A computer-implemented method comprising: obtaining reservoir data, the reservoir data_comprising seismic data unformatted for a machine learning model; converting the reservoir data into a machine learning training data set by: segmenting , by a crawler application, at least a section of the reservoir data into a sequence of subsections; labeling , by the crawler application, the sequence of subsections of the reservoir data to generate labeled reservoir data wherein labeling comprises, for each subsection of the sequence of subsections: processing the reservoir data of the subsection; and labeling the subsection using an n-tuple, wherein the n-tuple comprises a concatenation of two or more labels corresponding to different features identified in the subsection; feeding , by the crawler application, the labeled reservoir data to a machine learning algorithm that uses the labeled reservoir data as a training data set to train machine learning models, wherein the machine learning algorithm is a feed-forward artificial neural network in the form of a convolutional neural network, wherein the machine learning algorithm uses the trained models to label new reservoir data, such that the new reservoir data can be automatically analyzed; and outputting analyzed reservoir data for use in oilfield planning and exploitation operations. obtaining reservoir data, the reservoir data_comprising seismic data unformatted for a machine learning model; converting the reservoir data into a machine learning training data set by: segmenting , by a crawler application, at least a section of the reservoir data into a sequence of subsections; labeling , by the crawler application, the sequence of subsections of the reservoir data to generate labeled reservoir data wherein labeling comprises, for each subsection of the sequence of subsections: processing the reservoir data of the subsection; and labeling the subsection using an n-tuple, wherein the n-tuple comprises a concatenation of two or more labels corresponding to different features identified in the subsection; segmenting , by a crawler application, at least a section of the reservoir data into a sequence of subsections; labeling , by the crawler application, the sequence of subsections of the reservoir data to generate labeled reservoir data wherein labeling comprises, for each subsection of the sequence of subsections: processing the reservoir data of the subsection; and labeling the subsection using an n-tuple, wherein the n-tuple comprises a concatenation of two or more labels corresponding to different features identified in the subsection; processing the reservoir data of the subsection; and labeling the subsection using an n-tuple, wherein the n-tuple comprises a concatenation of two or more labels corresponding to different features identified in the subsection; feeding , by the crawler application, the labeled reservoir data to a machine learning algorithm that uses the labeled reservoir data as a training data set to train machine learning models, wherein the machine learning algorithm is a feed-forward artificial neural network in the form of a convolutional neural network, wherein the machine learning algorithm uses the trained models to label new reservoir data, such that the new reservoir data can be automatically analyzed; and outputting analyzed reservoir data for use in oilfield planning and exploitation operations. <EOS>