A computer implemented method for analyzing a reservoir grid modeling a reservoir geological formation is provided in which the reservoir grid corresponds to a 3D grid of cells associated to respective values of at least one geological property. The method includes obtaining a 4D seismic image of the reservoir geological formation. A skeleton of the 4D seismic image is calculated, and the skeleton extends between at least one origin and a plurality of extremities. Each point of the skeleton is associated to a value of the at least one geological property of the reservoir grid. Flow time values are calculated for a fluid flowing from the origin to the extremities along the skeleton, based on the at least one geological property values associated to the points of the skeleton. The reservoir grid is calculated based on the flow time values.

The present disclosure concerns a computer implemented method for correcting a reservoir model comprising a stratigraphic grid modeling a reservoir geological formation. The method includes obtaining a 3D image representing values of a physical property obtained from seismic measurements performed on the reservoir geological formation. A skeleton is calculated for the values of the physical property of the 3D image. Each point of the skeleton is associated to a respective cell of the stratigraphic grid. One reference layer is determined for at least one set of points of the skeleton. For each point of the at least one set, a layer gap is calculated between the reference layer and the cell associated to said point, and the reservoir model is corrected based on the layer gaps.

A system and method for seismic monitoring of large area subsurface reservoirs, for instance, the system comprising: multiple electro acoustic technology assemblies comprising seismic sensing elements, electronic circuits for converting the seismic sensing signals to frequencies, amplification circuitry to amplify the frequencies, an acoustic source that converts the amplified frequencies to an acoustic frequency signal; a fiber optic acoustic sensing system comprising a fiber optic cable deployed in a subsurface reservoir, where the multiple electro acoustic technology assemblies are proximate to and/or acoustic coupled with the fiber optic cable of the fiber optic acoustic sensing system, and a surface based distributed acoustic sensing interrogator connected to the fiber optic cable.

A fracture mapping system for use in hydraulic fracturing operations utilizing non-directionally sensitive fiber optic cable, based on distributed acoustic sensing, deployed in an observation well to detect microseismic events and to determine microseismic event locations in 3D space during the hydraulic fracturing operation. The system may include a weighted probability density function to improve the resolution of the microseismic event on the fiber optic cable.

A computer-implemented method includes receiving a test seismic dataset associated with a known truth interpretation, receiving one or more hard constraints, training a machine learning system based on the test seismic dataset, the known truth interpretation, and the one or more hard constraints, determining an error value based on the training the machine learning system, adjusting the error value based on one or more soft constraints, updating the training of the machine learning system based on the adjusted error value, receiving a second seismic dataset after the updating the training; applying the second seismic dataset to the machine learning system to generate an interpretation of the second seismic dataset, generating a seismic image representing a subterranean domain based on the interpretation of the second seismic dataset, and outputting the seismic image.

A method includes receiving, via a processor, a first seismic dataset generated using a first type of survey system. The method further includes receiving, via the processor, a second seismic dataset generated using a second type of survey system. The method additionally includes determining a frequency band in which to combine the first seismic dataset with the second seismic dataset to generate a combined dataset and generating a seismic image based upon the combined dataset, wherein the seismic image represents hydrocarbons in a subsurface region of the Earth or subsurface drilling hazards.

Systems and methods are disclosed for generating subsurface data as a function of position and time. Exemplary implementations may include obtaining a first initial subsurface model and a first set of subsurface parameters, obtaining training subsurface property data and a first training subsurface dataset, generating a first conditioned subsurface model, and storing the first conditioned subsurface model.

The present invention relates to an apparatus and method for detecting an earthquake using an accelerometer. More particularly, the present invention relates to an apparatus and method for detecting an earthquake using an accelerometer, the apparatus and method being capable of improving reliability of acceleration data obtained from the accelerometer and reliably determining whether an earthquake has occurred on the basis of a change between current acceleration data and previous acceleration data.

There is disclosed a system and method for analyzing geological features of a reservoir, such as a subterranean hydrocarbon reservoir undergoing changes during different stages of its production, by utilizing an artificial neural network to learn from hydrocarbon reservoir production project. In an aspect, there is provide a system and method for utilizing data collected from 4D seismic studies in order to train an artificial neural network to recognize how physical properties of a hydrocarbon reservoir change over time, as the hydrocarbon reservoir is produced. In an embodiment, the system and method are adapted to generate and obtain a plurality of image slices or image planes derived from a 3D seismic baseline and at least one monitor acquired over the course production of the hydrocarbon reservoir. Corresponding 2D image slices derived from the 3D seismic baseline and a subsequent monitor are correlated and matched and are then used to train an artificial neural network to create a predictive model of how the reservoir may change over time.

Disclosed is a method and associated computer program and apparatus for characterising changes within a subsurface volume between a first time and a second time. The method comprises obtaining first seismic data corresponding to the first time and processing this data to obtain a seismic image of the subsurface volume. This processing is reversed for relevant portions of the seismic image to obtain relevant portions of first seismic data. Changes within the subsurface volume between the first time and the second time are characterised by estimating the changes between second seismic data corresponding to the second time and the relevant portions of first seismic data.