Cuttings imaging for determining geological properties

An apparatus comprising:

• a processing system comprising a processor and a memory storing an executable computer program code that, when executed by the processor:
• ascribes one of a plurality of predetermined sub-classes to each of a plurality of pixels of an image of an unknown rock sample (“unknown rock sample image”) retrieved from a geological formation, wherein:
• the ascription utilizes a deep learning model that is stored in the memory and that was trained with an annotated training dataset;
• and
• the annotated training dataset comprises:
• a plurality of multi-pixel images of known rock samples (“known rock sample images”);
• and
• for each known rock sample image, which of the sub-classes corresponds to at least a subset of pixels of that image;
• for each pixel of the unknown rock sample image having an ascribed sub-class, derives which one of a plurality of predetermined meta-classes is associated with that pixel based on the sub-class ascribed to that pixel, wherein the meta-classes are each representative of a different one of a plurality of predetermined rock types;
• and
• predicts at least one property of the geological formation utilizing the ascription-derived meta-classes, wherein the at least one property includes:
• which one or more of the rock types are present in the geologic formation;
• and
• a quantity of each of the one or more rock types predicted to be present in the geologic formation.

A non-transitory, tangible, computer-readable medium having stored thereon computer-readable instructions executable by a processing system for:

• ascribing one of a plurality of predetermined sub-classes to each of a plurality of pixels of an image of an unknown rock sample (“unknown rock sample image”) retrieved from a geological formation, wherein:
• the ascription utilizes a deep learning model that is trained with an annotated training dataset;
• and
• the annotated training dataset comprises:
• a plurality of multi-pixel images of known rock samples (“known rock sample images”);
• and
• for each known rock sample image, which of the sub-classes corresponds to at least a subset of pixels of that image;
• for each pixel of the unknown rock sample image having an ascribed sub-class, deriving which one of a plurality of predetermined meta-classes is associated with that pixel based on the sub-class ascribed to that pixel, wherein the meta-classes are each representative of a different one of a plurality of predetermined rock types;
• and
• predicting at least one property of the geological formation utilizing the ascription-derived meta-classes, wherein predicting the at least one property includes:
• predicting which one or more of the rock types are present in the geologic formation;
• and
• predicting a quantity of each of the one or more rock types predicted to be present in the geologic formation.

A method comprising:

• ascribing one of a plurality of predetermined sub-classes to each of a plurality of pixels of an image of an unknown rock sample (“unknown rock sample image”) retrieved from a geological formation, wherein:
• the ascription utilizes a deep learning model that is trained with an annotated training dataset;
• and
• the annotated training dataset comprises:
• a plurality of multi-pixel images of known rock samples (“known rock sample images”), wherein the known rock sample comprises drill cuttings;
• and
• for each known rock sample image, which of the sub-classes corresponds to at least a subset of pixels of that image;
• for each pixel of the unknown rock sample image having an ascribed sub-class, deriving which one of a plurality of predetermined meta-classes is associated with that pixel based on the sub-class ascribed to that pixel, wherein the meta-classes are each representative of a different one of a plurality of predetermined rock types;
• and
• predicting at least one property of the geological formation utilizing the ascription-derived meta-classes, wherein predicting the at least one property includes predicting which one or more of the rock types are present in the geologic formation;
• wherein:
• the unknown rock sample is one of a plurality of unknown rock samples each retrieved from a different depth in the geological formation and depicted in a corresponding one of a plurality of unknown rock sample images;
• and
• the method further comprises:
• performing the sub-class ascription, the meta-class derivation, and the at least one property prediction utilizing each of the plurality of unknown rock sample images;
• and
• generating a lithology profile of the geologic formation versus depth based on the property predictions.