Patent classifications
G01V1/30
COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR OBTAINING A SUBSURFACE STACK IMAGE, SUBSURFACE ANGLE GATHERS, AND A SUBSURFACE VELOCITY MODEL, OVER AN ENTIRE SURVEY REGION HAVING HIGH VELOCITY CONTRAST GEO-BODIES
A computer-implemented method and computing system apparatus programmed to perform operations of the computer-implemented method for obtaining a subsurface stack image, subsurface angle gathers, and a subsurface velocity model over an entire survey region having high velocity contrast geo-bodies. Particularly, user inputs, input velocity models, and surface-seismic data are obtained by fixed source and receiver pairs and then used by the computer program product embedded within the computing system apparatus to minimize the number of iterations, required to obtain a final velocity model, a final stack image, and final angle gathers wherein their flatness deviation is equal to, or less than, a user-defined flatness value. Therefore, the attributes developed by said computer-implemented method and system can help solve the imaging problem of sub high velocity contrast geo-bodies like subsalt, or salt overhung deep mini basins.
Fracture Geometry And Orientation Identification With A Single Distributed Acoustic Sensor Fiber
A method for determining microseismic events. The method may include measuring a seismic travel time of a microseismic event with a fiber optic line disposed in a first wellbore, forming a probability density function for the microseismic event based at least in part on the seismic travel time measurement, modifying the probability density function by applying one or more constraints to form a modified probability density function, identifying one or more most probable source locations from the modified probability density function, and forming a microseismic event cloud from the one or more most probable source locations.
Fracture Geometry And Orientation Identification With A Single Distributed Acoustic Sensor Fiber
A method for determining microseismic events. The method may include measuring a seismic travel time of a microseismic event with a fiber optic line disposed in a first wellbore, forming a probability density function for the microseismic event based at least in part on the seismic travel time measurement, modifying the probability density function by applying one or more constraints to form a modified probability density function, identifying one or more most probable source locations from the modified probability density function, and forming a microseismic event cloud from the one or more most probable source locations.
DETERMINING FAULT SURFACES FROM FAULT ATTRIBUTE VOLUMES
Hydrocarbon exploration and extraction can be facilitated by determining fault surfaces from fault attribute volumes. For example, a system described herein can receive a fault attribute volume for faults in a subterranean formation determined using seismic data. The fault attribute volume may include multiple traces with trace locations. The system can determine a set of fault samples for each trace location. Each fault sample can include fault attributes such as a depth value, an amplitude value, and a vertical thickness value. The system can determine additional fault attributes such as a dip value and an azimuth value for each fault sample of each trace location. The system can determine fault surfaces for the faults using the fault samples and fault attributes. The system can then output the fault surfaces for use in a hydrocarbon extraction operation.
Simultaneous seismic refraction and tomography
A data seismic sensing system and method for obtaining seismic refraction data and tomography data. The system may comprise a subsurface sensor array, wherein the subsurface sensor array is a fiber optic cable disposed near a wellbore, a seismic source, wherein the seismic source is a truck-mounted seismic vibrator comprising a base plate, and a surface sensor array, wherein the surface sensor array is coupled to the seismic source. The method may comprise disposing a surface sensor array on a surface, disposing a subsurface sensor array into a wellbore, activating a seismic source, wherein the seismic source is configured to create a seismic wave, recording a reflected seismic wave with the surface sensor array and the subsurface sensor array, and creating a seismic refraction data and a seismic tomography data from the reflected seismic wave.
METHODS TO ESTIMATE FORMATION SHEAR WAVE SLOWNESS FROM MULTI-FIRINGS OF DIFFERENT TYPES OF ACOUSTIC SOURCES AND MULTI-MODE DISPERSION ESTIMATION SYSTEMS
Methods to estimate formation shear wave slowness from multi-firings of different types of acoustic sources and multi-mode dispersion estimation systems are presented. The method includes obtaining waveform data of waves traversing through a downhole formation, where the waves are generated from multi-firings of different types of acoustic sources. The method also includes performing a multimode dispersion analysis of the waveform data for each firing of the multi-firings, and removing one or more tool waves generated from the multi-firings. The method further includes determining a formation type of the formation the waves traverse based properties of the waves and determining an initial shear wave slowness estimate of the waves. The method further includes generating a modeling of the waves, and reducing a mismatch between the modeling of the waves and a slowness dispersion of the waves to improve the modeling of the waves.
Subsurface fluid-type likelihood using explainable machine learning
A system is described for determining a likelihood of a type of fluid in a subterranean reservoir. The system may include a processor and a non-transitory computer-readable medium that includes instructions executable by the processor to cause the processor to perform various operations. The processor may receive pre-stack seismic data having seismically-acquired data elements for geometric locations in a subterranean reservoir. The processor may determine, using the pre-stack seismic data, input features for each geometric location and may execute a trained model on the input features for determining a likelihood of a type of fluid in the subterranean reservoir and for determining a list of features affecting the likelihood. The processor may subsequently output the likelihood and the list of features.
Formation evaluation based on seismic horizon mapping with multi-scale optimization
A least one seismic attribute is determined for each voxel of the seismic volume. A first horizon is selected for mapping and a sparse global grid is generated which includes the horizon, at least one constraint point identifying the horizon, and a number of points having a depth in the seismic volume. A value of at least one seismic attribute is determined for each point and their depths are adjusted based on the value of the seismic attribute. A map of the horizon can be generated based on the adjusted depths. Multiple local grids can be generated based on the sparse global grid, and the depths of the local grid points adjusted to generate a map of the horizon at voxel level resolution. The seismic volume can be mapped into multiple horizons, where previously mapped horizons can function as constraints on the sparse global grid.
Repeating a previous marine seismic survey with a subsequent survey that employs a different number of sources
Methods and apparatus are described for performing a 4D monitor marine seismic survey that repeats a previous survey. A number of sources may be used during the 4D monitor survey that differs from a number of sources that were used during the previous survey. Shot points from the previous survey are repeated by the 4D monitor survey, and additional shot points may be produced during the 4D monitor survey that were not produced during the previous survey. Embodiments enable efficiency and data quality improvements to be captured during 4D survey processes, while preserving repeatability.
STRUCTURED REPRESENTATIONS OF SUBSURFACE FEATURES FOR HYDROCARBON SYSTEM AND GEOLOGICAL REASONING
A method and apparatus for utilizing a structured representation of a subsurface region. A method includes obtaining subsurface data for the subsurface region; and extracting the structured representation from the seismic data by: identifying geologic and fluid objects in the seismic images, wherein each object corresponds to a node of the structured representation; and identifying relationships among the identified geologic and fluid objects, wherein each relationship corresponds to an edge of the structured representation. A method further includes determining object attributes, edge attributes, and/or global attributes from the subsurface data. A method further includes inferring information from the structured representation.