Combining the gradual deformation method with seismic forward modeling to constrain reservoir models

2008 ◽  
Author(s):  
Audrey Neau ◽  
Pierre Thore ◽  
Béatrice de Voogd
Keyword(s):  
Forward Modeling ◽  
Deformation Method ◽  
Reservoir Models ◽  
Gradual Deformation

Two-level uncertainty assessment in stochastic seismic inversion based on the gradual deformation method

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. M33-M42
Author(s):  
Xiuwei Yang ◽  
Ningbo Mao ◽  
Peimin Zhu ◽  
Dan Xiao
Keyword(s):  
Correlation Length ◽  
Moving Average ◽  
Random Noise ◽  
Seismic Inversion ◽  
Deformation Method ◽  
Correlation Lengths ◽  
Noise Field ◽  
Reservoir Models ◽  
Gradual Deformation ◽  
Well Data

Geostatistical seismic inversion can combine seismic data, well data, and spatial continuity of the property of interest to obtain high-resolution reservoir models and evaluate uncertainties. Some workflows estimate global geostatistical parameters, such as correlation length, and keep them fixed in all simulations and inversions. This can introduce biases due to the sparsity of available well data and underestimate the uncertainty of inversion. A better approach is to incorporate the uncertainty in these global parameters. Lateral correlation length is one of the most difficult parameters to estimate. We have developed a seismic inversion method based on local gradual deformation method, which incorporates the uncertainty of lateral correlation length and provides a two-level uncertainty evaluation. We first estimate a uniform prior distribution of lateral correlation length from well data and additional geologic expert knowledge. After using fast Fourier transform (FFT) moving average simulations and local gradual deformation optimization, we obtain multiple realizations from which we could extract the lateral correlation lengths and calculate their posterior distribution. The FFT moving average method generates reservoir models by a convolution between a filter operator and a random noise field. The filter operator does not change during inversion, and the correlation structure of the random noise field could be changed by the local gradual deformation method to match the seismic data. A synthetic model test shows that the correlation lengths and the global probability distribution of the inverted results tend to the true geostatistical characteristics. The posterior distribution of the lateral correlation length narrows after inversion. Compared with conventional geostatistical seismic inversion techniques, uncertainties in the results increase because we incorporate the uncertainty in the global parameters. A real case also demonstrated that by modifying the random noise field locally, thin layers in a thick formation are well restored, even if they are not interpreted in advance.

An Integrated Methodology for Calibrating a Heterogeneous/Fractured Reservoir Model From Wellbore Flow Measurements: Case Study

2009 ◽  
Vol 12 (03) ◽  
pp. 433-445 ◽  
Author(s):  
Hamid Pourpak ◽  
Bernard J. Bourbiaux ◽  
Frédéric Roggero ◽  
Frederick Delay
Keyword(s):  
Fractured Reservoirs ◽  
Inversion Method ◽  
Petrophysical Properties ◽  
Flow Modeling ◽  
Flow Data ◽  
Reservoir Model ◽  
Deformation Method ◽  
Flow Measurements ◽  
Heterogeneous Reservoirs ◽  
Gradual Deformation

Summary Reliable flow modeling of highly heterogeneous/fractured reservoirs necessarily goes through the calibration of poorly determined geological and/or petrophysical parameters to field flow measurements. To that end, optimization procedures based on gradient methods or on gradual-deformation techniques have been developed in recent years. This paper proposes a sequential method combining those two approaches. The case under consideration is a water-bearing reservoir constituted of heterogeneous, karstic and fractured limestones located near Poitiers, France. In a preliminary step, drilling, core, and log data acquired in approximately 30 wells were integrated into a geostatistical facies model used as the support for flow simulation. First the facies petrophysical properties of this model were calibrated to well pumping and interference responses within a gradient-based inversion loop. Flow responses could be reproduced, with the exception of a few "problematic" observation wells. Second the gradual-deformation method was applied, globally then locally, to improve the distribution of facies while keeping the previously optimized petrophysical properties. The problematic wells' responses could be reproduced better without altering the other wells' match. Furthermore, that good match of calibration wells was obtained on a simplified geostatistical model involving fewer facies than in the initial model. The gradual-deformation method then appears as a robust and effective approach to find a model best matching a set of flow data among equiprobable geostatistical models. To conclude, the sequential-modeling method demonstrated herein is an effective way to actually integrate geological and flow data and to link geosciences and reservoir-engineering skills, for setting up consistent models of hardly tractable highly heterogeneous reservoirs. Introduction During the past 20 years, the technique of mathematical modeling has been used extensively in the study of groundwater-resources management and aquifer remediation (Sun 1994). Concern was especially focused on fluid transfer in heterogeneous and/or fractured reservoirs. That resulted in conclusive advances in the characterization and modeling of fractured reservoirs (Cacas et al. 2001). Actually, wellbore information on underground reservoir heterogeneities and fractures (e.g., core descriptions, image logs, and production profiles) is now used to condition the geostatistical pixel-based models or the object-based stochastic models of these reservoirs and to calibrate the hydraulic properties of major flow heterogeneities such as fractures (Sarda et al. 2002). However, the problem of reservoir-model construction remains highly undetermined. The purpose of this paper is to design and validate an inversion method for calibrating the poorly defined flow models of highly heterogeneous reservoirs to wellbore dynamic data. The design and the application of that method are performed on an experimental hydrogeological site (EHS) settled on a karstic and fractured limestone aquifer located near Poitiers, France. The flow model is based on a geostatistical distribution of facies. The method involves two successive steps:the inversion of facies petrophysical properties andthe gradual deformation of the facies distribution. The resulting model is shown to predict well responses effectively. Finally, the possibility of further calibration improvement is investigated by means of alternative flow-modeling approaches, such as the use of a dual-porosity model or a more accurate modeling of conductive bodies.

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