Well-Test-Model Recognition With Laplace Space

Bourgeois, Marcel; Horne, Roland N.

doi:10.2118/22682-pa

Cited by 36 publications

(19 citation statements)

References 18 publications

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“…The measurements are fully dimensional data, therefore to perform the well test parameter identification process in Laplace space we need to transform the fully dimensional data to Laplace space. This transformation is accomplished according to the algorithm presented by Bourgeois and Horne in [21]. Also according to Equation (8) …”

Section: Characterization In Laplace Spacementioning

confidence: 99%

“…From the filtered data and known field characteristics, we compute the corresponding dimensionless pressure data curve in Laplace domain according to the method presented in Bourgeois and Horne [21]. The dimensionless pressure data curve is computed for a set of 600 logarithmically spaced data points u i in interval 10 À8 ; 10 3 Â Ã .…”

Section: Real Field Casesmentioning

confidence: 99%

“…To avoid this inconvenience, some authors proposed to conduct the parameter identification process in Laplace space, see [20][21][22]. We will then perform the parameter identification in Laplace space, using the method proposed by Bourgeois and Horne [21] for the transformation of pressure data to Laplace space.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Well Test Analysis of Naturally Fractured Vuggy Reservoirs with an Analytical Triple Porosity – Double Permeability Model and a Global Optimization Method

Gómez¹,

Ramos²,

Mesejo

et al. 2014

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Geosciences Numerical Methods Modélisation numérique en géosciencesOil & Gas Science and Technology -Rev. IFP Energies nouvelles, Vol. 69 (2014), No. 4, Copyright © 2014, IFP Energies nouvelles > EditorialJ. E. Roberts > Modeling Fractures in a Poro-Elastic MediumUn modèle de fracture dans un milieu poro-élastique > Modeling Fluid Flow in Faulted BasinsModélisation des transferts fluides dans les bassins faillés Re´sume´-Analyse des puits d'essai de re´servoirs vacuolaires naturellement fracture´s avec un mode`le de triple porosite´-double perme´abilite´et une me´thode d'optimisation globale -L'objectif de ce travail est l'e´tude de la caracte´risation automatique de re´servoirs de pe´trole fracture´s ve´siculaires via l'analyse des puits d'essai, avec un mode`le de triple porosite´et de perme´abilite´double. Les parame`tres qui doivent eˆtre identifie´s comme ceux du mode`le, sont l'e´coulement entre les trois media, les ratios de stockage, la porosite´, le ratio de perme´abilite´, l'effet de stockage, la perme´abilite´de peau et totale. Dans ce travail, nous avons effectueĺ 'interpre´tation de l'essai dans l'espace de Laplace, en utilisant des algorithmes nume´riques pour transfe´rer les donne´es obtenues dans un espace de temps complet en un espace de Laplace. Le proble`me de l'interpre´tation des tests dans l'espace de Laplace est pose´comme un proble`me d'optimisation des moindres carre´s non line´aires avec boıˆte contrainte et ine´galite´line´aire contrainte, ce qui est ge´ne´ralement re´solu en utilisant des me´thodes de type Newton avec une re´gion de confiance. Cependant, les me´thodes locales comme celle utilise´e dans notre travail appele´e TRON, ou la me´thode bien connue de Levenberg-Marquardt, ne sont pas souvent en mesure de trouver une solution optimale avec un bon ajustement des donne´es. É galement la caracte´risation automatique des re´servoirs du pe´trole fracture´s vacuolaires via l'analyse des puits d'essai avec le mode`le de porosite´-perme´abilite´a`double triple, peut, comme la plupart des proble`mes inverses, donner des solutions multiples avec une bonne corre´lation des donne´es. Pour faire face a`ces spe´cificite´s, nous avons utilise´une approche avec la me´thode d'optimisation globale appele´e Tunneling Method (TM). Dans l'adaptation de l'algorithme, nous prenons en compte des proble`mes comme le fait que le l'estimation des parame`tres doit eˆtre formule´e avec une grande pre´cision, comme la pre´sence de bruit dans les mesures et comme la ne´cessite´de re´soudre le proble`me de calcul rapidement. Nous de´montrons dans cette e´tude que l'utilisation de TM est une alternative efficace et robuste pour re´soudre la caracte´risation des puits d'essai, du fait que plusieurs solutions avec un bon ajustement aux donne´es aient e´te´obtenues.Oil & Gas Science and Technology -Rev. IFP Energies nouvelles, Vol. 69 (2014), No. 4, pp. 653-671 Copyright Ó 2014, IFP Energies nouvelles DOI: 10.2516 Abstract -Well Test Analysis of Naturally Fractured Vuggy Reservoirs with an Analytical Triple P...

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Section: Characterization In Laplace Spacementioning

confidence: 99%

Section: Real Field Casesmentioning

confidence: 99%

See 1 more Smart Citation

Well Test Analysis of Naturally Fractured Vuggy Reservoirs with an Analytical Triple Porosity – Double Permeability Model and a Global Optimization Method

Gómez¹,

Ramos²,

Mesejo

et al. 2014

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…Such forward Laplace transform algorithms have been described in the literature ͑Guillot and Horne, 7 Roumboutsos and Stewart, 8 and Bourgeois and Horne 9 ͒. For this study, we used the algorithm presented by Bourgeois and Horne,9 in which the Laplace transform is computed as…”

Section: Mathematical Preliminariesmentioning

confidence: 99%

Analysis of Pressure/Flow-Rate Data With the Pressure-History-Recovery Method

Gilly

Horne²

1999

SPE Reservoir Evaluation &Amp; Engineering

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This study presents a new way to integrate flow rate history and pressure history data in well-test analysis. The method used is an extension of the deconvolution principle to a chosen period of time before the well test. It is based on the recovery of missing parts of the pressure history by taking into account the flow rate history and the available sections of measured pressure history. The method has the advantage of working without making any assumption about the reservoir by using the data itself to define the ''model.'' A real example with 71 pressure points and 70 corresponding flow rate points shows how it is possible to recover the first 20 pressure points correctly considering that only the flow rate history and the last 51 pressure points are known.The main purpose of this procedure is to have an alternative to treat the problem of flow rate variations both before and during the well test. In such a case, neither the usual deconvolution ͑only applicable for flow rate variations during the well test, when flow rate and pressure are both known͒, nor the multirate superposition plots ͑which assume specific reservoir behavior͒ can be applied properly. In addition, the principle of the method enables us to extend its use to the analysis of well tests with pressure recording errors or missing pressure recording. Examples show that it is possible to make a good analysis of a well test with missing pressure regions when current methods give only parts of the information.

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“…Bourgeois and Horne 14 provided an algorithm derived from Romboutsos and Stewart'sl6 earlier procedure, which overcomes much of the extrapolation difficulty.…”

Section: Deconvolutionmentioning

confidence: 99%