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.

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.

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.

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.

An acoustic logging tool may comprise a center load carrying pipe, a receiver module connected to the center load carrying pipe, one or more transmitter modules connected to the center load carrying pipe, and one or more mass modules connected to the center load carrying pipe.

An acoustic logging tool may comprise a center load carrying pipe, a receiver module connected to the center load carrying pipe, one or more transmitter modules connected to the center load carrying pipe, and one or more mass modules connected to the center load carrying pipe.

A method for steering a well based on rock properties and obtaining natural fracture information includes inducing tube waves in the well during drilling the well. Acoustic energy is measured in the well. The energy comprises tube wave reflections from formations adjacent to the well. The measured acoustic energy is inverted to determine at least one of a rock property, a near wellbore hydraulic conductivity, and natural fracture occurrence. A trajectory of the well is adjusted to maintain the at least one of a rock property, near wellbore hydraulic conductivity and natural fracture occurrence. An n optimized, well-customized hydraulic fracturing design may be created based on the measured natural fracture properties. A method to optimize hydraulic fracturing treatment based on measured natural fracture properties during drilling.

Systems and methods include a computer-implemented method for predicting fluid holdups along the borehole or the pipe on surface and to perform fluid typing and fluid properties characterization. Acoustic waves are transmitted by an array of acoustic wave transducers. Each transducer is configured to transmit acoustic waves at a different frequency. The acoustic waves are received by an array of acoustic wave receivers fixed on the bow centralizer on the tool used in the borehole. Each receiver is configured to receive only the given frequency of a given transducer, forming a receiver-transducer pair for the given frequency. Acoustic speeds measured at each given frequency and analyzed. A model is generated based on the analyzing. The model is configured to predict fluid holdups across the borehole and to perform fluid typing and fluid properties characterization in one phase, two phase, and three phase applications of gas, oil, and water.

Systems and methods include a computer-implemented method for predicting fluid holdups along the borehole or the pipe on surface and to perform fluid typing and fluid properties characterization. Acoustic waves are transmitted by an array of acoustic wave transducers. Each transducer is configured to transmit acoustic waves at a different frequency. The acoustic waves are received by an array of acoustic wave receivers fixed on the bow centralizer on the tool used in the borehole. Each receiver is configured to receive only the given frequency of a given transducer, forming a receiver-transducer pair for the given frequency. Acoustic speeds measured at each given frequency and analyzed. A model is generated based on the analyzing. The model is configured to predict fluid holdups across the borehole and to perform fluid typing and fluid properties characterization in one phase, two phase, and three phase applications of gas, oil, and water.