Saudi Arabian Oil Company

Roald Van Borselen

Jewoo Yoo

Robertus Franciscus Hegge

Paul M Zwartjes

Young Seo Kim

Rob Hegge

A system and method for determining a set of first breaks of a seismic dataset are disclosed, the method including obtaining the seismic dataset composed of a plurality of seismic traces and a provisional first break for each seismic trace. The method further includes selecting a plurality of proximal picks for each seismic trace, determining a near-offset pick for each seismic trace starting with shortest offset and sequentially selecting traces in order of increasing offset, and determining a far-offset pick for each seismic trace starting with the farthest offset and sequentially selecting traces in order of decreasing offset. The method further includes determining a set of coincident picks based on the near-offset and the far-offset picks for each seismic trace, fitting a curve to the set of coincident picks, and determining the set of first breaks of the seismic dataset from the curve.

System and method for determining a set of first breaks of a seismic dataset

A method may include obtaining seismic data regarding a geological region of interest. The seismic data may include various pre-processed gathers. The method may further include obtaining a machine-learning model that is pre-trained to predict migrated seismic data. The method may further include selecting various training gathers based on a portion of the pre-processed gathers, a migration function, and a velocity model. The method may further include generating a trained model using the training gathers, the machine-learning model, and a machine-learning algorithm. The method may further include generating a seismic image of the geological region of interest using the trained model and a remaining portion of the seismic data.

Method and system for faster seismic imaging using machine learning

A method may include obtaining an input gather regarding a geological region of interest. The method may further include obtaining parameterization data regarding a seismic processing operation. The parameterization data may correspond to a first set of process parameter values that are different from a second set of process parameter values that are used to generate the input gather. The method may further include generating a predicted output gather using a machine-learning model, the input gather, and the parameterization data. The machine-learning model may include an encoder model and a decoder model. The method may further include generating a seismic image of the geological region of interest using the predicted output gather.

Method and system for determining seismic processing parameters using machine learning

A method for picking first breaks on a synthetic seismic dataset for a full wavefield travel-time inversion is provided. This method includes obtaining a synthetic seismic dataset and for each synthetic trace, determining a plurality of synthetic trace amplitude maxima and minima, determining a modified energy ratio trace, and forming a trace mask based at least in part, on an amplitude of the synthetic trace and on an amplitude of the modified energy ratio trace. The method further includes determining for each synthetic trace, a positive estimated first break based on the plurality of synthetic trace amplitude maxima and the trace mask and a negative estimated first bread based on the plurality of synthetic trace amplitude minima and the trace mask. The method concludes with determining a predicted first break for each synthetic trace from the positive estimate first break and the negative estimate first break.

First break picking for full wavefield travel-time inversion

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for echo-deblending using coincidence-filtering of offshore seismic data. In one aspect, a method includes receiving an offshore seismic dataset of a surveyed subsurface, the offshore seismic dataset comprising a primary-wave signal and a ghost-wave signal; determining a forward extrapolation and a backward extrapolation for the offshore seismic dataset; determining a coincident signal by applying a coincidence filtering to the forward extrapolation and the backward extrapolation; extrapolating the coincident signal to determine a ghost-wave value for the ghost-wave signal; applying adaptive subtraction to the offshore seismic dataset with the ghost-wave value to determine a computed primary-wave value for the primary-wave signal; generating a model of the surveyed subsurface based on primary-wave data calculated from the offshore seismic dataset based on the computed primary-wave value; and evaluating a productivity of the surveyed subsurface according to the model.

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Delft, Netherlands

Roald Van Borselen