Automated method and system for categorising and describing thin sections of rock samples obtained from carbonate rocks

A non-transitory computer-readable medium storing a computer program comprising computer readable instructions which, when executed by one or more processors, cause the one or more processors to:

• capture, using an input image data module, images of one or more thin sections of one or more input rock samples obtained from reservoir carbonate rocks so as to generate input rock image data;
• classify, using a classifying module based on a trained convolutional neural network generated from supervised learning, the one or more input rock samples by applying convolutional neural network processing to the input rock image data to identify one or more attributes of the input rock samples;
• extract, using a trained salient feature extraction module based on a trained convolution deconvolution neural network, salient features from the one or more attributes of the input rock samples so as to generate salient feature data;
• apply, using a semantic text processing module based on a trained recurrent neural network generated from supervised learning, semantic text processing to the salient feature data so as to generate input rock sample text data indicative of the one or more attributes of the input rock samples;
• store, using a storage module, the input rock sample text data;
• search, using a search module, a rock image database for categorized images that are similar to the captured images based on the stored rock sample text data and database text data associated with the categorized images stored in the rock image database, in which the rock image database comprises categorized images of categorized rock samples and the database text data, and the database text data is indicative of one or more attributes of the categorized rock samples and is associated with petrophysical measurements of the categorized rock samples;
• and
• generate output data dependent upon a degree of similarity between the rock sample text data and the database text data so that the output data indicates petrophysical properties of the carbonate rocks associated with the thin sections of input rock samples that are identified as being within a selected degree of similarity to the categorized rock samples in the rock image database based on a comparison of the stored rock sample text data with the database text data, and in which the output data is based on the petrophysical measurements of the categorized rock samples identified as being within a selected degree of similarity to the input rock samples.

An automated system for categorizing and describing thin sections of reservoir rock samples obtained from carbonate rocks, the system comprising a processor in communication with memory storing instructions executable by the processor to:

• capture optical or microscopic images of one or more thin sections of one or more input rock samples obtained from reservoir carbonate rocks so as to generate input rock image data;
• classify, based on a trained convolution neural network generated from supervised learning, the one or more rock samples into lithological and quality categories of carbonate thin sections by applying convolutional neural network processing to the input rock image data to identify one or more attributes of the rock samples;
• extract, based on a trained convolution deconvolution neural network, salient features from the one or more attributes of the rock samples so as to generate salient feature data;
• apply semantic text processing, based on a trained recurrent neural network generated from supervised learning, to the salient feature data using learned associations between salient-feature data and geological descriptive terminology so as to generate input rock sample text data indicative of the one or more attributes of the rock samples;
• store the input rock sample text data in a storage module;
• search a rock image database for categorized images that are similar to the captured images based on the stored rock sample text data and database text data associated with the categorized images stored in the rock image database, in which the rock image database comprises categorized images of categorized rock samples and the database text data, and the database text data is indicative of one or more attributes of the categorized rock samples and is associated with petrophysical measurements of the categorized rock samples;
• and
• generate output data dependent upon a degree of similarity between the rock sample text data and the database text data so that the output data indicates petrophysical properties of the carbonate rocks associated with the thin sections of input rock samples that are identified as being within a selected degree of similarity to the categorized rock samples in the rock image database based on a comparison of the stored rock sample text data with the database text data, and in which the output data is based on the petrophysical measurements of the categorized rock samples identified as being within a selected degree of similarity to the input rock samples.