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Abstract

Summary

We implement an ensemble based petrophysical parameter inversion framework to estimate static as well as dynamic reservoir/ petrophysical parameters such as saturations, pressure and / or porosity fields using seismic data. Here, we consider acoustic impedance (Ip) data as the seismic data. The suggested approach is solved as a Bayesian inversion problem where the prior is provided as an ensemble of pressure-saturation and porosity fields. Here, the realizations of porosity and permeability fields of the prior model are generated using geostatistical methods and are further used in a reservoir simulator to obtain the realizations of pressures-saturations fields at the time of the seismic acquisition. The pressure-saturations and porosities are then changed to account for the information available from acoustic impedances using an iterative ensemble smoother. The outcome is a new ensemble of pressure-saturation and porosity fields that honor the seismic data.

The new approach differs from conventional deterministic petrophysical parameter inversion algorithms using seismic data by being stochastic, and more importantly, it pays more attention to the uncertainty quantification. Our results show that the suggested ensemble-based method is suitable to handle the nonlinear inverse problem and has the capacity of providing quantification of the uncertainty of the result.

We apply the proposed framework to a field-like 3D synthetic reservoir model, based on a compacting field scenario. The reservoir model consists of three fluid phases (water, oil and gas), and exhibits production related compaction. The numerical results from study indicates that the proposed framework can integrate the reservoir-engineering data as prior knowledge with the seismic data, achieving reasonable estimates of both the static and dynamic reservoir parameters.

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/content/papers/10.3997/2214-4609.201802143
2018-09-03
2024-04-24

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Petrophysical Parameters Inversion From Seismic Data Using An Ensemble-Based Method - A Case Study From A Compacting Reservoir
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201802143
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