Calculating Optimal Parameters for History Matching

Bissell, Robert

doi:10.3997/2214-4609.201411181

Cited by 77 publications

(46 citation statements)

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“…Some of the reparameterization techniques applied in history matching to achieve this include -Zonation- [20,23], and adapted versions thereof- [6,18] -Grad zones- [7][8][9] -Spectral decomposition and subspace methods- [1,30,32] -Kernel principle component analysis- [31] -Discrete cosine transform- [21,22] Despite all of these applications, it is not clear how many parameters can be uniquely identified for any particular reservoir model.…”

Section: History Matching and Identifiabilitymentioning

confidence: 99%

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Controllability, observability and identifiability in single-phase porous media flow

et al. 2008

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Over the past few years, more and more systems and control concepts have been applied in reservoir engineering, such as optimal control, Kalman filtering, and model reduction. The success of these applications is determined by the controllability, observability, and identifiability properties of the reservoir at hand. The first contribution of this paper is to analyze and interpret the controllability and observability of single-phase flow reservoir models and to investigate how these are affected by well locations, heterogeneity, and fluid properties. The second contribution of this paper is to show how to compute an upper bound on the number of identifiable parameters when history matching production data and to present a new method to regularize the history matching problem using a reservoir's controllability and observability properties. Keywords

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Section: History Matching and Identifiabilitymentioning

confidence: 99%

“…[7][8] with N u control inputs (i.e., controlled flow rates or bottom-hole pressures) and N y outputs (i.e., measured flow rates or bottom-hole pressures), the controllability matrix C k and observability matrix O k are defined as follows:…”

Section: Controllability and Observabilitymentioning

confidence: 99%

Controllability, observability and identifiability in single-phase porous media flow

et al. 2008

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“…This algorithm was proposed by Bissel [2] and tries to address the problem of parameter selection in a history-matching process. Note that, in principle, the values of the petrophysical properties in each grid cell can be treated as parameters to be determined, which makes the number of potential parameters much larger than the number of observations.…”

Section: Description Of the Gradzone Proceduresmentioning

confidence: 99%

“…To show how this framework for derivative calculation can be useful, even outside the context of optimization algorithms, it is discussed herein a new variant of the gradzone algorithm, proposed by Bissel [2] for grouping cells into regions for history-matching purposes. The singular value decomposition of the sensitivity matrix is used, instead of the eigenstructure of the GaussYNewton approximation to the Hessian of the objective function.…”

Section: Introductionmentioning

confidence: 99%

Calculating derivatives for automatic history matching

Rodrigues

2006

Comput Geosci

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Automatic history matching is based on minimizing an objective function that quantifies the mismatch between observed and simulated data. When using gradient-based methods for solving this optimization problem, a key point for the overall procedure is how the simulator delivers the necessary derivative information. In this paper, forward and adjoint methods for derivative calculation are discussed. Procedures for sensitivity matrix building, sensitivity matrix and transpose sensitivity matrix vector products are fully described. To show the usefulness of the derivative calculation algorithms, a new variant of the gradzone analysis, which tries to address the problem of selecting the most relevant parameters for a history matching, is proposed using the singular value decomposition of the sensitivity matrix. Application to a simple synthetic case shows that this procedure can reveal important information about the nature of the history-matching problem.

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“…These artificial data are new constraints superimposed on the history matching problem. The values of these constraints, which are initially unknown, must be adjusted until a minimum or a maximum is obtained at t = t i-The criterion to be minimized can be defined by a new term Fj incIuded in the objective function definition: (16) where:…”

Section: Production Forecasting Criterionmentioning

confidence: 99%

Gradient Method and Bayesian Formalism Application to Petrophysical Parameter Characterization

Roggero¹,

Guérillot²

1996

ECMOR v - 5th European Conference on the Mathematics of Oil Recovery

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Numerical models are routinely used today to analyze the performance of hydrocarbon reservoirs. However, the fit of the historical data has to take into account the initial geological knowledge to provide physical production forecasts, even if reservoir parameters are inherently uncertain over large parts of a field.This artiele proposes a methodology to obtain an improved grid representation of the geological parameters and to quantify uncertainties after history matching. The goal is to obtain a physically matched model, taking into account the a priori knowledge of the reservoir. The proposed method is based on the gradient method coupled with an efficient optimization algorithm.An objective function defined as an extension of the least squares technique is introduced as a history matching criterion. A priori information on the parameters is integrated through Gaussian probability density functions. During history matching. a statistical arialysis based on the Bayesian formalism provides a posteriori information on the parameters with reduced uncertainties.For quantifying the uncertainties on production forecasts. an algorithm is used to select directly. from all the possible realizations. extreme behaviour modeis. A production forecasting criterion is introduced to constrain the model in order to produce extreme forecasts belonging to a given probability domain.

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Calculating Optimal Parameters for History Matching

Cited by 77 publications

References 0 publications

Controllability, observability and identifiability in single-phase porous media flow

Controllability, observability and identifiability in single-phase porous media flow

Calculating derivatives for automatic history matching

Gradient Method and Bayesian Formalism Application to Petrophysical Parameter Characterization

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