A new procedure for the interpretation of VES data: 1.5-D simultaneous inversion method

Gyulai, Ákos; Ormos, T.

doi:10.1016/s0926-9851(98)00034-2

Cited by 36 publications

(24 citation statements)

References 22 publications

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“…In geoelectric data processing, the series expansion-based inversion methodology was used to evaluate the 2D resistivity and thickness distribution of shallow structures and an optimization method was suggested for giving the optimal number of inversion unknowns (Gyulai and Ormos, 1999). They show significant improvement in reducing the estimation errors and ambiguity.…”

Section: Introductionmentioning

confidence: 99%

Interval inversion approach for an improved interpretation of well logs

Dobróka

Szabó

Tóth³

et al. 2016

GEOPHYSICS

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The quality analysis of well-logging inversion results has always been an important part of formation evaluation. The precise calculation of hydrocarbon reserves requires the most accurate possible estimation of porosity, water saturation, and shale and rock-matrix volumes. The local inversion method conventionally used to predict the above model parameters depth by depth represents a marginally overdetermined inverse problem, which is rather sensitive to the uncertainty of observed data and limited in estimation accuracy. To reduce the harmful effect of data noise on the estimated model, we have suggested the interval inversion method, in which an increase of the overdetermination ratio allows a more accurate solution of the well-logging inverse problem. The interval inversion method inverts the data set of a longer depth interval to predict the vertical distributions of petrophysical parameters in a joint inversion procedure. In formulating the forward problem, we have extended the validity of probe response functions to a greater depth interval assuming the petrophysical parameters are depth dependent, and then we expanded the model parameters into a series using the Legendre polynomials as basis functions for modeling inhomogeneous formations. We solved the inverse problem for a much smaller number of expansion coefficients than data to derive the petrophysical parameters in a stable overdetermined inversion procedure. The added advantage of the interval inversion method is that the layer thicknesses and suitably chosen zone parameters can be estimated automatically by the inversion procedure to refine the results of inverse and forward modeling. We have defined depth-dependent model covariance and correlation matrices to compare the quality of the local and interval inversion results. A detailed study using well logs measured from a Hungarian gas-bearing unconsolidated formation revealed that the greatly overdetermined interval inversion procedure can be effectively used in reducing the estimation errors in shaly sand formations, which may refine significantly the results of reserve calculation.

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Section: Introductionmentioning

confidence: 99%

Interval inversion approach for an improved interpretation of well logs

Dobróka

Szabó

Tóth³

et al. 2016

GEOPHYSICS

View full text Add to dashboard Cite

show abstract

“…This problem can be reduced significantly by applying joint inversion methods (Vozoff and Jupp, 1975;Hering et al, 1995;Haber and Oldenburg, 1997;Misiek et al, 1997;Kis, 1998;Gallardo and Meju, 2004). In order to give better approximation for geological models with slow lateral variations a new inversion algorithm, the so-called 1.5-D inversion was introduced by Gyulai and Ormos (1999a). By the method all observation data (measured with an electrode array) from each VES station along the survey profile are integrated into one inversion procedure.…”

Section: Introductionmentioning

confidence: 99%

“…In order to assure cost-effective computation the authors use 1-D forward modeling procedure. The efficiency of the 1.5-D method was demonstrated both on synthetic and field data , 1998, 1999a.…”

Section: Introductionmentioning

confidence: 99%

“…We develop further the idea of the 1.5-D inversion presented by Gyulai and Ormos (1999a). For the forward modeling calculations we use the FD algorithm developed by Spitzer (1995).…”

Section: Introductionmentioning

confidence: 99%

“…This gives the possibility to define the inversion problem as an overdetermined one, without the need of any additional regularization (or smoothness) constraint. In order to reduce the computation time we apply the 1.5-D method (Gyulai and Ormos, 1999a) for the first few iteration steps and use the resultant model as an initial one for the subsequent 2-D inversion. We call the above procedure combined geoelectric inversion (CGI) method.…”

Section: Introductionmentioning

confidence: 99%

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