A method and system are provided for acquiring the probability of slope failure and destabilization caused by an earthquake. For example, the method includes performing azimuth division in an area around a site at which a slope is located as a center, pre-setting a seismic acceleration threshold value that varies within a certain range, and calculating an exceeding probability that the seismic acceleration of the slope site generated by an earthquake in each azimuth domain is greater than or equal to the seismic acceleration threshold value, to establish an exceeding probability curve of site seismic acceleration corresponding to each azimuth domain. The method and system achieve estimation of the probability of slope destabilization caused by an earthquake by comprehensively considering the uncertainty of the seismic action and the uncertainty of slope failure and destabilization.

The invention provides a method of detecting a seismic event, which comprises acquiring (110) a digital signal x characteristic of a signal measured by at least one seismic sensor, and calculating (130) a time-frequency distribution for at least one section of a given duration of said signal, in a given frequency band. For each frequency of said frequency band, the calculated time-frequency distribution is normalized. The method also comprises calculating (150) the moving average of the normalized time-frequency distribution ZD, in said frequency band and in a time window, given reference L, centered on the time n; and detecting (160) a seismic event when the average exceeds a predefined threshold value. The invention also provides a corresponding detection system.

A method for performing a borehole and/or subsurface formation-related action for a subsurface formation of interest includes: receiving a plurality of sets of fracture data for a subsurface rock; generating a discrete fracture network (DFN) for each set of fracture data; and determining a property of each DFN that corresponds to each set of fracture data. The method also includes: mapping the plurality of sets of fracture data to the corresponding property using artificial intelligence (AI) to provide an AI model; inputting a set of fracture data for the subsurface formation of interest into the AI model; outputting a property of the subsurface formation of interest from the AI model; and performing the borehole and/or subsurface formation-related action for the subsurface formation of interest using the property and equipment configured to perform the borehole and/or subsurface formation-related action.

A method of mapping micro-fractures of a fracture network comprising: introducing a capsule or coated capsule or containment into the fracture network, where in the capsules or containment comprise an explosive substance and a plurality of micro-proppant; allowing initiation of the explosive substance of some or all of the plurality of the capsules to occur, wherein initiation of the explosive substance causes detonation of the explosive substance, and wherein the detonation produces one or more micro-seismic events; and causing or allowing at least a portion of the micro-proppant to enter one or more of the micro-fractures.

A method of characterizing a subsurface of a region includes preparing a plurality of spectra illustrating a spectral density of passive seismic signals obtained in a vicinity of a surface of the region at one or more points of the region where recordings are made of the passive seismic signals. Each spectrum is prepared from an associated signal representative of a movement. The method also includes determining at least one spectral attribute for each frequency appearing in each spectrum so as to obtain a set of spectral attributes associated with the recordings and with the frequencies. The method further includes organizing the set of spectral attributes in a matrix in which each row is associated with one of the recordings. In addition, the method includes applying a principal component analysis method to the matrix in order to determine principal components and deduce therefrom one or more characteristics of the subsurface.

A multivariate analysis may be used to correlate seismic attributes for a subterranean formation with petrophysical properties of the subterranean formation and/or microseismic data associated with treating, creating, and/or extending a fracture network of the subterranean formation. For example, a method may involve modeling petrophysical properties of a subterranean formation, microseismic data associated with treating a complex fracture network in the subterranean formation, or a combination thereof with a mathematical model based on measured data, microseismic data, completion and treatment data, or a combination thereof to produce a petrophysical property map, a microseismic data map, or a combination thereof; and correlating a seismic attribute map with the petrophysical property map, the microseismic data map, or the combination thereof using the mathematical model to produce at least one quantified correlation, wherein the seismic attribute map is a seismic attributed modeled for the complex fracture network.

Examples of techniques for generating a high-resolution lithology model for subsurface formation evaluation are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method includes determining, by a processing device, a low-resolution lithology volumetric model. The method further includes comparing, by the processing device, the low-resolution lithology volumetric model to a high-resolution imaging log. The method further includes calculating, by the processing device, a dynamic boundary curve for each of a plurality of moving windows. The method further includes generating, by the processing device, the high-resolution lithology model based at least in part on the calculated dynamic boundary curve for each of the plurality of moving windows. The method further includes controlling a drilling operation based at least in part on the high-resolution lithology model.

A method can include measuring microseismic activity in a relief wellbore, thereby detecting a microseismic event in an earth formation penetrated by the relief wellbore, and determining a location of an influx into a target wellbore, based on the microseismic event detecting. A microseismic ranging system for use with a subterranean well can include at least one microseismic sensor in a relief wellbore that penetrates an earth formation, with the microseismic sensor detecting a microseismic event in the formation, the microseismic event being caused by an influx into a target wellbore. Another method can include measuring optical scattering in an optical waveguide positioned in a relief wellbore, thereby detecting a microseismic event in an earth formation penetrated by the relief wellbore, and determining a location of an influx into a target wellbore, based on the microseismic event detecting.

A method can include receiving locations of microseismic events associated with a fracturing operation performed in a geologic environment; determining an individual correlation exponent for one of the microseismic events based at least in part on distances where each of the distances is between the location of the one microseismic event and a location of another one of the microseismic events; and, based at least in part on the individual correlation exponent, associating the one of the microseismic events with a fracture generated or activated by the fracturing operation.

A method including selecting a forward model based on a modeled well structure and including a single modeled acoustic source located in a modeled wellbore and a plurality of modeled acoustic sensors located in a modeled source area, simulating an acoustic signal generated by the single modeled acoustic source and received by each modeled acoustic sensor, calculating phases of the simulated acoustic signals received at each modeled acoustic sensor, obtaining with a principle of reciprocity a plurality of modeled acoustic sources in the modeled source area and a single modeled acoustic sensor in the modeled wellbore, calculating phase delays of the simulated acoustic signals between each modeled acoustic source and the single modeled acoustic sensor, detecting acoustic signals generated by a flow of fluid using acoustic sensors in a wellbore, and processing the acoustic signals using the phase delays to generate a flow likelihood map.