Disclosed herein is a method of stripping a strong reflection layer based on deep learning. The method establishes a direct mapping relationship between a strong reflection signal and seismic data of a target work area through a nonlinear mapping function of the deep neural network, and strips a strong reflection layer after the strong layer is accurately predicted. A mapping relationship between the seismic data containing the strong reflection layer and an event of the strong reflection layer is directedly found through training parameters. In addition, this method does not require an empirical parameter adjustment, and only needs to prepare a training sample that meets the actual conditions of the target work area according to the described rules.

A method for modelling the formation of a sedimentary area is disclosed, comprising: •—a setup step comprising defining a geological gridded model of the area comprising a plurality of cells, and setting a reference water level, •—a step of simulating the evolution of the model over a period of time, comprising: •a. assigning a water depth to each cell, •b. determining, for each cell, a direction and velocity of a water current, •c. introducing at least one particle in at least one cell of the model, •d. transporting each introduced particle in the model, based on the computed direction and velocity of the water current, comprising displacing the particle to a neighboring cell or depositing the particle in the cell, and the determination whether the particle is displaced or deposited depends on a particle granulometric class and the velocity of the water current applied to the particle, •e. updating the geological gridded model of the area according to the transport of each introduced particle.

A system and method for analyzing geologic features including fluid estimation and lithology discrimination may include the steps of identifying areas of interest on a seismic horizon, computing statistical data ranges for the seismic amplitudes within the areas of interest, and analyzing the geologic features based on the amplitude variation with offset (AVO) or angle (AVA) curves including the statistical data ranges.

A method for variogram modeling is disclosed. The method includes obtaining a synthetic well data and a well data for facies or petrophysical properties of interest in a targeted reservoir zone, training machine learning models using the synthetic well data as inputs and outputting a plurality of final variogram parameters predicted by using a plurality of machine learning models for the facies or petrophysical properties of interest in the targeted reservoir zone, wherein the well data for the facies or petrophysical properties of interest in the targeted reservoir zone is used as input.

Systems and methods for creating a surface in a faulted space, which includes using interpolation techniques.

A method (1200) can include receiving implicit functions values for a mesh that represents a geologic environment that includes intersecting faults defined by fault patches (1210); assigning states to the fault patches (1220); revising the implicit function values based at least in part on the assigned states to provide revised implicit function values (1230); and outputting a structural model of the geologic environment based at least in part on the revised implicit function values (1240).

A system and method for analyzing geologic features including fluid estimation and lithology discrimination may include the steps of identifying areas of interest on a seismic horizon, computing statistical data ranges for the seismic amplitudes within the areas of interest, and analyzing the geologic features based on the amplitude variation with offset (AVO) or angle (AVA) curves including the statistical data ranges.

A computer system receives multiple datapoints of a geomechanical property of a hydrocarbon reservoir modeled by a three-dimensional (3D) grid. Each datapoint corresponds to a respective grid cell of the 3D grid. Each grid cell of the 3D grid is represented by 3D coordinates. For each grid cell of the 3D grid, the computer system generates a data component of the geomechanical property based on the 3D coordinates of the grid cell. The computer system adds the data component to a datapoint corresponding to the grid cell to provide an augmented set of datapoints. The computer system transforms the augmented set of datapoints into a Gaussian distribution using Gaussian approximation. The computer system simulates the geomechanical property of the hydrocarbon reservoir based on the Gaussian distribution using sequential Gaussian simulation. A display device of the computer system generates a graphical representation of the geomechanical property of the hydrocarbon reservoir based on the sequential Gaussian simulation.

A Gaussian process is used to provide a nonparametric approach for modeling nonlinear relationships among physical quantities involved in the geomechanics workflow supporting drilling & completion operations. Gaussian process provides a nonparametric framework that enables injection of a prior belief into the basic model format while allowing its specific format to be adaptive in a certain range following an estimated distribution. Both this model-related uncertainty and the pre-assumed input data distributions may be calibrated using non-parametric Bayesian framework with Gaussian process as prior. This approach not only the addresses the uncertainty stemming from the input physical properties but also tackles the uncertainties underlying the adopted physical model, all in this nonparametric Bayesian framework with Gaussian process encoded as prior.

Provided in this disclosure are systems and methods for selection of petrophysical techniques to model reservoirs. Reservoir properties were calculated using two techniques—a deterministic technique and an optimizing petrophysics technique, and simulation models were developed. The technique that yielded a simulation model that aligned more accurately and consistently with the reservoir data was selected to further develop the reservoir model.