Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving seismic data acquired by at least one receiver of a geologic survey system configured to perform a geologic survey of a subterranean formation, wherein the seismic data is associated with reflected acoustic signals generated by at least one source of the geologic survey system; calculating a ground force signal by stacking the acoustic signals generated by the least one source; calculating, using the ground force signal, a time and depth variant quality factor (Q) of the subterranean formation; and compensating, based on the time and depth variant Q, attenuation in the seismic data.

A method and system for locating a reflector in a formation. The method may comprise broadcasting a sonic waveform as a shear formation body wave or a compressional formation body wave into the formation, recording a reflected wave from a reflector with the one or more receivers as dipole data by the dipole receiver and quadrupole data by the quadrupole receiver, and processing the dipole data and the quadrupole data with an information handling system to determine a location of the reflector from the borehole sonic logging tool. The system may comprise a borehole sonic logging tool and an information handling system. The borehole sonic logging tool may comprise one or more transmitters configured to transmit a sonic waveform into a formation and one or more receivers configured to record a reflected wave as a dipole receiver for dipole data and a quadrupole receiver for quadrupole data.

An apparatus and method utilize a trained machine learning model to synthesize formation evaluation data such as formation tops and LWD logs. In some instances, the synthesis of formation evaluation data may further be based upon drilling mechanics data collected during drilling, thus effectively enabling formation evaluation data to be synthesized primarily based upon surface measurements collected in real time, and in many cases without the need for collecting downhole measurements during drilling. In addition, in some instances, a machine learning model implemented as a generative adversarial network (GAN) may be used to synthesize formation evaluation data, with drilling mechanics data collected during drilling also used in some instances.

A method for designing a physical component of a drilling system includes defining input parameters of a first primitive in the drilling system, simulating the first primitive to obtain a performance parameter of the first primitive, designing, based on the performance parameter and to obtain a design, the physical component for the drilling system, and storing the design. The physical component has the input parameters of the first primitive. The input parameters include at least one control relationship between a first control point on the first primitive and a second control point.

A method of evaluating a subterranean formation includes conveying a tool along a borehole. The tool includes a transmitter that transmits a drive pulse and a receiver that receives at least one formation response to the drive pulse. The method further includes calculating a signal-to-noise ratio of the at least one formation response and comparing the signal-to-noise ratio to a programmable threshold. The method further includes determining, based on the comparing, an adjusted drive pulse to transmit and transmitting the adjusted drive pulse. The method further includes and receiving at least one formation response to the adjusted drive pulse and deriving formation data from the at least one formation response to the adjusted drive pulse. The method further includes displaying a representation of the formation based on the formation data.

A system, method, and a computer program configured to perform a method for processing drilling data. The method includes transmitting a signal with a telemetry tool to a computer processor. The method includes applying a plurality of predetermined templates to the signal. The method also includes applying a plurality of first filters to the transmitted signal. The method also includes applying one or more second, adjustable filters to the transmitted signal and to the plurality of predetermined templates. The method also includes decoding the transmitted signal based on the best match between a) two or more of the plurality of predetermined templates, and b) the transmitted signal, wherein the two or more of the plurality of predetermined templates and the transmitted signal are processed through the same adjustable filter of the one or more second, adjustable filters.

Near real-time methodologies for maximizing return-on-fracturing-investment for shale fracturing. An example system can calculate, based on sonic data and density data, mechanical properties and closure stress of a portion of shale rocks for fracture modeling. The system can generate one or more rock mechanical models based on the mechanical properties and closure stress of the portion of shale rocks, and perform one or more fracture modeling simulations based on one or more treatment parameter values. Based on the one or more fracture modeling simulations, the system can generate a neural network model which predicts a fracture productivity indicator of an effective propped area (EPA) and/or an effective propped length (EPL), and calculate a return-on-fracturing-investment (ROFI) based on the EPA or EPL predicted by the neural network model.

A measurement-while-drilling method and device assesses outburst risk and evaluates gas drainage performance of a coal seam. The device includes a compartment, a pressure sensor, a temperature sensor, a flow sensor, an electromagnetic sensor, an acoustic sensor, a wave velocity measurement module, a monitoring and control module, a power supply and a communication interface. These are installed between a drill bit and an inclinometer or a first drill pipe. Measurements are performed while drilling to obtain a real time gas parameter, lithologic and coal seam information, for on-site assessment of outburst risk. During drill bit replacement, gas pressure, temperature, flow velocity, wave velocity, electromagnetic radiation, and an acoustic signal are recorded in real time, to calculate stress of the coal seam and coal seam outburst risk. On-site measurement is done while drilling without sampling and can use multiple parameters to perform synchronous measurement to obtain a comprehensive evaluation.

A procedure that integrates petrophysics and rock typing taken from vertical well measurements, 3D seismic elastic properties and seismic attributes, and geostatistical modeling to build a 3D reservoir model is provided. The 3D reservoir model may be directly incorporated into horizontal fracture model designs. The developed 3D reservoir model for a subsurface volume may be used in a fracture model to optimize fracturing designs and maximize well performance.

In a method for synchronizing acoustic asynchronous serial signals while drilling, communication and high-voltage signal isolation between a transmitting terminal and a receiving master control board are realized in an asynchronous serial communication way. The receiving master control board serving as a master control terminal configures parameters for the transmitting terminal in the asynchronous serial communication way and controls the excitation of a sound source. The transmitting terminal is a synchronous sound source excitation signal initiator; signal synchronization is realized under an asynchronous communication condition due to the coordination of the transmitting terminal and the receiving master control board; the transmitting terminal includes a plurality of transmitting boards, and asynchronous serial communication between each of the transmitting boards and the receiving master control board is realized through two data lines R+ and R.