Abstract: An example system may be used to automate one or more processes relating to drill cutting handling, cleaning, and packing to produce consistently high-quality cleaned samples. In some implementations, an example system may employ cleaning processes including centrifugal and ultrasonic processes to clean a sample automatically without the aid of a human operator thereby increasing efficiency and quality of the produced samples for better subsequent sample analysis. A system may include an unloading module, a cleaning module, and packing module, all of which may be operated in combination with a robotic arm.

Abstract: System and methods are disclosed. The method includes obtaining an observed stratigraphic thickness map, initial bathymetry map, and initial subsidence sequence for a model of the geological region of interest, where the model comprises a plurality of cells each representing a portion of the geological region. The methods further includes simulating, using a forward stratigraphic modeler, a predicted stratigraphic thickness map for each cell based on the initial subsidence sequence, then iteratively, forming an objective function for each cell based, at least in part, on the observed stratigraphic thickness map and the predicted stratigraphic thickness map, determining if the objective function for each cell satisfies a stopping criterion, and updating the subsidence sequence for cells not satisfying the criterion.

Abstract: A method for formation pore pressure prediction involves obtaining an input parameter set while drilling a well. The input parameter set includes surface drilling parameters, logging while drilling parameters, and advanced mud gas measurements. The method further involves generating, from the input parameter set, a reduced input parameter set, by eliminating at least one input parameter of the input parameter set that is considered non-relevant for predicting the pore pressure, and predicting the pore pressure by applying a machine learning model to the reduced input parameter set.

Abstract: A method and a system for estimating a thermal maturity of a rock sample of a subterranean region of interest are disclosed. The method includes preparing a plurality of rock samples of the subterranean region of interest and obtaining an image of an organic matter sample from the plurality of the rock. Further, the histograms are obtained based on RGB pixel values extracted from the image of the organic matter sample and a functional relationship describing the histograms is determined. Additionally, the method includes constructing a regression model using weight values of the functional relationship as input values and estimating the thermal maturity of the rock sample of the subterranean region of interest based on the constructed regression model.

Abstract: A method for determining a property of a formation, including the steps: drilling a well in the formation, collecting drill cuttings from the well, taking a digital image of each drill cutting, entering each digital image to a trained first model that outputs a predicted lithology class of each drill cutting from each digital image, taking a random number of X-ray diffraction (XRD) images of the drill cuttings, while at least one XRD image is selected from each lithology class, entering each XRD image and the corresponding digital image into a trained second model that predicts a property of the drill cuttings, and determining the property of the formation by determining the properties of the drill cuttings as a function of the depth of the drill cuttings.

Abstract: A geological core inspection system that includes a table to support core samples for inspection, a robotic geological core inspection system including a core sample sensing system to acquire sample inspection data (including an imaging sensor and a core sample position sensor), a core sample interaction system (including a dispensing system and a scoring system), and a robotic positioning system, and a control and communications system to provide for remote control of the core sample sensing system. The system further including a remote geological core inspection system to receive and communicate remote commands specifying requested operations of the robotic geological core inspection system (the control and communications system adapted to control operation of the core sample sensing system in response to the remote commands to perform the requested operations) and receive and present core data.

Abstract: A method for formation pore pressure prediction involves obtaining an input parameter set while drilling a well. The input parameter set includes surface drilling parameters, logging while drilling parameters, and advanced mud gas measurements. The method further involves generating, from the input parameter set, a reduced input parameter set, by eliminating at least one input parameter of the input parameter set that is considered non-relevant for predicting the pore pressure, and predicting the pore pressure by applying a machine learning model to the reduced input parameter set.

Abstract: A geological core inspection system that includes a table to support core samples for inspection, a robotic geological core inspection system including a core sample sensing system to acquire sample inspection data (including an imaging sensor and a core sample position sensor), a core sample interaction system (including a dispensing system and a scoring system), and a robotic positioning system, and a control and communications system to provide for remote control of the core sample sensing system. The system further including a remote geological core inspection system to receive and communicate remote commands specifying requested operations of the robotic geological core inspection system (the control and communications system adapted to control operation of the core sample sensing system in response to the remote commands to perform the requested operations) and receive and present core data.

Abstract: A system for estimating a pore pressure value associated with a depth of a well subject to drilling operations may include a data repository for storing integrated mud gas and pore pressure data associated with one or more existing wells. The data repository may also store a machine learning (ML) engine. The system may also include one or more hardware processors configured to train a ML model using the ML engine and the integrated mud gas and pore pressure data, to estimate, during the drilling operations, the pore pressure value of a formation zone, at the depth of the well, using the trained ML model and mud gas data associated with a depth value that identifies the depth of the well subject to the drilling operations, and to update a drilling program for a production system based on the estimated pore pressure value.

Abstract: A method of creating a focused image of a sample includes: acquiring a first image of the sample at a first height of a focal plane; acquiring a second image of the sample at a second height of the focal plane; creating a mask from the first image; calculating a first metric for a first pixel in the first image, wherein the first pixel is not covered by the mask; calculating a second metric for a second pixel in the second image, wherein the second pixel is not covered by the mask; and constructing the focused image of the sample from data of the first pixel and data of the second pixel based on the first metric and the second metric.

