Schlumberger Technology Corporation

Sunil Manikani

Aria Abubakar

Hiren Maniar

Cen Li

Haibin Di

Leigh Truelove

Karan Pathak

Gayatri Novenita

A method includes receiving seismic training data comprising a plurality of images each including a plurality of traces, predicting a location of a feature in at least some of the plurality of traces based on a location of an amplitude peak therein, applying labels to the locations, classifying pixels of the plurality of images as representing the feature or not representing the feature, using a semantic segmentation model, adjusting the labels based on the classification of the pixels, training, using the adjusted labels and the seismic training data, a machine-learning model to identify the feature, and identifying the feature in a different seismic data set using the trained machine-learning model.

Feature detection in seismic data

A method includes receiving a seismic survey that includes a plurality of seismic slices. The method also includes converting the seismic slices into an embedding. The embedding includes one or more vectors. Each of the vectors includes more than 3 dimensions. The method also includes generating a plot based at least partially upon the embedding.

Quantifying diversity in seismic datasets

Growing community of inventors

Pune, India

Sunil Manikani