Abstract: Planning a wellbore includes determining drillability values from surface drilling parameters for an offset wellbore. The drillability values are used to prepare a protein code sequence of protein codes assigned to a range of drillability values. The protein code sequence from the offset wellbore is used to develop a protein code sequence for a planned wellbore. A machine learning model analyzes the offset surface drilling parameters and protein code sequence, and provides target surface drilling parameters for the planned wellbore.

Abstract: The disclosure relates to a method and system for downhole processing of data, such as images, including using a set of downhole sensors to measure parameters relative to the borehole at a plurality of depths and azimuths and detecting predetermined features of the borehole, using a downhole processor, with a trained machine-learning model and extracting characterization data, characterizing the shape and position of the predetermined features that are transmitted to the surface. It also provides a method and system for providing an image of a geological formation at the surface including transmitting a first dataset to the surface that will be used for reconstructing an image at the surface, downhole processing of a second dataset to detect predetermined features and extract characterization data that are transmitted at the surface and displaying a combined image comprising the predetermined features overlaid on the first image.

Abstract: A system for wireline service planning and advising includes a receiver, one or more computing system processors, and a transmitter. The receiver is configured to receive, from a user of the system, an objective parameter for interpreting a state of a well barrier. The one or more computing system processors is in communication with the receiver and configured to generate a plurality of candidate services based on the objective parameter and a model of the well barrier, each candidate service specifying sensor data to be acquired using wireline tools, select at least one wireline service from the wireline candidate services based on a selection logic or input by the user, and generate an execution plan specifying operational parameters of the selected wireline service. The transmitter is in communication with the one or more computing system processors and configured to transmit the execution plan to execute the selected wireline service at a wellsite.

Abstract: Apparatus and methods for ascribing one of multiple predetermined sub-classes to multiple pixels of an image of an unknown rock sample retrieved from a geological formation. The ascription utilizes a deep learning model trained with an annotated training dataset. The annotated training dataset includes multi-pixel images of known rock samples and, for each known rock sample image, which sub-class 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, which one of predetermined meta-classes is associated with that pixel is derived based on the sub-class ascribed to that pixel. The meta-classes represent different predetermined rock types. At least one property of the formation is predicted utilizing the ascription-derived meta-classes, including which rock type(s) are present in the formation.

Abstract: Systems and methods for identifying a potential third interface echo (TIE) using objective criteria are provided. A system includes an acoustic logging tool that obtains measurements in a wellbore and a data processing system that has a processor that receives the measurements from the acoustic logging tool. The data processing system may identify a third interface echo (TIE) using a neural network and/or by a signal analysis method based on the behavioral characteristics of the TIE signal.

Abstract: Embodiments of the present disclosure are directed towards systems and methods for automated stratigraphy interpretation from borehole images. Embodiments may include constructing, using at least one processor, a training set of synthetic images corresponding to a borehole, wherein the training set includes one or more of synthetic images, real images, and modified images. Embodiments may further include automatically classifying, using the at least one processor, the training set into one or more individual sedimentary geometries using one or machine learning techniques. Embodiments may also include automatically classifying, using the at least one processor, the training set into one or more priors for depositional environments.

Abstract: Systems and methods for identifying a potential third interface echo (TIE) using objective criteria are provided. A system includes an acoustic logging tool that obtains measurements in a wellbore and a data processing system that has a processor that receives the measurements from the acoustic logging tool. The data processing system may identify a third interface echo (TIE) using a neural network and/or by a signal analysis method based on the behavioral characteristics of the TIE signal.

Abstract: Apparatus and methods for ascribing one of multiple predetermined sub-classes to multiple pixels of an image of an unknown rock sample retrieved from a geological formation. The ascription utilizes a deep learning model trained with an annotated training dataset. The annotated training dataset includes multi-pixel images of known rock samples and, for each known rock sample image, which sub-class 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, which one of predetermined meta-classes is associated with that pixel is derived based on the sub-class ascribed to that pixel. The meta-classes represent different predetermined rock types. At least one property of the formation is predicted utilizing the ascription-derived meta-classes, including which rock type(s) are present in the formation.

Abstract: The disclosure relates to a method and system for downhole processing of data, such as images, including using a set of downhole sensors to measure parameters relative to the borehole at a plurality of depths and azimuths and detecting predetermined features of the borehole, using a downhole processor, with a trained machine-learning model and extracting characterization data, characterizing the shape and position of the predetermined features that are transmitted to the surface. It also provides a method and system for providing an image of a geological formation at the surface including transmitting a first dataset to the surface that will be used for reconstructing an image at the surface, downhole processing of a second dataset to detect predetermined features and extract characterization data that are transmitted at the surface and displaying a combined image comprising the predetermined features overlaid on the first image.

Abstract: The present disclosure introduces an apparatus including a toolstring for use in a tubular extending into a subterranean formation. The toolstring includes modular components that include one or more caliper modules and a power and control (P/C) module. The one or more caliper modules each include radially rotatable fingers for sensing an internal diameter of the tubular. The P/C module is operable for distributing power and control signals to the one or more caliper modules. The caliper and P/C modules are mechanically and electrically interconnected by common lower interfaces of the caliper and P/C modules.

