In this paper, 2D seismic data and petrophysical logs of the Pabdeh Formation from four wells of the Mansuri oil field are utilized. ΔLog R method was used to generate a continuous TOC log from petrophysical data. The calculated TOC values by ΔLog R method, used for a multi-attribute seismic analysis. In this study, seismic inversion was performed based on neural networks algorithm and the resu...

In this study, we apply advanced numerical optimization techniques to extend the work of Imhof and Sharma (2005, 2006) to integrate geological and geophysical data and infer the sedimentary parameters that produce a match to seismic data. In particular, we seek to match not just event timing (phase) but also reflection strength (amplitude). This inverse problem of quantitatively matching presen...

Full waveform inversion (FWI) of seismic or Ground Penetrating Radar data provides high-resolution quantitative images of the constitutive parameters of the rock/soil which control seismic/GPR wave propagation. We developed a 2D inversion tool in the frequency domain adapted to the multi-parameter physics controlling GPR propagation in isotropic non dispersive media, i.e. dielectric permittivit...

iran is situated in one of the world's seismic regions and the possibility of destructive earthquakes in most regions of the country has given great significance to recognition of iranian seismic nature from a seismic and seismotectonic standpoint. study of the crust and upper mantle velocity structure in the iranian plateau provides better understanding of its evolution and tectonic histo...

Seismic waveforms contain much information that is ignored under standard processing schemes; seismic waveform inversion seeks to use the full information content of the recorded wavefield. In this paper I present, apply, and evaluate a frequency-space domain approach to waveform inversion. The method is a local descent algorithm that proceeds from a starting model to refine the model in order ...

We present a new seismic inversion method that uses deep learning (DL) features for the subsurface velocity model estimation. The DL feature is low-dimensional representation of high-dimensional data, which automatically generated by convolutional autoencoder (CAE) and preserved in latent space. contains key information input data. Therefore, instead directly comparing waveform differences betw...

We consider a Bayesian model for doing lithology–fluid prediction from prestack (amplitude versus offset) seismic data. Related to the Bayesian model, we look at two inversion algorithms. The first algorithm simulates from the posterior distribution with no approximations, but the algorithm is quite computer demanding. The second inversion algorithm introduces an approximation in the likelihood...

A method to estimate S-wave interval velocity, using P-S seismic data is presented. The method is composed of three steps. First, the P-S data are converted to a relative change in S-velocity section. The section is then linearized. Constrained inversion is used to convert this last section to S-interval velocity. To illustrate the method, the S velocity of a field data set was derived, and a V...