A method is claimed for automatically transforming sonic well logs from a depth domain to a seismic two-way travel-time domain. The method includes obtaining a training well with a measured sonic well log in the depth domain and a borehole seismic dataset in the depth domain and obtaining an application well with only a measured sonic well log in the depth domain. The method further includes training a network to predict a transformed sonic well log for the training well based, at least in part, on the measured sonic well log and the borehole seismic dataset in the training well, and predicting with the network, the transformed sonic well log in the application well.

A method is claimed for automatically transforming sonic well logs from a depth domain to a seismic two-way travel-time domain. The method includes obtaining a training well with a measured sonic well log in the depth domain and a borehole seismic dataset in the depth domain and obtaining an application well with only a measured sonic well log in the depth domain. The method further includes training a network to predict a transformed sonic well log for the training well based, at least in part, on the measured sonic well log and the borehole seismic dataset in the training well, and predicting with the network, the transformed sonic well log in the application well.

A system can receive seismic data that can correlate to a subterranean formation. The system can derive a set of seismic attributes from the seismic data. The seismic attributes can include discontinuity-along-dip. The system can determine parameterized results by analyzing the seismic data and the seismic attributes using a deep learning neural network. The deep learning neural network can include a dilation module. The system can determine one or more fault probabilities of the subterranean formation using the parameterized results. The system can output the fault probabilities for use in a hydrocarbon exploration operation.

A mud pulse telemetry valve assembly and method including a mud valve sub and a mud pulse telemetry valve. The mud pulse telemetry valve having a flow tube positioned within the mud valve sub in a way that at least a portion of an outer surface of the flow tube is spaced a distance from at least a portion of an inner surface of the mud valve sub to form a hydraulic passageway having an upstream end and a downstream end. The mud pulse telemetry valve further having a control valve assembly and a pilot valve assembly.

A rock-piercing flexible rock drilling robot and a rock breaking method therefor are disclosed. The robot includes a control system, a head, and at least one tail. The head includes a head housing, a propulsion turntable, a drilling mechanism, a hydraulic propulsion system, a first driving mechanism, and a second driving mechanism. The propulsion turntable includes a drill bit located at a center thereof and a cutting turntable arranged around the drill bit. The first driving mechanism is connected to the drill bit, and the second driving mechanism is connected to the cutting turntable. The tail includes a tail housing, an advancing and retreating power system, and a fixed support system. The head and the tail are connected through a flexible component, and the tails are connected through flexible components.

In one aspect, a system (5) for use in providing an approximation or estimation of a characteristic (for example, a bulk density value) of a deposit subject to a drilling operation is disclosed. In one form, the system (5) comprises a processor module (25) arranged in operable association with a network of sensors (30) operable for measuring one or more parameters relating to the operation of the drilling assembly (10). The processor module (25) is configured operable for receiving data/information derived from the network of sensors (30), and processing the data/information so as to provide a representation of the incursion (eg. depth of penetration into the relevant deposit) achieved by way of the drilling assembly (10). The processor module (25) is further configured for processing the representation of the incursion with a predetermined relationship that is characteristic of, or unique to, the drilling assembly (10) for providing or allowing an approximation/estimation of the characteristic of the deposit as a function of one or more parameters of the incursion to be made.

A method and a system for well log data quality control is disclosed. The method includes obtaining a well log data regarding a geological region of interest, verifying an integrity and a quality of the well log data, determining the quality of the well log data based on a quality score of the well log data and making a determination regarding the access to the databases based on the quality of data. Additionally, the method includes performing the statistical analysis and the classification of well log data, a predictive and a prescriptive analysis of trends and predictions of the well log data, and generating an action plan for datasets with unsatisfactory quality scores.

A support arm can be positioned in an inner area of a tubular body. The support arm can extend from the inner surface of the tubular body to retain a sensor in flow from a wellbore blowout passing through the tubular body.

A system for determining completion parameters for a wellbore includes a sensor and a computing device. The sensor can be positioned at a surface of a wellbore to detect data prior to finishing a completion stage for the wellbore. The computing device can receive the data, perform a history match for simulation and production using the sensor data and historical data, generate inferred data for completion parameters using the historical data identified during the history match, predict stimulated area and production by inputting the inferred data into a neural network model, determine completion parameters for the wellbore using Bayesian optimization on the stimulated area and production from the neural network model, profit maximization, and output the completion parameters for determining completion decisions for the wellbore.

A system for determining completion parameters for a wellbore includes a sensor and a computing device. The sensor can be positioned at a surface of a wellbore to detect data prior to finishing a completion stage for the wellbore. The computing device can receive the data, perform a history match for simulation and production using the sensor data and historical data, generate inferred data for completion parameters using the historical data identified during the history match, predict stimulated area and production by inputting the inferred data into a neural network model, determine completion parameters for the wellbore using Bayesian optimization on the stimulated area and production from the neural network model, profit maximization, and output the completion parameters for determining completion decisions for the wellbore.