Estimating petrophysical parameters and average mud-filtrate invasion rates using joint inversion of induction logging and pressure transient data

Liang, Lin; Abubakar, Aria; Habashy, Tarek M.

doi:10.1190/1.3541963

Cited by 26 publications

(4 citation statements)

References 30 publications

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“…The flowchart for the forward-modeling workflow is shown in Figure 1. A similar workflow has also been used for single-well applications as referred to Liang et al (2011).…”

Section: Forward Modelingmentioning

confidence: 98%

Joint inversion of controlled-source electromagnetic and production data for reservoir monitoring

2012

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We present a fluid-flow constrained inversion approach for integrating controlled-source electromagnetic data and production data. In this approach, we assumed that the reservoir model has been well defined from a priori knowledge obtained from other independent measurements such as seismic and/or well-logs. Our objective was to reconstruct the permeability distribution and the shape and location of the flooding front. A finite-difference reservoir simulator was used to model the water-flooding process to simulate the time-dependent production data as well as the temporal and spatial distributions of water saturation and salt concentration, which were then transformed into the reservoir resistivity distribution using a petrophysical relationship. A finite-difference frequency-domain electromagnetic forward-modeling code was then employed to simulate the controlled-source electromagnetic response. The permeability distribution was reconstructed using a multiplicativeregularized Gauss-Newton algorithm for jointly inverting controlled-source electromagnetic and production data. From the water saturation distribution, we can identify the shape and location of the fluid-front resulting from the recovery process.

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“…The flowchart for the forward-modeling workflow is shown in Figure 1. A similar workflow has also been used for single-well applications as referred to Liang et al (2011).…”

Section: Forward Modelingmentioning

confidence: 98%

Joint inversion of controlled-source electromagnetic and production data for reservoir monitoring

2012

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“…As the synthetic case study, we use simple harmonic and arithmetic averaging methods for mapping properties between different grids. More details on the joint forward-modeling workflow can be found in Liang et al (2011a).…”

Section: Modeling and Inversionmentioning

confidence: 99%

Simultaneous estimation of permeability and porosity from joint inversion of time-lapse seismic, electromagnetic, and production data

Liang

Abubakar

Habashy

2013

SEG Technical Program Expanded Abstracts 2013

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We present an approach for the simultaneous estimation of permeability and porosity from time-lapse seismic, time-lapse electromagnetic, and production data. Permeability and porosity are some of the most important parameters for reservoir characterization because of their significant influence over oil recovery. Both permeability and porosity has a big footprint on the distribution of saturation and pressure in the reservoir during production, which can be snapshot by the time-lapse seismic and time-lapse electromagnetic measurements. Production data is the traditional source used for reservoir model conditioning but only provides limited constraint to the reservoir model that is very localized to the near-wellbore regions. In contrast, time-lapse seismic and time-lapse electromagnetic can impose additional constraints to the reservoir model conditioning problem covering the inter-well regions. We have developed a reservoir centric workflow to allow jointly invert these multiphysical data. The non-uniqueness of this inverse problem is hence effectively reduced. We use a synthetic case to demonstrate that the proposed approach can significantly improve the estimation of permeability and porosity as well as the fluid-front movement monitoring.

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“…Inversion methods for induction logging tools typically consider either parametric or pixel-based approaches (Liang et al, 2011). The former is usually preferred since the number of parameters to be optimized is significantly smaller compared to the pixel-based inversion.…”

Section: Introductionmentioning

confidence: 99%

Recent advances on the inversion of deep directional borehole resistivity measurements

Puzyrev

Pardo

Calo

et al. 2019

ASEG Extended Abstracts

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Estimating petrophysical parameters and average mud-filtrate invasion rates using joint inversion of induction logging and pressure transient data

Cited by 26 publications

References 30 publications

Joint inversion of controlled-source electromagnetic and production data for reservoir monitoring

Joint inversion of controlled-source electromagnetic and production data for reservoir monitoring

Simultaneous estimation of permeability and porosity from joint inversion of time-lapse seismic, electromagnetic, and production data

Recent advances on the inversion of deep directional borehole resistivity measurements

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