Yongchae Cho

We applied the generalized multiscale finite-element method (GMsFEM) to simulate seismic wave propagation in fractured media. Fractures are represented explicitly on a fine-scale triangular mesh, and they are incorporated using the linear-slip model. The motivation for applying GMsFEM is that it can reduce computational costs by using basis functions computed from the fine-scale fracture model to simulate propagation on a coarse grid. First, we apply the method to a simple model that has a unifo

SUMMARY In an ideal case, the time-lapse differences in 4-D seismic data should only reflect the changes of the subsurface geology. Practically, however, undesirable discrepancies are generated because of various reasons. Therefore, proper time-lapse processing techniques are required to improve the repeatability of time-lapse seismic data and to capture accurate seismic information to analyse target changes. In this study, we propose a machine learning-based time-lapse seismic data processing m

Accurate estimation of subsurface properties plays an important role in successful hydrocarbon exploration, and a variety of different types of inversion schemes are used to infer earth properties such as velocity or density by analyzing the surface seismic. The Markov-chain Monte Carlo (MCMC) stochastic approach is widely used to estimate subsurface properties. We have used a transdimensional form of MCMC, reversible jump MCMC (RJMCMC), to estimate seismic impedance, which allows the inference

ABSTRACT Elastic full-waveform inversion (FWI) is superior to acoustic FWI due to its ability to simulate complex mode conversions in fast-varying elastic media. Using elastic FWI may become important when building velocity models in areas of large complex salt bodies that we typically see in the Gulf of Mexico. Accounting for elastic effects in FWI can reduce the artifacts that are caused by using an acoustic approximation. We often rely on petrophysics relations to define an initial shear velo

ABSTRACT Although horizon interpretation is a routine task for building reservoir models and accurately estimating hydrocarbon production volumes, it is a labour‐intensive and protracted process. Hence, many scientists have worked to improve the horizon interpretation efficiency via auto‐picking algorithms. Nevertheless, the implementation of a classic auto‐tracking method becomes challenging when addressing reflections with weak and discontinuous signals, which are associated with complicated s

ABSTRACT Reverse time migration (RTM) is widely used because of its ability to recover complex geologic structures. However, RTM also has a drawback in that it requires significant computational cost. In RTM, wave modeling accounts for the largest part of the computing cost for calculating forward- and backward-propagated wavefields before applying an imaging condition. For this reason, we have applied a frequency-adaptive multiscale spatial grid to enhance the efficiency of the wave simulations

The prediction of natural fracture networks and their geomechanical properties remains a challenge for unconventional reservoir characterization. Because natural fractures are highly heterogeneous and of subseismic scale, integrating petrophysical data (i.e., cores and well logs) with seismic data is important for building a reliable natural fracture model. Therefore, I have developed an integrated and stochastic approach for discrete fracture network modeling with field data experimentation. In

The Markov chain Monte Carlo (McMC) stochastic approach is widely used to estimate subsurface properties. However, estimating uncertainty quantitatively is also very important when performing stochastic inversion. Therefore, the goal of this paper is to apply the transdimensional, or reversible jump, McMC (rjMcMC) method to obtain a 3-D seismic impedance model and to determine a corresponding uncertainty cube by estimating the standard deviation of the models that are included in the Markov chai

Seismic acoustic impedance inversion plays an important role in understanding subsurface structures and obtaining subsurface properties. The stochastic approach is one of the methods used for impedance inversion, and it aims to produce more reliable results by accounting for modeling uncertainty . Stochastic inversion represents the uncertainty of a subsurface model as a probability distribution and uses this distribution to estimate model parameters . In this study, seismic acoustic impedance i