In addition to drilling operations a hard rock drill rig is used to induce seismic energy into subsurface rock formations. In quartz rich rock the seismic energy will partially convert to electrical energy thereby allowing for the prospecting of ore-bearing quartz veins. Seismic energy and electric energy are detected by a sensor array. The electric energy is used to infer the presence of piezoelectric materials in the rock formations, and such materials are mapped using the detected seismic energy and electric energy.

System and method for guiding a drill using a subsurface model generated by successive full waveform inversions (FWI) on surface data and seismic-while-drilling (SWD) data. A server receives surface data from at least one surface sensor that records elastic energy radiated from surface seismic source and SWD data from at least one surface or at least one subsurface crosswell sensor (i.e., deployed in a nearby well). The server also receives top-drive measurements. A drillbit source signature estimation is performed, on the SWD data, by the server by blind deconvolution or by using drill string modeling and top-drive measurements. The server then performs FWI on the surface data by using the background subsurface velocity obtained by kinematic analysis of surface seismic data, to obtain an updated approximation of the subsurface velocity. The new approximation along with the drillbit source signature is then used when performing FWI on the SWD data.

Techniques for determining a drill bit location includes identifying a plurality of acoustic energy signals received at a plurality of sets of acoustic receivers from a passive acoustic energy source that is part of a wellbore drilling system; processing the plurality of acoustic energy signals; determining a location of a drill bit of the wellbore drilling system based on the processed plurality of acoustic signals; and updating a geo-steering path of the drill bit based on the determined location of the drill bit.

Apparatus and methods of identifying rock properties in real-time during drilling, are provided. An apparatus includes an acoustic sensor installed in a drilling fluid circulation system of a drilling rig, the acoustic sensor coupled to one of the following: (i) a bell nipple, (ii) a gooseneck, or (iii) a standpipe. Raw acoustic sensor data generated real-time as a result of rotational contact of the drill bit with rock during drilling is received, and a plurality of acoustic characteristics are derived from the raw acoustic sensor data. The lithology type of rock undergoing drilling may be determined from the acoustic characteristics. Petrophysical properties of the rock undergoing drilling may be determined using a petrophysical properties evaluation algorithm employable to predict the petrophysical properties of rock undergoing drilling from the raw acoustic sensor data.

Drilling systems and related methods are disclosed. A drilling systems may include a tool configured to be positioned at an end of a drill string adjacent a drill bit, and the tool may be configured to detect localized seismo-electromagnetic conversion from one or more predetermined positions within a medium ahead of the drill bit. The tool may include two or more pressure sources configured to generate focused acoustic and/or elastic energy at the one or more predetermined positions to generate the localized seismo-electric conversion.

Using microseismic analysis to identify and quantify the hydraulic fracture interaction in the Earth formation. Identification of the interaction is based on the magnitude of the events and therefore independent of the location uncertainty. Quantification of the interaction is location based.

Apparatus (10) and methods for combining time reversal and elastic nonlinearity of formation materials for qualtitatively probing for over-pressured regions down hole in advance of a well drilling bit, to determine the distance to the over-pressured region, and for accurately measuring pore pressure downhole in a formation, are described. Classical and reciprocal time reversal methods may be utilized to achieve these measurements.

Extracting a core sample from within a wellbore is optimized based on information extracted from acoustic signals that are generated downhole. The acoustic signals provide an indication of the formation being cored, which is used to obtain or adjust designated coring operating parameters for accomplishing an efficient and effective coring procedure. Coring operating parameters that are adjusted include weight on bit and bit rotational speed. Optimizing coring operating parameters reduces wear on the bit and produces samples with less fractures. Generating acoustic signals is done by the operation of coring itself, or contacting the formation with a coring bit. Contacting includes impacting the coring bit radially against the wellbore's sidewall, or moving the bit laterally after coring operations have initiated.

Drilling systems and related methods are disclosed. A drilling system may include a drill bit positioned at an end of a drill string, and one or more sources and receivers positioned on the drill string behind the drill bit. The sources may be configured to emit signals into a medium surrounding a borehole in which the drill string extends, and the signals may cause a response in the medium at one or more predetermined positions ahead of the drill bit, and response signals may be measured by the one or more receivers. A processor may use the measured response signals to iteratively update an estimate of a property of the medium at the predetermined position, which in some embodiments, may be done using a sequential estimation process, as the drill string is advanced into the medium.

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.