A flexible, rapid deployable perimeter monitoring system and method that employs distributed fiber optic sensing (DFOS) technologies and includes a deployment/operations field vehicle including an interrogator and analyzer/processor. The deployment/operations field vehicle is configured to field deploy a ruggedized fiber optic sensor cable in an arrangement that meets a specific application need, and subsequently interrogate/sense via DFOS any environmental conditions affecting the deployed fiber optic sensor cable. Such sensed conditions include mechanical vibration, acoustic, and temperature that may be advantageously sensed/evaluated/analyzed in the deployment/operations vehicle and subsequently communicated to a central location for further evaluation and/or coordination with other monitoring systems. Upon completion, the field vehicle and DFOS reconfigure a current location or redeployed to another location.

A method, apparatus, and program product utilize a disentangled factor learning framework to analyze petro-technical image data such as seismic image data to infer properties of a subsurface volume and/or to generate image data for use in training machine learning algorithms for use in petro-technical applications.

In an example implementation, first seismic energy is generated using first seismic sources positioned on an earth's surface. First data including measurements of the first seismic energy is obtained from first geophones positioned at a first depth below the earth's surface. Second data including measurements of the first seismic energy is obtained from second geophones positioned on the earth's surface. Second seismic energy is generated using second seismic sources positioned on an earth's surface and proximal to the second geophones. Third data including measurements of the second seismic energy is obtained from third geophones positioned at the first depth below the earth's surface. A propagation of the first seismic energy along a first path is estimated based on the first, second and third data. One or more characteristics of the target are determined based on the estimate.

A method for characterizing a hydraulic fracture in a subsurface formation includes inducing a pressure change in a borehole drilled through the subsurface formation. At least one of pressure and a time derivative of pressure is measured in the borehole for a selected length of time. At least one physical parameter of at least one fracture is determined using the measured pressure and/or the time derivative of pressure. A method for characterizing hydraulic fracturing rate uses microseismic event count measured through the borehole and its real-time implementation.

Example seismic sensors and methods relating thereto are disclosed. In an embodiment, the seismic sensor includes an outer housing and a proof mass disposed in the inner cavity of the outer housing. In addition, the seismic sensor includes a first biasing member positioned in the inner cavity between the proof mass and an outer housing upper end that is configured to flex in response to axial movement of the outer housing relative to the proof mass. Further, the seismic sensor includes a second biasing member positioned in the inner cavity between the first biasing member and the outer housing upper end. Still further, the seismic sensor includes a sensor element positioned in the inner cavity between the proof mass and an outer housing lower end that is configured to generate a potential in response to movement of the outer housing relative to the proof mass.

A method may include obtaining seismic data regarding a geological region of interest. The seismic data may include various pre-processed gathers. The method may further include obtaining a machine-learning model that is pre-trained to predict migrated seismic data. The method may further include selecting various training gathers based on a portion of the pre-processed gathers, a migration function, and a velocity model. The method may further include generating a trained model using the training gathers, the machine-learning model, and a machine-learning algorithm. The method may further include generating a seismic image of the geological region of interest using the trained model and a remaining portion of the seismic data.

A method for estimating a thickness of a deep reservoir may include obtaining seismic data relating to the deep reservoir. The method may include performing spectral decomposition to obtain one or more frequency components from the seismic data. The method may include identifying a number of mono-frequency horizons corresponding to high frequencies in the seismic data, determining whether the deep reservoir is a thin reservoir based on the number of mono-frequency horizons, and estimating the thickness of the deep reservoir when the deep reservoir is determined to be the thin reservoir.

Methods and systems for seismic data acquisition in a survey area use compressed sensing and take into consideration operational limitations. The operational limitations may be related to the equipment used for the survey, the topography of the surveyed area or limitations that otherwise optimize the survey path.

A method for building a three dimensional (3D) model of a subsurface formation includes selecting, from a set of seismic shots, a plurality of first arrival signals representing the seismic shots. The method includes applying a quality control function to the plurality of first arrival signals to obtain a set of remaining first arrival signals. For each remaining first arrival signals, the method includes applying a velocity inversion function to obtain a depth velocity value at a common-midpoint (CMP) location in a shot gather including the seismic shot associated with that remaining first arrival signal, the CMP location representing a lateral variation of the shot gather including that seismic shot. The method includes, based on the depth velocity value for the seismic shot associated with each remaining first arrival signal, generating a velocity model representing the 3D model of the subsurface formation.

A method of seismic exploration above a region of the subsurface containing structural or stratigraphic features conducive to the presence, migration, or accumulation of hydrocarbons comprises accessing at least a portion of a blended seismic source survey, separating the at least two interfering seismic source excitations using inversion separation, producing one or more source gathers based on the separating, and using the one or more source gathers to explore for hydrocarbons within said region of the subsurface. The blended source seismic survey contains at least two interfering seismic source excitations therein, and the seismic source excitations can be produced by seismic source types having different signatures or frequency characteristics.