. A reasonably good match of <10% can be obtained to the historical well production data. A few cases where the match was not satisfactory (14% to 65%) are also analyzed, and the difference is attributed to the imprecise fluid model. The developed experimental and modeling methodology may be applied to other reservoirs developed by the solution gas drive mechanism.

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Section: Characterizing the Misfit Using The Design Of Experiments Ap...mentioning

confidence: 99%

Impact of Gas Liberation Effects on the Performance of Low Permeability Reservoirs

et al. 2022

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Using Experimental Designs, Assisted History Matching Tools and Bayesian Framework to get Probabilistic Production Forecasts

Schaaf

Coureaud

Labat

2008

Europec/Eage Conference and Exhibition

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One of the main concern in the O&G business is generating reliable production profile forecasts. Such profiles are the cornerstones of optimal technico-economical management decisions. A workflow combining different methodologies to integrate and reduce most of the subsurface uncertainties using multiple history matched models (explaining the past) to infer reasonably reliable production forecasts is proposed. Using experimental design theory, a sensitivity study is first performed to scan the whole range of static and dynamic uncertain parameters using a proxy-model of the fluid flow simulator. Only the most sensitive ones with respect to an objective function (quantifying the mismatch between the simulation results and the observations) are retained for subsequent steps. Assisted History Matching tools are then used to get multiple History matched models, an order of magnitude faster than traditional History Matching processes. Updated uncertain parameters (selected from the sensitivity studies) may be picked anywhere in the direct problem building workflow. Using the Bayesian framework, a posterior distribution of the most sensitive parameters are derived from the a priori distributions and a non-linear proxy model of the likelihood function. The later is computed using experimental design, kriging and dynamic training techniques. Multiple History Matched models together with a posteriori parameter distributions are finally used in a joint modeling approach to capture the main uncertainties and to obtain typical (P10-P90) probabilistic production profiles. This workflow has been applied to a gas storage real case submitted to significant seasonal pressure variations. Probabilistic operational pressure profiles for a given period can then be compared to the actual gas storage dynamic behaviour to assess the added value of the proposed workflow. Introduction Getting probabilistic production forecasts of a reservoir through a risk analysis is closely linked to uncertainty quantification 1. Uncertainty quantification should end with a posteriori uncertain parameter distributions, reflecting all the knowledge that we have on the reservoir and explaining as much as possible the observation data. The uncertainties may result both from the observation data quality as well as from the numerical modeling steps. On top of that, the fluid flows equations we are dealing with are non linear, which leads to possible complex hydrodynamical behaviour. CPU constraints make inefficient brute force Monte Carlo sampling of the a posteriori distributions from a priori ones. Most of the current approaches try to scan more efficiently (in a given timeframe) the uncertain parameters space using reliable proxy models 2 of the fluid flow simulator and/or to take advantage of new computational power 3,4,5,6. Due to the numerous uncertain parameters involved in the history matching process, a screening of the parameters space is first performed (i.e. a sensitivity study) to retain only the most sensitive ones with respect to the history matching criteria, possibly reducing the variation intervals. This is a pre-processing step of the history matching job.

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Using Experimental Designs, Assisted History-Matching Tools, and Bayesian Framework To Get Probabilistic Gas-Storage Pressure Forecasts

Schaaf¹,

Coureaud²,

Labat³

et al. 2009

SPE Reservoir Evaluation &Amp; Engineering

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Summary One of the main concerns in the oil and gas business is generating reliable reservoir hydrodynamics forecasts. Such profiles are the cornerstones of optimal technico-economical management decisions. A workflow combining different methods to integrate and reduce most of the subsurface uncertainties using multiple history matched models (explaining the past) to infer reasonably reliable forecasts is proposed. A sensitivity study is first performed using experimental design to scan the whole range of static and dynamic uncertainty parameters using a proxy model of the fluid-flow simulator. Only the most sensitive ones with respect to an objective function (OF) (quantifying the mismatch between the simulation results and the observations) are retained for subsequent steps. Assisted history-matching tools are then used to obtain multiple history-matched models. To obtain probabilistic pressure profiles, multiple history-matched models are combined with the uncertain parameters not retained in the sensitivity study, using the joint modeling method. Another way to constrain uncertain parameters with observation data is to use Bayesian framework where a posteriori distributions of the input parameters are derived from the a priori distributions and the likelihood function. The latter is computed through a nonlinear proxy model using experimental design, kriging, and dynamic training techniques. These two workflows have been applied to a real gas storage case submitted to significant seasonal pressure variations. The obtained probabilistic operational pressure profiles for a given period are then compared to the actual gas storage dynamic behavior so that we can compare the two approaches and assess the added value of both proposed workflows.

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Event-Targeting Model Calibration Used for History Matching Large Simulation Cases

Cited by 4 publications

References 13 publications

Impact of Gas Liberation Effects on the Performance of Low Permeability Reservoirs

Impact of Gas Liberation Effects on the Performance of Low Permeability Reservoirs

Using Experimental Designs, Assisted History Matching Tools and Bayesian Framework to get Probabilistic Production Forecasts

Using Experimental Designs, Assisted History-Matching Tools, and Bayesian Framework To Get Probabilistic Gas-Storage Pressure Forecasts

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