Elastic-reflection waveform inversion with petrophysical model constraints

Geophysical Prospecting

2020

Self Cite

A B S T R A C TFull waveform inversion aims to use all information provided by seismic data to deliver high-resolution models of subsurface parameters. However, multiparameter full waveform inversion suffers from an inherent trade-off between parameters and from ill-posedness due to the highly non-linear nature of full waveform inversion. Also, the models recovered using elastic full waveform inversion are subject to local minima if the initial models are far from the optimal solution. In addition, an objective function purely based on the misfit between recorded and modelled data may honour the seismic data, but disregard the geological context. Hence, the inverted models may be geologically inconsistent, and not represent feasible lithological units. We propose that all the aforementioned difficulties can be alleviated by explicitly incorporating petrophysical information into the inversion through a penalty function based on multiple probability density functions, where each probability density function represents a different lithology with distinct properties. We treat lithological units as clusters and use unsupervised K-means clustering to separate the petrophysical information into different units of distinct lithologies that are not easily distinguishable. Through several synthetic examples, we demonstrate that the proposed framework leads full waveform inversion to elastic models that are superior to models obtained either without incorporating petrophysical information, or with a probabilistic penalty function based on a single probability density function. [Correction added on 24 January 2020, after first online publication: the word "wavefield" in the title has been replaced with "waveform" in this version.]

Section: Introductionmentioning

confidence: 99%

Elastic full waveform inversion with probabilistic petrophysical clustering

Geophysical Prospecting

2020

Self Cite

“…Manukyan et al (2018) constrain the inversion by assuming that the elastic parameters have structural similarity and thus mitigate parameter tradeoff problems and the different spatial resolution of elastic parameters. Duan and Sava (2016) and Rocha and Sava (2018) use a logarithmic penalty function to constrain, respectively, elastic wavefield tomography and elastic reflection waveform inversion, assuming a general linear relationship between the model parameters. Aragao and Sava (2018) apply probabilistic model constraints, by explicitly using petrophysical information during the inversion in order to recover models that honor both geophysical and petrophysical data.…”

Section: Introductionmentioning

confidence: 99%

Elastic wavefield tomography with probabilistic petrophysical clustering

SEG Technical Program Expanded Abstracts 2019

2019

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Full waveform inversion (FWI) aims to use all information provided by seismic data to deliver high resolution models of subsurface parameters. However, multiparameter FWI suffers from an inherent trade-off between parameters and from ill-posedness due to the highly nonlinear nature of FWI. Also, the models recovered using elastic FWI are subject to local minima if the initial models are far from the optimal solution. In addition, an objective function purely based on the misfit between recorded and modeled data may honor the seismic data, but disregard the geological context. Hence, the inverted models may be geologically inconsistent, and not represent feasible lithological units. We propose that all the aforementioned difficulties can be alleviated by explicitly incorporating petrophysical information into the inversion through a penalty function based on multiple probability density functions (PDFs), where each PDF represents a different lithology with distinct properties. We treat lithological units as clusters and use unsupervised K-means clustering to separate the petrophysical information into different units of distinct lithologies are not easily distinguishable. Through several synthetic examples, we demonstrate that the proposed framework leads FWI to elastic models that are superior to models obtained either without incorporating petrophysical information, or with a probabilistic penalty function based on a single PDF.

Elastic full-waveform inversion with probabilistic petrophysical model constraints

2020

GEOPHYSICS

Full-waveform inversion (FWI) based on the minimization of data residuals may not enhance our understanding of the subsurface and can at times lead to misleading subsurface models. Additionally, unconstrained multiparameter FWI may also lead to models that do not represent realistic lithology for independently derived parameters. We have developed a method for elastic FWI that explicitly imposes petrophysical restrictions to guide models toward realistic and feasible lithology, that is, to subsurface models consistent with the seismic data and with the underlying petrophysics. We exploit petrophysical information, such as that provided by well logs, to constrain the inversion and to avoid implausible models. We achieve this goal by confining the inverted models to a feasible region defined by a probability density function instead of imposing lithologic facies as a function of position. Inside this feasible petrophysical regime, the inverted models do not need to obey a specific trend, that is, we do not link the parameters with explicit and potentially inaccurate petrophysical relations. Instead, we define a petrophysical basin of attraction that confines models to a feasible region validated by regional lithologic and petrophysical information. We find through elastic models that incorporating probabilistic petrophysical constraints into the inversion objective function leads to models that are superior to models obtained either without constraints or with approximate analytic constraints. In addition, we discover that these constraints can help in mitigating common issues affecting elastic FWI, such as the artifacts produced by interparameter crosstalk and limited acquisition coverage.