Apparatus and methods are described, including identifying an arrival of a first arriving S-wave emitted from a seismic source at an array (120) of sensors (129, 140) in real-time, by continuously analyzing waveforms received by the sensors (120, 140), and continuously monitoring back-azimuth and slowness data within the detected waveforms. Arrival of a first arriving P-wave emitted from the seismic source at the array (120) of sensors (129, 140) is identified, based upon the back-azimuth and slowness data. Slowness and back azimuth of the first arriving P-wave are determined, by analyzing a waveform of the P-wave, and based upon the determined slowness of the first arriving P-wave, the arrival of the first arriving S-wave at the array (120) of sensors (129, 140) is identified. Other applications are also described.

A method of characterizing an inflow into a wellbore includes obtaining an acoustic signal from a sensor within the wellbore. In addition, the method includes determining a plurality of frequency domain features from the acoustic signal. Further, the method includes identifying at least one of a gas phase flow, an aqueous phase flow, or a hydrocarbon liquid phase flow using the plurality of the frequency domain features. The method also includes classifying a flow rate of the at least one of the gas phase flow, the aqueous phase flow, or the hydrocarbon liquid phase flow using the plurality of frequency domain features. The acoustic signal comprises acoustic samples across a portion of a depth of the wellbore.

Methods and systems are disclosed for monitoring operation integrity during hydrocarbon production or fluid injection operations. According to the methods and systems, received microseismic data is processed to obtain a plurality of data panels corresponding to microseismic data measured over a predetermined time interval. For each data panel, trigger values are calculated for data traces corresponding to sensor receivers of the microseismic monitoring system. At least one data panel is selected as a triggered data panel that satisfies predetermined triggering criteria. At least one triggered data panel is selected as a non-trivial data panel that satisfies spectral density criteria. A value is calculated for each of at least two event attributes of a plurality of event attributes of the event. An event score is determined based on the values of the plurality of event attributes. An event is classified into at least one event category of a plurality of event categories based on the event score.

The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for determining first-break (FB) points. One computer-implemented method includes: selecting, by a hardware processor, potential first-break (PFB) points based on seismic data obtained by plurality of seismic receivers in a geological location; determining, by the hardware processor, a first plurality of FB lines based on the PFB points; selecting, by the hardware processor, a first FB line among the plurality of FB lines; filtering, by the hardware processor, the PFB points based on the first FB line; determining, by the hardware processor, a second plurality of FB lines based on the filtered PFB points; selecting, by the hardware processor, a second FB line among the second plurality of FB lines; and determining, by the hardware processor, FB points based on the second FB line.

System and methods for generating microseismic velocity models are provided. One or more existing well sites in proximity to a planned well site are selected. Historical microseismic velocity models associated with the selected well sites are obtained. The formation depths for each velocity component of the historical models are correlated to formation depths from well logs acquired for a subsurface formation associated with the planned well site. A classification and non-linear regression on the historical microseismic velocity models is performed to identify the best-fitting velocity components for layers of the subsurface formation corresponding to the correlated formation depths. An initial microseismic velocity model of the formation is generated using the best-fitting velocity components. Seismic wave propagation through each layer of the formation is simulated using the generated model. Locations of one or more microseismic events of interest within the formation are estimated, based on the simulated wave propagation.

A computerized machine (a) determines temporal and spatial statistical characterizations for each one of plural sonic events, (b) classifies certain pairings among the sonic events as comparable, and (c) estimates a minimum number of sonic sources, some of which are in motion, that could have generated the sonic events. Sonic event times and positions can be characterized by corresponding temporal and spatial confidence intervals. A pairing of sonic events is classified as comparable only when that pairing meets one or more preselected constraints, some of which depend on the temporal and spatial statistical characterizations. The estimated minimum number of sonic sources is equal to the number of sonic events in a longest antichain within a chronological ordering of the set of sonic events. An antichain comprises a subset of the sonic events for which no pairing of sonic events of that subset is a comparable pairing.

A rock sample is nano-indented from a surface of the rock sample to a specified depth less than a thickness of the rock sample. While nano-indenting, multiple depths from the surface to the specified depth and multiple loads applied to the sample are measured. From the multiple loads and the multiple depths, a change in load over a specified depth is determined, using which an energy associated with nano-indenting rock sample is determined. From a Scanning Electron Microscope (SEM) image of the nano-indented rock sample, an indentation volume is determined responsive to nano-indenting, and, using the volume, an energy density is determined. It is determined that the energy density associated with the rock sample is substantially equal to energy density of a portion of a subterranean zone in a hydrocarbon reservoir. In response, the physical properties of the rock sample are assigned to the portion of the subterranean zone.

Methods are presented for determining the location of underground features (e.g., CO.sub.2). One method includes capturing, by sensors distributed throughout a region, seismic traces associated with seismic signals generated by a seismic source. For multiple sensors, active noise is identified or passive noise is measured within each seismic trace and values for attributes associated with the active or passive noise are determined. Further, an unsupervised machine-learning model, based on the values of the attributes, is utilized to determine noise characteristics for multiple sensors. The sensors are grouped in clusters based on the noise characteristics for each sensor. For multiple clusters, a noise filter is created based on the noise characteristics of the sensors in the cluster, and the noise filter of the cluster is applied, for multiple sensors, to the seismic traces of the sensor. Additionally, the filtered seismic traces are analyzed to determine a location of CO.sub.2 underground.

System and methods for generating microseismic velocity models are provided. One or more existing well sites in proximity to a planned well site are selected. Historical microseismic velocity models associated with the selected well sites are obtained. The formation depths for each velocity component of the historical models are correlated to formation depths from well logs acquired for a subsurface formation associated with the planned well site. A classification and non-linear regression on the historical microseismic velocity models is performed to identify the best-fitting velocity components for layers of the subsurface formation corresponding to the correlated formation depths. An initial microseismic velocity model of the formation is generated using the best-fitting velocity components. Seismic wave propagation through each layer of the formation is simulated using the generated model. Locations of one or more microseismic events of interest within the formation are estimated, based on the simulated wave propagation.

A method of identifying inflow locations along a wellbore includes obtaining an acoustic signal from a sensor within the wellbore, determining a plurality of frequency domain features from the acoustic signal, and identifying, using a plurality of fluid flow models, a presence of at least one of a gas phase inflow, an aqueous phase inflow, or a hydrocarbon liquid phase inflow at one or more fluid flow locations. The acoustic signal includes acoustic samples across a portion of a depth of the wellbore, and the plurality of frequency domain features are obtained across a plurality of depth intervals within the portion of the depth of the wellbore. Each fluid flow model of the plurality of fluid inflow models uses one or more frequency domain features of the plurality of the frequency domain features, and at least two of the plurality of fluid flow models are different.