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: A method for formation properties prediction in near-real time. The method may include obtaining lab measurements of existing drill cuttings at a plurality of depths of a first well. The method may include obtaining historical drilling surface data at the plurality of depths from a plurality of wells. The method may include obtaining real-time digital photos and real-time drilling surface data of new drill cuttings at a new depth of a new well. The method may include generating, using a prediction model, predicted formation properties of the new drill cuttings based on the real-time digital photos, the real-time drilling surface data, and the new depth. The method may include predicting, using a near-real-time model and the predicted formation properties, near-real-time formation properties in the new well, wherein the prediction model comprises a historical model that employs a machine-learning algorithm.

Abstract: A method includes building a mud-gas hydrocarbon oil fraction database comprising historical data, training a machine learning model using the historical data in the mud-gas hydrocarbon oil fraction database, drilling a new wellbore, processing drilling mud returns, from the new wellbore, through a gas sampler comprising a gas chromatograph and a gas mass spectrometer, retrieving real-time mud-gas data from the gas sampler, and generating a real-time hydrocarbon oil fraction log for the new wellbore by processing the real-time mud-gas data through the trained machine learning model and producing estimated hydrocarbon oil fraction data.

Abstract: A method for identifying the most representative well logs for the purpose of locating the top of a hydrocarbon-bearing formation employs the Pearson Correlation Coefficient by quantifying the consistency of historic previous top formation picks from a historic database of well data and corresponding logging data associated with each well in a defined oil field to provide Well Correlation Scores from which are derived Log Correlation Scores that are employed in accordance with the disclosure to provide a Top Correlation Score that is indicative of the relative reliability of the location of a top pick for a specific well in the defined oil field.

Abstract: Systems and methods for determining a continuous grain size log from a collection of petrographic thin section images are provided. Thin section images from core samples from one or more wells may be obtained and analyzed to estimate grain sizes. Using wireline logs from the one or more wells and the estimated grain sizes, a data structure (for example, a database) of grain sizes and wireline logs at depths may be constructed. The data structure may be used to train a machine learning model. Next, a wireline tool may be used to obtain wireline logs in a new well, and a continuous grain size log may be determined from the wireline logs of using the machine learning model. Computer-readable media for determining reservoir rock grain sizes from a collection of petrographic thin section images is also provided.

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: Systems and methods for determining a continuous grain size log from a collection of petrographic thin section images are provided. Thin section images from core samples from one or more wells may be obtained and analyzed to estimate grain sizes. Using wireline logs from the one or more wells and the estimated grain sizes, a data structure (for example, a database) of grain sizes and wireline logs at depths may be constructed. The data structure may be used to train a machine learning model. Next, a wireline tool may be used to obtain wireline logs in a new well, and a continuous grain size log may be determined from the wireline logs of using the machine learning model. Computer-readable media for determining reservoir rock grain sizes from a collection of petrographic thin section images is also provided.

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: Embodiments of the present disclosure aim to provide advanced log scale permeability estimation systems and methods. In an embodiment, a computer system is provided and configured to perform petrographic thin section image permeability analysis and then generate log scale permeability estimations for a well using machine learning methods and techniques. In an embodiment, the advanced log scale permeability estimation systems and methods are configured with a computer system or a micro-computer system, that can be configured with a computer circuit board comprising a processor, memory, networking capability, and software. In another embodiment the log scale permeability estimations for a well are input into a reservoir simulation model to determine hydrocarbon resources and to make portfolio predictions for a well.

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: Embodiments of the present disclosure aim to provide advanced log scale permeability estimation systems and methods. In an embodiment, a computer system is provided and configured to perform petrographic thin section image permeability analysis and then generate log scale permeability estimations for a well using machine learning methods and techniques. In an embodiment, the advanced log scale permeability estimation systems and methods are configured with a computer system or a micro-computer system, that can be configured with a computer circuit board comprising a processor, memory, networking capability, and software. In another embodiment the log scale permeability estimations for a well are input into a reservoir simulation model to determine hydrocarbon resources and to make portfolio predictions for a well.