Abstract: A method for generating a core description is disclosed. The method includes coring and collecting rock cores from geographical locations in the subterranean formation, detecting, using an augmented reality (AR) device worn by a user, content of an identifying tag of a rock core within a device view of the AR device to identify a well where the rock core is obtained, retrieving, by the AR device from a data repository, historical data of the well, activating, by the AR device, a sensor to acquire additional data from the rock core to supplement the historical data, and presenting, by the AR device, an AR image including a first image of the historical data and the additional data superimposed over a second image of the rock core, where the user generates the core description based on viewing the AR image.

Abstract: A method for determining a real-time pore pressure log of a well in a reservoir, including the steps: storing existing data logs of surface drilling parameters, logging while drilling (LWD), and mud gas of existing wells in a database, storing existing pore pressure logs of the existing wells in the database, wherein the existing pore pressure logs correspond to the existing data logs, determining a relationship between the existing data logs and the existing pore pressure logs, drilling a new well into the reservoir, determining new data logs of surface drilling parameters, LWD, and mud gas of the new well while drilling the new well, inputting the new data logs of the new well into the relationship while drilling the new well, determining a real-time pore pressure log of the new well by outputting an estimated pore pressure at a certain depth by the relationship while drilling the new well.

Abstract: A method for determining a property of a formation, including the steps: drilling a well in the formation, collecting drill cuttings from the well, taking a digital image of each drill cutting, entering each digital image to a trained first model that outputs a predicted lithology class of each drill cutting from each digital image, taking a random number of X-ray diffraction (XRD) images of the drill cuttings, while at least one XRD image is selected from each lithology class, entering each XRD image and the corresponding digital image into a trained second model that predicts a property of the drill cuttings, and determining the property of the formation by determining the properties of the drill cuttings as a function of the depth of the drill cuttings.

Abstract: A modular system for analyzing drilled cuttings includes a sampler unit, a washer unit, an analysis unit, and a central processing unit. The sampler unit receives the drilled cuttings from a shale shaker disposed on a rig site that obtains the drilled cuttings. The washer unit removes debris from the drilled cuttings. The analysis unit determines lithological properties of the drilled cuttings. The packager unit packages the drilled cuttings. The central processing unit coordinates operations to process the drilled cuttings through each of the sampler unit, washer unit, analysis unit and packager unit. The central processing unit facilitates a processing link among the sampler unit, washer unit, analysis unit and packager unit so that the sampler unit, washer unit, analysis unit and packager unit are integrated to form the modular system.

Abstract: A system and method for determining a noise-attenuated wellbore image is disclosed. The method includes obtaining a plurality of training images of a first wellbore wall portion, where each training image includes a first signal component and a first noise component, and training, using the plurality of training images, an artificial neural network to estimate the first signal component of one of the plurality of training images. The method further includes obtaining an application image of a second wellbore wall portion, including a second signal component and a second noise component, and determining the noise-attenuated wellbore image by applying the trained artificial neural network to the application image, wherein the noise-attenuated wellbore image comprises the second signal component.

Abstract: A modular geologic core examination table is claimed. The modular geologic core examination table includes at least two legs, each leg comprising an extending portion for increasing the effective length of each leg, the extending portion at least partially concentrically disposed within or around the leg from which it extends, and wheels affixed to each of the at least two legs, wherein the wheels comprise braking functionality. The modular geologic core examination table also includes a tabletop coupled to the at least two legs, the tabletop including a top surface for receiving a core sample tray configured to hold the geologic core samples, wherein the tabletop is disposed above an automated guided vehicle (AGV) configured to navigate the modular core examination table while carrying geologic core samples from one geologic processing station to another without collisions.

Abstract: A geologic core inspection table includes at least two legs, each including an extending portion for increasing the effective length of each leg, the extending portion at least partially concentrically disposed within or about the leg from which it extends; and a tabletop disposed above and coupled to the legs. The tabletop includes a top surface for receiving a core sample tray. At least one leg may be extended to a different height than at least one other leg.

Abstract: A method may include obtaining a request to determine automatically a depth of a formation top for a well in a geological region of interest. The method may include obtaining various well logs regarding the well and various wells in the geological region of interest. The method may include determining various depth values using the various well logs and a statistical interpolation method. The method may further include determining a final depth of the well using the various depth values and a searching method.

Abstract: A method including obtaining, by a computer processor, at least one key log in each of a set of training wells located, at least partially, within a hydrocarbon reservoir, identifying a target formation bounding surface in each of the set of training wells, and generating an initial depth surface for the target formation bounding surface from the target formation bounding surface in each of the set of training wells. The method further including, determining from the initial depth surface an initial depth estimate of the target formation bounding surface at a location of a current well, forming an objective function based, at least in part on a correlation between each key log in each of the set of training wells, and each corresponding key log in the current well, and optimizing the objective function by varying a depth shift between each of the set of training wells and the current well, to determine an optimum depth shift that produces an extremum of the objective function.

Abstract: An example system may be used to automate one or more processes relating to drill cutting handling, cleaning, and packing to produce consistently high-quality cleaned samples. In some implementations, an example system may employ cleaning processes including centrifugal and ultrasonic processes to clean a sample automatically without the aid of a human operator thereby increasing efficiency and quality of the produced samples for better subsequent sample analysis. A system may include an unloading module, a cleaning module, and packing module, all of which may be operated in combination with a robotic arm.