Abstract: Embodiments of the disclosure involve a method comprising a method comprising inputting borehole dip data; determining characteristics of a plurality of dips based on the borehole dip data; applying one or more geological models to the characteristics; and generating one or more geological cross-sections based on geological modeling.

Abstract: The disclosure relates to a method for flagging at least an event of interest in an unlabeled time series of a parameter relative to a wellsite (including to the well, formation or a wellsite equipment), wherein the time series of the parameter is a signal of the parameter as a function of time. The disclosure also relates to a method for evaluation a downhole operation such as a pressure test using a pressure time series. Such methods comprises collecting a time series, extracting at least an unlabeled subsequence of predetermined duration in the time series, and assigning an event of interest a label, in particular representative of the status of the downhole operation, to at least one of the unlabeled subsequences. A command may be sent to a wellsite operating system based on assigned label.

Abstract: A method for determining a property of a geological formation based on an optical image of rock samples taken from the formation is presented therein. The image comprises a plurality of pixels and the method comprises defining windows in the image, each window comprising predetermined number of pixels and being of a predetermined shape. The method also includes, for each window, extracting a rockprint value representative of the window. A rockprint comprises indicators for characterizing a texture of the window. The method also includes classifying the windows into categories of a predetermined set. Each category is representative of one type of rock and the classification is based on a comparison of the rockprint value of each window with rockprint values of images of reference rock samples for each category. Based on the classification, the method then includes determining the at least one property of the geological formation, ie the quantification of each type of rock in the sample.

Abstract: The disclosure relates to a method for analysing cuttings exiting a borehole. The method comprises taking at least an image of a sample of cuttings on a background surface, obtaining spectra representative of the image in the (hue, saturation, brightness) coordinate space, wherein each spectrum is associated to a coordinate and is representative of the distribution of the values of the pixels for the coordinate. Based on the spectrum and the values of each pixel for the associated coordinate, classifying the pixel in one of a plurality of groups, wherein each group is representative of a type of objects within the image, i.e. cuttings and background surface. The method also comprises determining at least a cuttings zone in the image based on the classification of the pixels.

Abstract: The disclosure relates to methods and systems for analyzing an image of the formation intersected by a borehole. One of the methods determines a local apparent dip of the borehole at least at a measured depth i represented on the image, applies at least a window to the image, wherein each of the windows includes one of the measured depth i and is shaped as a function of the determined local dip at the corresponding measured depth i, compares a texture of at least a first zone of each window and a texture of at least a second zone of said window, wherein each of the first and second zones are adjacent and shaped as a function of the determined dip. Based on the comparison, the method determines at least a location of a texture boundary and derives a property of the formation. The other method includes determine locations of the texture boundaries, segmenting the image as a function of the texture boundaries, and perform clustering of the segments in order to determine a facies of the formation.

Abstract: In one embodiment, a computer-based method includes obtaining a borehole image deriving from a downhole tool in a borehole of a geological formation, performing dip picking on the borehole image to derive one or more structural dips, deriving a continuous structural dip based on the one or more structural dips, defining one or more locations of zone boundaries on the borehole image, deriving one or more zone boundaries based on the continuous structural dip and the one or more locations of zone boundaries, and defining one or more zones of the wellbore in a second image based on the one or more zone boundaries.

Abstract: A method can include receiving data for a geologic environment where the data include data acquired via different types of borehole tool sensors; based at least in part on the data, determining rock composition of the geologic environment where the rock composition includes depositional components and diagenetic components; and, based at least in part on the rock composition, outputting a stratigraphic model of at least a portion of the geologic environment.

Abstract: Using machine learning for sedimentary facies prediction by using one or more logs acquired in a borehole. This includes performing a petrophysical clustering of borehole depths wherein the depths of the borehole are gathered into clusters based on similarities in the one or more logs. Also performed is a log inclusion optimization, including a selection of one or more parameters of the petrophysical clustering, wherein the one or more parameters include a number and/or type of considered logs among the one or more logs and/or a clustering method. Also performed is a classification of the clusters into core depositional facies using one or more predetermined rules.

Abstract: A method for determining a property of a geological formation based on an optical image of rock samples taken from the formation is presented therein. The image comprises a plurality of pixels and the method comprises defining windows in the image, each window comprising predetermined number of pixels and being of a predetermined shape. The method also includes, for each window, extracting a rockprint value representative of the window. A rockprint comprises indicators for characterizing a texture of the window. The method also includes classifying the windows into categories of a predetermined set. Each category is representative of one type of rock and the classification is based on a comparison of the rockprint value of each window with rockprint values of images of reference rock samples for each category. Based on the classification, the method then includes determining the at least one property of the geological formation, ie the quantification of each type of rock in the sample.

Abstract: The disclosure provides methods and systems for evaluating formation geometry and petrophysical properties directly from raw electromagnetic measurements. The methods involve using a downhole tool to measure a property of a formation at multiple depths of investigation and calculating geometry and petrophysical property information by direct inversion from raw measurements acquired by the tool. The system includes a tool for measuring a formation property at different depths of investigation and a processor for calculating geometry and petrophysical information by direct inversion from the raw measurements acquired by the tool.