Aspects of the disclosure provide for a method using clusters of sonic peaks from a logging tool to generate a log of an acoustic property of the formation as a function of depth.

Systems and methods include a method for generating a high-resolution advanced three-dimensional (3D) transient model that models multiple wells by integrating pressure transient data into a static geological model. A crude 3D model is generated from a full-field geological model that models production for multiple wells in an area. A functional 3D model is generated from the crude 3D model. An intermediate 3D model is generated by calibrating the functional 3D model with single-well data. An advanced 3D transient model is generated by calibrating multi-well data in the functional 3D model.

A well tool can be used in a wellbore that can measure characteristics of an object in the wellbore. The well tool includes an ultrasonic transducer for generating an ultrasonic wave in a medium of the wellbore. The ultrasonic transducer includes a front layer, a rear layer, backing material coupled to the rear layer, and piezoelectric material coupled to the front layer and to the backing material. The rear layer can improve signal-to-noise ratio of the transducer in applications such as imaging and caliper applications.

A method can include receiving multi-channel time series data of drilling operations; training a deep neural network (DNN) using the multi-channel time series data to generate a trained deep neural network as part of a computational simulator where the deep neural network includes at least one recurrent unit; simulating a drilling operation using the computational simulator to generate a simulation result; and rendering the simulation result to a display.

An automatic microseismic monitoring-intelligent rockburst early warning integrated method is further provided.

A system for drilling a wellbore into an earth formation includes a logging tool in the wellbore having at least one near-range measurement sensor, and a processor. The processor is configured to receive, at each depth along the wellbore, near-range measurement data and reference data related to a density of the formation, determine one or more near-range earth models that include a density model of a layer at each depth based on the near-range data constrained by the reference data, receive surface gravitational data from multiple surface locations, determine a mid-range or far-range formation model based on the near-range earth model and the surface gravitational data, and provide the mid-range or far-range formation model to a well driller for geosteering a drill bit into the earth formation.

The present invention discloses a physical embedded deep learning formation pressure prediction method, device, medium and equipment, the present invention characterizes seismic attenuation by logging impedance quality factor Q, based on the Q value and rock physics model of formation pressure, the physical mechanism of this kind of certainty replace Caianiello convolution neurons of the nonlinear activation function, using the convolution neurons, build deep learning convolution neural networks (CCNNs), can greatly increase the stress inversion precision and learning efficiency, get accurate formation pressure prediction results. Compared with the prior art, the present invention uses acoustic attenuation instead of the traditional acoustic velocity to characterize formation pressure, and solves the problem that the traditional pressure prediction method based on velocity has strong multiple solutions due to high gas content and complex structure.

An automated slide drilling system (ASDS) may be used with a drilling rig system to control slide drilling. The ASDS may autonomously control slide drilling without user input during the slide drilling. The ASDS may further support a transition from rotary drilling to slide drilling to rotary drilling without user input during the transitions. The ASDS may also support user input and user notifications for various steps to enable manual or semi-manual control of slide drilling by a driller or an operator.

A method is provided for determining a lithology of a subterranean formation into which a wellbore has been drilled. The method includes receiving a set of measurement logs including one or more measurement logs, each representing a measured characteristic of the wellbore plotted according to depth. The method also includes segmenting the wellbore into regions based on identified change of trend in one or more of the measurement logs of the set, and sub-segmenting at least one region into zones based on detection of appearance or disappearance of a rock type in the cuttings percentage log, The method also includes determining, in each zone, a location, length and rock type of one or more layers based on a total percentage of each rock type in the zone in the cuttings percentage log and at least one of the additional measurement logs.

Waves from cement bond logging with a sonic logging-while-drilling tool (LWD-CBL) are often contaminated with tool waves and may yield biased CBL amplitudes. The disclosed LWD-CBL wave processing corrects the first echo amplitudes of LWD-CBL before calculating the BI. The LWD-CBL wave processing calculates a tool wave amplitude and a phase angle difference as the difference of the phases between the tool waves and casing waves. The tool waves are then used to correct the LWD-CBL casing wave amplitude and remove errors introduced from tool waves. In conjunction with the sets of operations described, the LWD-CBL wave processing also include array preprocessing operations. Array preprocessing may employ variation of bandpass filtering and frequency-wavenumber (F-K) filtering operations to suppress tool wave.