Geologic modeling methods and systems disclosed herein employ an improved simulation gridding technique. For example, an illustrative geologic modeling method may comprise: obtaining a geologic model representing a faulted subsurface region in physical space; mapping the physical space geologic model to a design space model representing an unfaulted subsurface region; gridding the design space model to obtain a design space mesh; partitioning cells in the design space mesh with faults mapped from the physical space geologic model, thereby obtaining a partitioned design space mesh; mapping the partitioned design space mesh to the physical space to obtain a physical space simulation mesh; and outputting the physical space simulation mesh.

A method for training a backpropagation-enabled regression process is used for predicting values of an attribute of subsurface data. A multi-dimensional seismic data set with an input dimension of at least two is inputted into a backpropagation-enabled process. A predicted value of the attribute has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension.

A system can receive seismic data that can correlate to a subterranean formation. The system can derive a set of seismic attributes from the seismic data. The seismic attributes can include discontinuity-along-dip. The system can determine parameterized results by analyzing the seismic data and the seismic attributes using a deep learning neural network. The deep learning neural network can include a dilation module. The system can determine one or more fault probabilities of the subterranean formation using the parameterized results. The system can output the fault probabilities for use in a hydrocarbon exploration operation.

Methods, computing systems, and computer-readable media for interpreting seismic data, of which the method includes receiving seismic data representing a subterranean volume, and determining a feature-likelihood attribute of at least a portion of a section of the seismic data. The feature-likelihood attribute comprises a value for elements of the section, the value being based on a likelihood that the element represents part of a subterranean feature. The method also includes identifying contours of the subterranean feature based in part on the feature-likelihood attribute of the section, and determining a polygonal line that approximates the subterranean feature.

A process for drilling a well into a subsurface formation includes receiving data representing depth maps for a given subsurface region, each depth map being generated from seismic data acquired in a seismic survey at a subsurface region. The process includes determining, for depth maps of the plurality, respective weight values; generating data representing a combination of the depth maps based on the respective weight values; generating a cumulative distribution function (CDF) for a particular location in the subsurface region based on the data representing a combination of the depth maps; determining, based on the CDF for that particular location, a probability value representing a depth at which a geological layer occurs in the subsurface region at the particular location; and drilling the well into the subsurface formation at the particular location to a target depth based on the probability value.

Geologic modeling methods and systems disclosed herein employ an improved simulation meshing technique. One or more illustrative geologic modeling methods may comprise: obtaining a geologic model representing a faulted subsurface region in physical space; providing a set of background cells that encompass one or more partial faults within the subsurface region; defining a pseudo-extension from each unterminated edge of said one or more partial faults to a boundary of a corresponding background cell in said set; using the pseudo-extensions and the background cell boundaries to partition the subsurface region into sub-regions; deriving a simulation mesh in each sub-region based on the horizons in each sub-region; and outputting the simulation mesh.

A rock-piercing flexible rock drilling robot and a rock breaking method therefor are disclosed. The robot includes a control system, a head, and at least one tail. The head includes a head housing, a propulsion turntable, a drilling mechanism, a hydraulic propulsion system, a first driving mechanism, and a second driving mechanism. The propulsion turntable includes a drill bit located at a center thereof and a cutting turntable arranged around the drill bit. The first driving mechanism is connected to the drill bit, and the second driving mechanism is connected to the cutting turntable. The tail includes a tail housing, an advancing and retreating power system, and a fixed support system. The head and the tail are connected through a flexible component, and the tails are connected through flexible components.

A process for drilling a well into a subsurface formation includes receiving data representing depth maps for a given subsurface region, each depth map being generated from seismic data acquired in a seismic survey at a subsurface region. The process includes determining, for depth maps of the plurality, respective weight values; generating data representing a combination of the depth maps based on the respective weight values; generating a cumulative distribution function (CDF) for a particular location in the subsurface region based on the data representing a combination of the depth maps; determining, based on the CDF for that particular location, a probability value representing a depth at which a geological layer occurs in the subsurface region at the particular location; and drilling the well into the subsurface formation at the particular location to a target depth based on the probability value.

A method for fracture dynamic hydraulic properties estimation and reservoir simulation may include obtaining a first set of images of a first fracture. The method may include obtaining a first set of fracture detections from the first set of images, generating a plurality of numerical calculations based on the first set of fracture detections, and generating a second model based on the plurality of numerical calculations and the first set of fracture detections. The method may further include obtaining a second set of images of a second fracture of a new reservoir, generating a second set of fracture detections of the second fracture, and generating dynamic hydraulic estimations of the second fracture. The method may also include generating a three-dimensional reservoir simulation and determining a plurality of recovery schemes for the new reservoir.

A full probability-based seismic risk analysis method for a tunnel under fault dislocation comprises: evaluating a magnitude-frequency relationship of a fault; obtaining a probabilistic seismic risk curve of a fault dislocation; calculating a series of bending moments of a tunnel lining under different fault dislocations; obtaining a series of damage index values R.sub.M of the tunnel; obtaining a vulnerability model of the tunnel damaged by fault dislocation; calculating a probabilistic risk that the tunnel crossing the fault is damaged due to the dislocation of the active fault; obtaining a probability P that the damage state is equal to or higher than a certain damage state within a specified period; and using the results to guide the assessment of the seismic risk of the tunnel crossing the fault. Modeling and analysis can be performed according to the actual situation of the tunnel crossing the fault with different factors.