Disclosed is a method of quantitatively evaluating structural disturbance characteristics of present in-situ geo-stress in deep shale gas reservoirs, including: measuring geomechanics key parameters of key wells in different tectonic zones within a study area; performing interpretations of single-well profile rock mechanics and continuity of the in-situ geo-stress in magnitude and direction; establishing a geological model; performing anisotropic sequential Gaussian stochastic simulation to obtain three-dimensional (3D) heterogeneous rock mechanics parameter field distribution; performing prediction of distribution of geo-stress states in the study area, and calculating a stress structural index and stress disturbance factor of the target layer and a rotation degree of a maximum horizontal principal stress; and performing quantitative evaluation on an in-situ geo-stress structural disturbance and mapping.

Methods and systems are disclosed for updating a seismic velocity model of a subterranean region of interest. The method includes receiving an observed seismic dataset and a seismic velocity model, and generating a simulated seismic dataset based on the seismic velocity model and the geometry of the observed seismic dataset, wherein each dataset is composed of a plurality of seismic traces. The method further includes determining a transformed observed seismic dataset and a transformed simulated seismic dataset by determining the instantaneous frequency of at least one member of the plurality of observed seismic traces; and at least one member of the plurality of simulated seismic traces. The method still further includes forming an objective function based on the transformed observed seismic dataset and the transformed simulated seismic dataset and determining an updated seismic velocity model based on an extremum of the objective function.

A method includes receiving a model representing a real subsoil geological formation. The model includes a stratigraphic layer, which includes a shore line dividing the stratigraphic layer into a continental zone and a marine zone. First and second flow speed fields are received, with the first flow speed field representative of a continental domain for the stratigraphic layer, and the second flow speed field representative of a marine domain for the stratigraphic layer. A global flow speed field is determined and includes a weighted combination of the first and second flow speed fields for each position in the stratigraphic layer. Weights of the combination are based on a distance of the position to the shore line and whether the position is within the continental zone or the marine zone. The real subsoil geological formation for the stratigraphic layer is determined based on the determined global flow speed field.

A computer system and method for determining subterranean rock composition is described in which user input data is received having a plurality of parameters defining a desired subterranean rock composition from a wellbore. Data associated with at least one geologic environment is received, which data contains data acquired from at least one wellbore. An analytical analysis is then conducted by a computer processor utilizing the user input data and the received geologic environment data to determine a match between the user desired subterranean rock composition and the received geologic environment data. Output graphic data is then determined and generated, based at least in part on the analytical analysis, on a computer graphical display consisting of a two-dimensional (2D) graphical representation indicating a region of the geologic environment having a match between the user desired subterranean rock composition and the received geologic environment data.

A method and apparatus for subsurface data processing includes determining a set of clusters based at least in part on measurement vectors associated with different depths or times in subsurface data, defining clusters in a subsurface data by classes associated with a state mode, reducing a quantity of the subsurface data based at least in part on the classes, and storing the reduced quantity of the subsurface data and classes with the state model in a training database for a machine learning process.

A method is claimed that includes obtaining a measured present-day value of at least one parameter for each member of a set of unvalidated geological layers arranged in order of increasing depth and iteratively selecting a member of the set as a current layer. For each current layer in turn, the method further determines an estimated archaic value of at least one parameter of the current layer based on its measured present-day value by applying an alternating cycle of decompaction followed by geomechnical modeling to predict a present-day value of the parameter of the current layer based on its estimated archaic value. The method still further determines a validated archaic value of at least one parameter of each current layer based on a difference between the predicted and the measured present-day values. A non-transitory computer readable medium storing instructions for validating the archaic value for each layer is claimed.

A method of estimating a property associated with a subterranean region includes acquiring a synthetic physical model of the subterranean region, the physical model made from at least a mineral material and constructed using an additive manufacturing process, the physical model having a microstructure, the microstructure having a parameter that varies along at least a first axis of the physical model. The method also includes performing a measurement of the physical model under an applied condition, and estimating the property of the subterranean region based on the measurement.

A method and apparatus for generating a fluid saturation model for a subsurface region. One example method generally includes obtaining a model of the subsurface region; for each of a plurality of fluid types: flooding the subsurface region model with the fluid type to generate a flood model; and running a trial petrophysical inversion with the flood model to generate a trial petrophysical model; identifying potential fluid contact regions in the trial petrophysical models; partitioning the subsurface region model at the identified potential fluid contact regions; and constructing the fluid saturation model from the partitioned subsurface region model.

The present invention belongs to the field of treatment for data identification and recording carriers, and specifically relates to a method and system for analyzing filling for a karst reservoir based on spectrum decomposition and machine learning, 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.

The present disclosure relates to the field of geophysical processing methods for oil and gas exploration, and more particularly, to a method for gas detection using multiple quantum neural networks. A plurality of stratigraphic and structural seismic attributes are extracted from the seismic data of a target horizon, and input seismic characteristic parameters are divided into different classes by using an unsupervised learning and supervised learning combined quantum self-organizing feature map network. Gas detection is then performed using a particle swarm optimization based quantum gate node neural network with clustering results of various seismic characteristic parameters output by the quantum self-organizing feature map network as inputs. The present method uses the unsupervised learning and supervised learning combined quantum self-organizing feature map network for a plurality of stratigraphic and structural seismic attributes of the seismic data and thus has improved accuracy and uniqueness of clustering.