Finding a Petro-elastic Model Suitable for Sim2seis Calculation

Amini, Hamed; Alvarez, Erick; MacBeth, Colin; Shams, Asghar

doi:10.3997/2214-4609.20148818

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Synthetic seismic acoustic impedance data are created using a "true" petro-elastic model based on the Gassmann's equations [26,62], the Hashin-Shtrikman bound models [27] for a mixture of sands and shales, and the fluid and mineral parameters proposed for the Norne Field [12]. For the Hashin-Shtrikman bounds, we assume that the volume of shale in each of the active cells in the model is given by 1-NTG [3]. The true PEM Fig.…”

Section: Norne Field Examplementioning

confidence: 99%

Seismic data assimilation with an imperfect model

Alfonzo

Oliver

2019

Comput Geosci

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Data assimilation methods often assume perfect models and uncorrelated observation error. The assumption of a perfect model is probably always wrong for applications to real systems, and since model error is known to generally induce correlated effective observation errors, then the common assumption of uncorrelated observation errors is probably almost always wrong, too. The standard approach to dealing with correlated observation errors, which simply ignores the correlation, leads to suboptimal assimilation of observations. In this paper, we examine the consequences of model errors on assimilation of seismic data. We show how to recognize the existence of correlated error through model diagnostics modified for large numbers of data, how to estimate the correlation in the error, and how to use a model with correlated errors in a perturbed observation form of an iterative ensemble smoother to improve the quantification of a posteriori uncertainty. The methodologies for each of these aspects have been developed to allow application to problems with very large number of model parameters and large amounts of data with correlated observation error. We applied the methodologies to a small toy problem with linear relationship between data and model parameters, and to synthetic seismic data from the Norne Field model. To provide a controlled investigation in the seismic example, we investigate an application of data assimilation with two sources of model error-errors in seismic resolution and errors in the petro-elastic model. Both types of model errors result in effectively correlated observation errors, which must be accounted for in the data assimilation scheme. Although the data are synthetic, parameters of the seismic resolution and the observation noise are estimated from the actual inverted acoustic impedance data. Using a structured approach, we are able to assimilate approximately 115,000 observations with correlated total observation error efficiently without neglecting correlations. We show that the application of this methodology leads to less overfitting to the observations, and results in an ensemble estimate with smaller spread than the initial ensemble of predictions, but that the final estimate of uncertainty is consistent with the truth.

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Section: Norne Field Examplementioning

confidence: 99%

Seismic data assimilation with an imperfect model

Alfonzo

Oliver

2019

Comput Geosci

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“…Numerical computation determines that an offset of 9 km and frequency of 0.2 Hz gives an optimal response for the particular reservoir and overburden structure under consideration. For comparative purposes, seismic modelling is also performed on the same model using the work of Amini et al (2012), which incorporates a calibrated petroelastic model for the reservoir and convolutional modelling to generate a seismic data volume.…”

Section: The Composite Resistivity Of Rock and Fluidsmentioning

confidence: 99%

Potential applications of time-lapse CSEM to reservoir monitoring

Salako¹,

MacBeth²,

MacGregor³

2015

First Break

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“…Recently, Salako et al (2012) considered a practical simulator to CSEM tool driven by the 1D dipole method of Key (2009), which also accounted for variable net-to-gross in the reservoir. This permitted a direct comparison with simulator to seismic methods, (described by Amini et al, 2012), regarding the relative sensitivities of water saturation detection, particularly in the presence of variable reservoir thickness, net-to-gross and porosity.…”

Section: Introductionmentioning

confidence: 99%

Potential Applications of Time-lapse Marine CSEM to Reservoir Monitoring

Salako¹,

MacBeth²,

MacGregor³

et al. 2013

London 2013, 75th Eage Conference en Exhibition Incorporating SPE Europec

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Three different oil field production settings are identified where time-lapse marine CSEM could find reservoir monitoring application. One of these settings, low salinity water injection, is given particular attention in this work. We generate time-lapse CSEM amplitude data from a 3D fluid flow simulation model by converting the dynamic changes to resistivity changes, and then employing 1D-dipole forward modelling for an in-line acquisition geometry. We utilise a rock physics model that accounts for heterogeneity, and takes into consideration the time variations in the salinity and temperature profile to calculate the vertical resistivity for each simulator cell. The modelling uses as a guide a full field model for a North sea turbidite reservoir into which low salinity water is injected. Measuring the three amplitude components of the CSEM survey, it is concluded that time lapse CSEM has the potential to be used qualitatively to monitor the evolution of low salinity water injection.

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Finding a Petro-elastic Model Suitable for Sim2seis Calculation

Cited by 3 publications

References 4 publications

Seismic data assimilation with an imperfect model

Seismic data assimilation with an imperfect model

Potential applications of time-lapse CSEM to reservoir monitoring

Potential Applications of Time-lapse Marine CSEM to Reservoir Monitoring

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