No abstract

No abstract

Conventional gradient methods have already been applied to reservoir engineering for matching the history of former field performances. The key point of these methods is to select the best areal reservoir zoning for reduction of the amount of reservoir parameters to be identified. In this paper we propose a zoning based on reservoir lithofacies, thus making a more natural than geographical choiee. By introducing gradients to numrmze an objective function that measures the difference between observed and simulated well pressure responses, we can effectively achieve the inversion of petrophysical lithofacies parameters. By fixing the value of petrophysical parameters, the influence of the geometry can be studied by varying the geologie body's dimensions. Several examples of inversion are given at the end of the artiele to illustrate the effectiveness of this gradient method.

No abstract

No abstract

Th,s papw was selected for presentatmn by an SPE Commmee Iolbwmg rewew 01 mfcfmaton contwned m an abstract submitted by the aulhor[ s). Cdents of Ihe papef, as presented, have not been reviewed by the %xtety of Petroleum Engmears and are subject to correction by the author(s) The matertal, as presenled does not necassary reflact any pesdmn of the Scmety of Petroleum Eng(neers, IIS oHlcels or membars. Papefs presanted at SPE mealings are subpct 10 publlcat$on review by Ed!tor!al Committals of the Soxty of Petroleum Engmears Parmmmon to copy IS restricted 10 an abslract of not more than 300 words. Illustrabon may not be copied The abstract should contain conspicuous acknwladgmenl o! where and by whom the papar was praaented Write L!branan, SPE,

This paper presents a new approach to constrain reservoir facies models to dynamic data. This approach is mainly based on the combination of an optimization method called the simplex method and parameterization techniques such as the gradual deformation method. This parameterization technique is well known for its efficiency in constraining geostatistical models to dynamic data. Originaly developped for continuous Gaussian models, the gradual deformation method was extended to facies models [12] and more recently to any kind of geostatistical model. However, the numerical behavior of the inversion can be strongly affected by discontinuities in the model changes in case of facies-based models. Our approach proposes a robust inversion method coupled with the gradual deformation method for the calibration of facies models. The methodology we present is directly inspired by the construction of a reservoir facies model. We start by generating a related Gaussian continuous geostatistical model. In a second phase this continuous model is transformed according to the number of facies and their proportions to obtain a consistent facies model. Since a direct parameterization of the facies model does not allow the preservation of the geological properties of the model, we perform the parameterization on the related continuous model and then we proceed to the model truncation. In this manner, we can optimize the deformation parameters involved in the parameterization using the simplex method to obtain an history match. The efficiency of this approach is illustrated by matching an interference test. The parameterization phase was performed using the gradual deformation method. Constrained facies models were thus obtained. A second phase of this study was devoted to quantifying the impact of uncertainty of spatial facies distribution on production forecasts. In an integrated process, we combine the simplex approach with experimental design theory and the joint modeling technique to achieve a probabilistic production forecast which takes into account the uncertainty on spatial facies distribution and classical reservoir parameters while preserving the history match. This application demonstrates the importance of the interference test matching in reducing the uncertainties on production forecasts.

Conventional gradient methods have already been applied to reservoir engineering for matching the history of former field performances. The key point of these methods is to select the best areal reservoir zoning for reduction of the amount of reservoir parameters to be identified. In this paper we propose a zoning based on reservoir lithofacies, thus making a more natural than geographical choiee. By introducing gradients to numrmze an objective function that measures the difference between observed and simulated well pressure responses, we can effectively achieve the inversion of petrophysical lithofacies parameters. By fixing the value of petrophysical parameters, the influence of the geometry can be studied by varying the geologie body's dimensions. Several examples of inversion are given at the end of the artiele to illustrate the effectiveness of this gradient method.