Prospect Identification Using AVO Inversion and Lithology Prediction

Barclay, Frazer; Bailey, Brad; Paxton, Andrea

doi:10.3997/2214-4609.201400888

72nd EAGE Conference and Exhibition Incorporating SPE EUROPEC 2010 2010

DOI: 10.3997/2214-4609.201400888

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Prospect Identification Using AVO Inversion and Lithology Prediction

Frazer Barclay¹,

Brad Bailey²,

Andrea Paxton³

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2014

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Using Stochastic Seismic Inversion as Input for 3D Geomechanical Models

et al. 2014

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The dual issues of band-limited vertical resolution and nonuniqueness of deterministic inversion results has led to the development of methodologies known as geostatistical, or stochastic, inversion. In these approaches, seismic data are typically inverted directly into a high-resolution geological model. Compared to deterministic inversion, stochastic methods deliver multiple realizations that are consistent with the available well and seismic data. The seismic inversion process is inherently nonunique, meaning that there is an unbounded number of elastic property models that fit the seismic data equally well above some threshold misfit. We explore the notion of the equally large number of possible stress states that could be interpreted from same seismic observations. We make use of stochastic inversion results to incorporate the impact of subseismic uncertainty in seismic-driven geomechanical models. By taking multiple realizations from a prestack stochastic inversion-acoustic impedance, V p /V s , and density-we generate and feed a series of distributions of elastic constants into a finite element stress simulator. The multiple stress solutions allow us to account for uncertainties in the inversion results that can be ultimately captured in a suite of numerical models to predict a set of possible geomechanical states of a field. Therefore, beyond a unique geomechanical forecast for a field, we can now solve for the range of variability in geomechanically safe operational parameters within the field's development plan.

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Using Stochastic Seismic Inversion as Input for 3D Geomechanical Models

et al. 2014

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Prospect Identification Using AVO Inversion and Lithology Prediction

Cited by 1 publication

References 2 publications

Using Stochastic Seismic Inversion as Input for 3D Geomechanical Models

Using Stochastic Seismic Inversion as Input for 3D Geomechanical Models

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