The invention describes a method for monitoring vibrations produced by an operating area, comprising the following steps: (El): dividing an operating frequency range into a plurality of frequency sub-ranges; (E2): for each frequency sub-range, defining an associated vibration threshold; (E3): continuously acquiring vibration measurements produced by the operating area; (E4): periodically transmitting vibration data resulting from the vibration measurements, the vibration data being transmitted to a remote server (200) via an LPWAN network; (E5) detecting a vibration event corresponding to at least one vibration threshold being exceeded in the associated frequency sub-range; (E6): when a vibration event is detected, transmitting a warning to the remote server (200) via the LPWAN network.

The present invention relates to a target-oriented seismic acquisition method and apparatus, a medium and a device. The target-oriented seismic acquisition method comprises the steps of: giving parameters of an initial velocity model and a three-dimensional seismic layout aiming to an underground target position; conducting wave field continuation and focusing analysis on the three-dimensional seismic layout, and calculating distribution of seismic energy on the ground in an underground target region; conducting normalization processing on distribution of the seismic energy on the ground, and then conducting level partitioning to obtain a primary energy region and a secondary energy region; adding the number of shot points in the primary energy region to achieve target-oriented acquisition, and obtaining a target-oriented inhomogeneous laying acquired data imaging result. By using the method of the present invention, automatic feedback adjustment on excitation and receiving sites and parameters thereof is achieved.

Acoustic characterization and mapping of flow from a perforation zone of a well. As a wireline probe containing acoustic sensors moves through the well, the acoustic sensors record acoustic pressure measurements of flow for each perforation in the well casing. The acoustic data is recorded and compiled into a three-dimensional flow model showing flow of hydrocarbons within and/or out of perforation tunnels. The three-dimensional flow models generated can be combined with historical data to form four-dimensional models illustrating flow over time, and both the three and four-dimensional models can be used to determine effectiveness of perforation charges as well as future flow from the well.

Methods and systems for identifying near-surface heterogeneities in a subterranean formation using surface seismic arrays can include: recording raw seismic data using sensors at ground surface; applying a band bass filter to the raw seismic data using a central frequency; picking a phase arrival time for the filtered data; generating an initial starting phase velocity model for tomographic inversion from the raw seismic data; applying tomographic inversion to the filtered data to generate a dispersion map associated at the central frequency; repeating the applying a band bass filter, picking a phase arrival time, generating an initial starting velocity model, and applying tomographic inversion steps for each of a set of central frequencies; and generating a three-dimensional dispersion volume representing near-surface conditions in the subterranean formation by combining the dispersion maps.

Aspects of the present disclosure relate to earth modeling using machine learning. A method includes receiving detected data at a first depth point along a wellbore, providing at least a first subset of the detected data as first input values to a machine learning model, and receiving first output values from the machine learning model based on the first input values. The method includes receiving additional detected data at a second depth point along the wellbore, providing at least a second subset of the additional detected data as second input values to the machine learning model, and receiving second output values from the machine learning model based on the second input values. The method includes combining the first output values at the first depth point and the second output values at the second depth point to generate an updated model of the wellbore, the updated model comprising an earth model.

Systems and methods are disclosed for hydrocarbon exploration using seismic imaging and, more specifically, measuring randomness in seismic data utilizing a vectorized disorder algorithm. The vectorized disorder algorithm is configured to measure the randomness level (e.g., noise) in seismic data to improve seismic data processing/imaging and the ability to expose subsurface geology. The vectorized disorder algorithm includes performing convolution of seismic data with a vectorized disorder operator having an extra dimension than the seismic data. A nonlinear reduction operation is performed on the vectorized output to generate a randomness distribution dataset having the same dimension as the input data. The randomness distribution dataset comprises data points representing the level of randomness for respective seismic data points. A more accurate seismic image is generated from the seismic data as a function of the measured randomness distribution.

An actuation location for actuation of a first source coupled to a first marine survey vessel relative to a position of a second marine survey vessel towing a receiver to enhance illumination of a subsurface location can be determined based on a survey route of the second marine survey vessel and a priori data of the subsurface location. The first marine survey vessel can be navigated along a survey route of the first marine survey vessel to the actuation location during a marine survey by changing at least a cross-line position or an in-line position of the first marine survey vessel relative to the survey route of the second marine survey vessel.

Property values inside an explored underground subsurface are determined using hybrid analytic and machine learning. A training dataset representing survey data acquired over the explored underground structure is used to obtain labels via an analytic inversion. A deep neural network model generated using the training dataset and the labels is used to predict property values corresponding to the survey data using the DNN model.

A method for verticalizing recorded seismic data, the method including recording first data with a particle motion sensor, wherein the particle motion sensor is located on a streamer, and the particle motion sensor is configured to be insensitive to a direct current, recording second data with a gravity motion sensor, wherein the gravity motion sensor is also located on the stream, and the gravity motion sensor is configured to be sensitive to the direct current and temporally synchronous to the particle motion sensor, selecting a cost function that associates corresponding values of the first data and the second data, determining a misalignment angle from maximizing the cost function, wherein the misalignment angle describes a misalignment between corresponding axes of the particle motion sensor and the gravity motion sensor, and correcting seismic data recorded by the particle motion sensor based on the misalignment angle so that the corrected seismic data is verticalized with regard to gravity.

A method for verticalizing recorded seismic data, the method including recording first data with a particle motion sensor, wherein the particle motion sensor is located on a streamer, and the particle motion sensor is configured to be insensitive to a direct current, recording second data with a gravity motion sensor, wherein the gravity motion sensor is also located on the stream, and the gravity motion sensor is configured to be sensitive to the direct current and temporally synchronous to the particle motion sensor, selecting a cost function that associates corresponding values of the first data and the second data, determining a misalignment angle from maximizing the cost function, wherein the misalignment angle describes a misalignment between corresponding axes of the particle motion sensor and the gravity motion sensor, and correcting seismic data recorded by the particle motion sensor based on the misalignment angle so that the corrected seismic data is verticalized with regard to gravity.