Abstract: A machine, computer program product, and method to enable scalable parallel reservoir simulations for a variety of simulation model sizes are described herein. Some embodiments of the disclosed invention include a machine, methods, and implemented software for performing parallel processing of a grid defining a reservoir or oil/gas field using a plurality of sub-domains for the reservoir simulation, a parallel process of re-ordering a local cell index for each of the plurality of cells using characteristics of the cell and location within the at least one sub-domain and a parallel process of simulating at least one production characteristic of the reservoir.

Abstract: Example embodiments utilize machines to model reservoir geometry having geological layers as 2.5D unstructured grids. Example embodiments include program products to simulate a reservoir by generating a reservoir data system, performing a numerical fluid flow simulation, and visualizing the simulation. Data system embodiments include data structures to model a reservoir geometry as laterally unstructured two-dimensional (2D) grids and associated layer depths defining z-lines to thereby define a 2.5D unstructured grid, including datasets for: vertices of the grid cells for the future grid top and bottom surfaces, a number and listing of vertices for each grid cell, cell center coordinates, and vertex adjacency information using a compressed sparse row format. Computer-implemented methods include projecting external and internal boundaries onto a future grid surface; generating 2D unstructured, e.g.

Abstract: A machine, computer program product, and method to enable scalable parallel reservoir simulations for a variety of simulation model sizes are described herein. Some embodiments of the disclosed invention include a machine, methods, and implemented software for performing parallel processing of a grid defining a reservoir or oil/gas field using a plurality of sub-domains for the reservoir simulation, a parallel process of re-ordering a local cell index for each of the plurality of cells using characteristics of the cell and location within the at least one sub-domain and a parallel process of simulating at least one production characteristic of the reservoir.

Abstract: A machine, computer program product, and method to enable scalable parallel reservoir simulations for a variety of simulation model sizes are described herein. Some embodiments of the disclosed invention include a machine, methods, and implemented software for performing parallel processing of a grid defining a reservoir or oil/gas field using a plurality of sub-domains for the reservoir simulation, a parallel process of re-ordering a local cell index for each of the plurality of cells using characteristics of the cell and location within the at least one sub-domain and a parallel process of simulating at least one production characteristic of the reservoir.

Abstract: A machine, computer program product, and method to enable scalable parallel reservoir simulations for a variety of simulation model sizes are described herein. Some embodiments of the disclosed invention include a machine, methods, and implemented software for performing parallel processing of a grid defining a reservoir or oil/gas field using a plurality of sub-domains for the reservoir simulation, a parallel process of re-ordering a local cell index for each of the plurality of cells using characteristics of the cell and location within the at least one sub-domain and a parallel process of simulating at least one production characteristic of the reservoir.

Abstract: A machine, computer program product, and method to enable scalable parallel reservoir simulations for a variety of simulation model sizes are described herein. Some embodiments of the disclosed invention include a machine, methods, and implemented software for performing parallel processing of a grid defining a reservoir or oil/gas field using a plurality of sub-domains for the reservoir simulation, a parallel process of re-ordering a local cell index for each of the plurality of cells using characteristics of the cell and location within the at least one sub-domain and a parallel process of simulating at least one production characteristic of the reservoir.

Abstract: Example embodiments utilize machines to model reservoir geometry having geological layers as 2.5D unstructured grids. Example embodiments include program products to simulate a reservoir by generating a reservoir data system, performing a numerical fluid flow simulation, and visualizing the simulation. Data system embodiments include data structures to model a reservoir geometry as laterally unstructured two-dimensional (2D) grids and associated layer depths defining z-lines to thereby define a 2.5D unstructured grid, including datasets for: vertices of the grid cells for the future grid top and bottom surfaces, a number and listing of vertices for each grid cell, cell center coordinates, and vertex adjacency information using a compressed sparse row format. Computer-implemented methods include projecting external and internal boundaries onto a future grid surface; generating 2D unstructured, e.g.