BACKGROUND OF THE DISCLOSURE

Surface logging is a wellsite service providing early indications about drilled rocks and reservoir potential. For example, a wellsite operator, known as a “mud logger,” may attempt to perform lithology identification from drill cuttings returning from a well in order to reconstruct a geology map of the well. The mud logger creates a manual description based on images and acid tests. For each sample, the mud logger may examine cutting samples (e.g., through binoculars or other magnifying means) and attempt to recognize different rock types in the samples. If the rock types are not visually clear, some of the cuttings may be isolated for acid tests and final identification. The mud logger then attempts to quantify proportions of the different rock types from the samples. Using this information, a reconstruction of the well formation can be performed to create a lithology column. However, manual rock type identification and quantification can be very subjective (e.g., biased by the mud logger's geological background) and significantly time-consuming.

SUMMARY OF THE DISCLOSURE

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify indispensable features of the claimed subject matter, nor is it intended for use as an aid in limiting the scope of the claimed subject matter.

The present disclosure introduces a method including ascribing one of multiple predetermined sub-classes to each of multiple pixels of an image of an unknown rock sample (“unknown rock sample image”) retrieved from a geological formation. The ascription utilizes a deep learning model that is trained with an annotated training dataset. The annotated training dataset includes multiple 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. The method also includes deriving, for each pixel of the unknown rock sample image having an ascribed sub-class, which one of multiple predetermined meta-classes is associated with that pixel based on the sub-class ascribed to that pixel. The meta-classes are each representative of a different one of multiple predetermined rock types. The method also includes predicting at least one property of the geological formation utilizing the ascription-derived meta-classes. Predicting the at least one property includes predicting which one or more of the rock types are present in the geologic formation.

The present disclosure also introduces an apparatus including a processing system having a processor and a memory storing an executable computer program code. By executing the computer program code, the processor ascribes one of multiple predetermined sub-classes to each of multiple pixels of an image of an unknown rock sample (“unknown rock sample image”) retrieved from a geological formation. The ascription utilizes a deep learning model that is stored in the memory and that was trained with an annotated training dataset. The annotated training dataset includes 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. By executing the computer program code, the processor also derives, for each pixel of the unknown rock sample image having an ascribed sub-class, which one of multiple predetermined meta-classes is associated with that pixel based on the sub-class ascribed to that pixel. The meta-classes are each representative of a different one of multiple predetermined rock types. By executing the computer program code, the processor also predicts at least one property of the geological formation utilizing the ascription-derived meta-classes. The at least one property includes which one or more of the rock types are present in the geologic formation. The apparatus may further include an imaging device for obtaining the unknown rock sample image.

The present disclosure also introduces a non-transitory, tangible, computer-readable medium having stored thereon computer-readable instructions executable by a processing system for ascribing one of multiple predetermined sub-classes to each of multiple pixels of an image of an unknown rock sample (“unknown rock sample image”) retrieved from a geological formation. The ascription utilizes a deep learning model that is trained with an annotated training dataset. The annotated training dataset includes 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. The computer-readable instructions also executable by the processing system for deriving, for each pixel of the unknown rock sample image having an ascribed sub-class, which one of multiple predetermined meta-classes is associated with that pixel based on the sub-class ascribed to that pixel. The meta-classes are each representative of a different one of multiple predetermined rock types. The computer-readable instructions also executable by the processing system for predicting at least one property of the geological formation utilizing the ascription-derived meta-classes. The at least one property includes predicting which one or more of the rock types are present in the geologic formation.

These and additional aspects of the present disclosure are set forth in the description that follows, and/or may be learned by a person having ordinary skill in the art by reading the material herein and/or practicing the principles described herein. At least some aspects of the present disclosure may be achieved via means recited in the attached claims.

