Abstract: A method for formation properties prediction in near-real time 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: Automated drill cutting monitoring system includes a digital imaging device mounted to a shale shaker of a wellbore drilling assembly and a computer system. The digital imaging device captures digital images of solid objects released when drilling a subterranean zone. The computer system receives the digital images and determines a space occupied by the solid objects on the shale shaker. Using the space occupied by the solid objects on the shale shaker, the computer system determines wellbore conditions.

Abstract: A method for monitoring solids content during drilling operations may include collecting real-time cuttings image data at a surface outlet of a natural resource well, determining cuttings characteristics data based on the real-time cuttings image data, collecting real-time surface mud data, and determining real-time, one-dimensional downhole cuttings information based on a multi-dimensional computational fluid dynamics model. The cuttings characteristics data may include cuttings size distribution, cuttings volume, cuttings velocity, cuttings orientation, cuttings area, or combinations thereof. The real-time surface mud data may include inlet mud parameters, drilling operational parameters, well planning parameters, or combinations thereof.

Abstract: A system for controlling and optimizing hydrocarbon production may include sensors that capture sensor data pertaining to wellhead pressure values in a well. The system may also include a multiphase flow meter that captures production data pertaining to multiphase production flow rates of the well. The system may include an access module to access an estimated parameter value associated with a second time and a parameter that pertains to production. The estimated parameter value is predicted by a data-driven model for describing production fluid dynamics of the well, based on the sensor data and the production data obtained at a first time. The system includes a processor to update the data-driven model using a data assimilation algorithm and the production data received at the second time. The processor generates, using the updated data-driven model, an optimal control setting of a control tool for causing an adjustment to a production system.

Abstract: A method may include obtaining, from various sensors, acquired sensor data regarding various multiphase flows in a multiphase flow meter that are sampled at a predetermined sampling frequency. The acquired sensor data may describe various transient signals that correspond to various gas droplets. The method may further include generating, based on the acquired sensor data, a flow model for the multiphase flow meter. The method may further include updating the flow model to produce a first updated flow model using simulated flow data. The method may further include updating the first updated flow model to produce a second updated flow model using simulated sensor data. The second updated flow model may be used to determine one or more flow rates within a multiphase flow.

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: There is disclosed an educational system including a science experimental set and a computer system, the science experimental set comprising experimental set items, and the computer system including a processor, a detector and a display, the computer system configured to display educational media content on the display relating to the science experimental set in response to the detector detecting an item in the science experimental set, and the processor identifying the media content to be displayed based on the detection of the item. Related methods, computer program products and kits of parts are disclosed.

Abstract: A method can include receiving stimulation treatment scenario definitions for stimulation treatment of a reservoir that includes hydrocarbons; receiving reservoir data; receiving imagery data of a proppant pack; generating a model of the proppant pack based at least in part on the imagery data; simulating physical phenomena associated with a plurality of the stimulation treatment scenarios based at least in part on the model to generate simulation results; and, based at least in part on the simulation results, selecting parameter values for a stimulation treatment.

Abstract: There is disclosed an educational system including a science experimental set and a computer system, the science experimental set comprising experimental set items, and the computer system including a processor, a detector and a display, the computer system configured to display educational media content on the display relating to the science experimental set in response to the detector detecting an item in the science experimental set, and the processor identifying the media content to be displayed based on the detection of the item. Related methods, computer program products and kits of parts are disclosed.