Systems and methods include a computer-implemented method for determining normalized apparent power. Drilling acoustic signals corresponding to a time domain and generated during drilling of a well. A fast Fourier transformation (FFT) is performed using the drilling acoustic signals to generate FFT data. Normalized FFT data is generated using normalization parameters and a drill string rotation rate record of a drill string used to drill the well. The drill string rotation rate is received during drilling. Normalized apparent power is determined from data points of a predetermined top percentage of the normalized FFT data within a lithological significant frequency range. The normalized apparent power is a measure of the power of the drilling acoustic signals and it is a function of the amplitude and frequency of the normalized FFT data. The lithological significant frequency range is a frequency range within which the drill sounds are more closely related with lithology.

Various embodiments include apparatus and methods to estimate properties of rock, drill bit, or a combination thereof associated with a drilling operation. The properties can include, but are not limited to, rock chip size, drill bit dullness, drilling efficiency, or a combination selected from rock chip size, drill bit dullness, and drilling efficiency. The estimate may be accomplished from correlating detected acoustic emission with detected electromagnetic emissions. In various embodiments, formation brittleness may be determined. The various estimates may be used to direct a drilling operation. Additional apparatus, systems, and methods are disclosed.

A sonic tool is activated in a well having multiple casings and annuli surrounding the casing. Detected data is preprocessed using slowness time coherence (STC) processing to obtain STC data. The STC data is provided to a machine learning module which has been trained on labeled STC data. The machine learning module provides an answer product regarding the states of the borehole annuli which may be used to make decision regarding remedial action with respect to the borehole casings. The machine learning module may implement a convolutional neural network (CNN), a support vector machine (SVM), or an auto-encoder.

A multi-wavefield seismic detection method and system based on construction noise of a shield machine. Multi-wavefield seismic information such as a body wave and a surface wave formed during propagation of a seismic wave generated by excitation in a stratum is obtained by using noise information caused by the construction of a shield machine as a seismic source, a stratum velocity model along a tunnel is constructed through joint inversion, and reflection wave information or the like is used for migration imaging, to eventually implement relatively accurate detection of a geological condition in front of a tunnel face of shield construction.

A multi-wavefield seismic detection method and system based on construction noise of a shield machine. Multi-wavefield seismic information such as a body wave and a surface wave formed during propagation of a seismic wave generated by excitation in a stratum is obtained by using noise information caused by the construction of a shield machine as a seismic source, a stratum velocity model along a tunnel is constructed through joint inversion, and reflection wave information or the like is used for migration imaging, to eventually implement relatively accurate detection of a geological condition in front of a tunnel face of shield construction.

Some systems and methods for determining depths of subterranean formation layer tops while drilling through the subterranean formation include a drill bit, a drill rig, a microphone, a depth sensor, and a processor. While drilling the through the subterranean formation, the processor receives a measured sound from the microphone and a measured drill bit depth from the depth sensor, normalizes the measured sound across all measured drill bit depths, determines frequency information of the normalized sound for each depth of the plurality of depths, determines frequency spectrums of the normalized sound for one or more depths of the plurality of depths, transforms the frequency spectrums into a depth spectrum, and determines the depths of subterranean formation layer tops based on the depth spectrum.

A system includes at least one processing unit and a bottomhole assembly (BHA) that includes or communicates with the at least one processing unit. The BHA includes at least one drilling component and at least one acoustic transducer to convert drilling noise into one or more electrical signals. The at least one processing unit analyzes the one or more electrical signals or related data to categorize different components of the drilling noise as rock contact noise and mechanical noise. The at least one processing unit derives a data log, a plan, or a control signal based on the categorized drilling noise components.

A method can include accessing data generated during field operations; analyzing at least a portion of the data as to legal tag property values; storing the legal tag property values in association with the data; and operating a computational framework in accordance with the legal tag property values.

A rock breaking seismic source and active source three-dimensional seismic combined detection system uses a tunnel boring machine for three-dimensional seismic combined detection by active seismic source and rock breaking seismic source methods. Long-distance advanced prediction and position recognition of a geological anomalous body are realized using the active source seismic method. Machine construction is adjusted and optimized according to the detection result; real-time short-distance accurate prediction of the body is realized using the cutter head rock breaking vibration having weak energy but containing a high proportion of transverse wave components as seismic sources and adopting an unconventional rock breaking seismic source seism recording and handling method. An area surrounding rock quality to be excavated is represented and assessed. A comprehensive judgment is made to the geological condition in front of the working face with the results of active source and rock breaking seismic source three-dimensional seismic advanced detection.

Systems and methods include a computer implemented method for evaluating rock physical properties. Drilling acoustic signals are received in real time during a drilling operation through rock at a drilling location. Transformed data is generated in a frequency domain from the drilling acoustic signals. The transformed data includes frequency and amplitude information for the drilling acoustic signals. De-noised transformed data is generated from the transformed data by filtering noise including background noise generated in a recording system and top drive rotation generated traces. A lithological significant frequency range that includes de-noised significant data points is determined from the de-noised transformed data. Physical properties of the rock are determined in real time using drill bit rotation rates and the amplitudes of the de-noised significant data points.