Implementations described and claimed herein provide systems and methods for reservoir modeling. In one implementation, an input dataset comprising seismic data is received for a particular subsurface reservoir. Based on the input dataset and utilizing a deep learning computing technique, a plurality of trained reservoir models may be generated based on training data and/or validation information to model the particular subsurface reservoir. From the plurality of trained reservoir models, an optimized reservoir model may be selected based on a comparison of each of the plurality of reservoir models to a dataset of measured subsurface characteristics.

A system, computer-readable medium, and method for determining a potential drilling location, of which the method includes obtaining data representing a subterranean domain. The data includes at least seismic data. The method also includes inverting the seismic data, creating a petroleum systems model of the subterranean domain based at least in part on a result of inverting the seismic data, simulating a dynamic reservoir model of the subterranean domain based at least in part on the petroleum systems model, and identifying the potential drilling location based on a combination of the inverting of the seismic data, creating the petroleum systems model, and simulating the dynamic reservoir model.

A device for monitoring and identifying a mountain torrent and debris flow and a method for early warning of disasters relate to the technical field of debris flow protection. The device includes a computation device, sensors, an amplifier and an analog-to-digital converter. The sensors convert an acquired impact force signal into a digital signal by the amplifier and the analog-to-digital converter, and transmits the digital signal to the computation device. The computation device utilizes the digital signal to compute an energy coefficient of a liquid impact signal and a solid-liquid impact energy ratio, and a debris flow mode is monitored and identified in combination with a threshold range of the energy coefficient and a threshold range of the solid-liquid impact energy ratio. The device identifies the nature of the mountain torrent and debris flow through time-frequency analysis of an impact force signal generated by the debris flow to sensors.

Provided are a device, method, and computer-readable recording medium for detecting an earthquake in a microelectromechanical system (MEMS)-based auxiliary seismic observation network. The method includes performing detrending of removing a moving average from original acceleration data received from single sensors of an MEMS-based auxiliary seismic observation network to preprocess the acceleration data, calculating a short-term average/long-term average (STA/LTA) value using a filter parameter value specified on the basis of the preprocessed acceleration data, generating an event occurrence message or event end message on the basis of the calculated STA/LTA value and transmitting the event occurrence message or event end message, when the event occurrence message is generated, calculating an earthquake probability through an earthquake detection deep learning model using the preprocessed acceleration data as an input, and analyzing noise by calculating a power spectral density (PSD) from the original acceleration data which is merged at certain intervals.

The present invention belongs to the field of treatment for data identification and recording carriers, and specifically relates to a method and system for evaluating the filling characteristics of a deep paleokarst reservoir through well-to-seismic integration, which aims to solve the problems that by adopting the existing petroleum exploration technology, the reservoir with fast lateral change cannot be predicted, and the development characteristics of a carbonate cave type reservoir in a large-scale complex basin cannot be identified. The method comprises: acquiring data of standardized logging curves; obtaining a high-precision 3D seismic amplitude data body by mixed-phase wavelet estimation and maximum posteriori deconvolution and enhancing diffusion filtering. According to the method and the system, the effect of identifying the development characteristics of the carbonate karst cave type reservoir in the large-scale complex basin can be achieved, and the characterization precision is improved.

An inversion method for a multilayer seabed geoacoustic parameter in a shallow sea, includes: establishing a plurality of seabed models, different seabed models corresponding to different layer numbers, randomly generating a value of each geoacoustic parameter based on a preset change range corresponding to each geoacoustic parameter, then calculating to obtain a theoretical sound pressure value, and comparing the theoretical sound pressure value with an actual sound pressure value, adjusting and updating the value of each geoacoustic parameter according to the comparison result until the obtained theoretical sound pressure value is matched with the actual sound pressure value, and obtaining a target geoacoustic parameter value; calculating to obtain a BIC value corresponding to each seabed model; and taking the seabed model with the minimum BIC value as a target seabed model, and taking a target geoacoustic parameter value corresponding to the target seabed model as a target inversion parameter value.

A method includes receiving over a network from one or more seismic sensors a data set characterizing a seismic event generating a seismic wave. Based on the data set, a time of arrival and intensity of the seismic wave at a predetermined location is calculated. The predetermined location has one or more mitigation devices. Whether the intensity of the seismic wave exceeds a predetermined seismic intensity threshold is determined. If the intensity of the seismic wave exceeds the predetermined seismic intensity threshold, the one or more mitigation devices are activated.

One or more first sensors may be configured to sense seismic signals and one or more second sensors may be configured to sense electromagnetic crosstalk signals. The second sensors are not responsive to the seismic signals. The data from the first and second sensors may be recorded as first data and second data, respectively. The first data may be modified based on the second data to remove the electromagnetic crosstalk noise form the seismic data.

A distributed acoustic sensing (DAS) system for determining an acoustic event may include an interferometer and an acoustic event detection processing device. The interferometer may measure DAS data from sensed signals from a sensing fiber deployed in a wellbore. The acoustic event detection processing device may determine an acoustic event in the wellbore from an out-of-band signal using the DAS data by performing operations. The operations can include determining a first acoustic event and a second acoustic event from the DAS data. The operations can include determining a first set of aliased frequencies from the first acoustic event and a second set of aliased frequencies form the second acoustic event. The operations can include determining, using an intersection of the first set of aliased frequencies and the second set of aliased frequencies, a frequency or amplitude of out-of-band signals that are usable to determine the at least one acoustic event.

A method includes receiving over a network from one or more seismic sensors a data set characterizing a seismic event generating a seismic wave. Based on the data set, a time of arrival and intensity of the seismic wave at a predetermined location is calculated. The predetermined location has one or more mitigation devices. Whether the intensity of the seismic wave exceeds a predetermined seismic intensity threshold is determined. If the intensity of the seismic wave exceeds the predetermined seismic intensity threshold, the one or more mitigation devices are activated.