A method, including: obtaining, with a computer, an initial geophysical model; modeling, with a computer, a forward wavefield based on the initial geophysical model with wave equations including a second order z-derivative in a rotated coordinate system that accounts for a tilted transverse isotropic (TTI) medium; modeling, with a computer, an adjoint wavefield with adjoint wave equations including a second order z-derivative in a rotated coordinate system that accounts for a tilted transverse isotropic (TTI) medium, wherein the wave equations and the adjoint wave equations include relaxation terms accounting for anelasticity of earth in an update of a primary variable and an evolution relationship for the relaxation terms; and obtaining, with a computer, a gradient of a cost function based on a combination of a model of the forward wavefield and a model of the adjoint wavefield.

A method may include obtaining core image data regarding a geological region of interest. The method may further include obtaining well log data regarding the geological region of interest from one or more wells. The method may further include determining a sliding window that corresponds to a predetermined window size. The method may further include determining various quantitative image attributes using the core image data, the well log data, and the sliding window. The quantitative image attributes may be determined in a continuous manner by moving the sliding window along the core image data. The method may further include generating predicted rock data for the geological region of interest using the quantitative image attributes, a machine-learning algorithm, and a machine-learning model.

Systems, methods, and apparatus including computer-readable mediums for forecasting hydrocarbon reservoir properties such as well log responses and petrophysical parameters using artificial intelligence are provided. In one aspect, a method of forecasting well logs of a target well includes obtaining well data of the target well including depth and geological information and reservoir parameters and estimating jointly multiple well logs of the target well by utilizing an artificial intelligence (AI) network with the well data of the target well. The AI network is trained based on well data of existing wells that includes multiple reservoir parameters of the existing wells jointly as inputs and multiple well logs of the existing wells jointly as outputs. The estimated multiple well logs of the target well are reconciled with each other, with the well logs of the existing wells, and with geographic formation associated with the target well and the existing wells.

Provided is a method for determining convergence in full wavefield inversion (FWI) of 4D seismic (time-lapse seismic: 3D seismic surveys acquired at different times with the first survey termed as the baseline and subsequent surveys termed as monitors). FWI applied to field seismic data includes iteratively solving for subsurface property models and model difference between monitor and baseline. Iteration occurs until the model difference is sufficiently converged. Rather than determining convergence by examining an entire subsurface region of the models and/or the model difference, subparts of the subsurface region models and/or the model difference are examined in order to determine convergence. For example, different regions behave differently, include the target reservoir region (where hydrocarbon is present) and the background region that is outside the target reservoir region. Thus, transforming the subregions of the models and/or the model difference and analyzing the transformations may indicate convergence of the overall model difference.

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.

A method is described for seismic imaging including generating extended image gathers by extended reverse time migration of a seismic dataset using an earth model; processing the extended image gathers to generate processed image gathers; performing extended modeling based on the processed image gathers to generate a modeled seismic dataset; enhancing the processed image gathers to generate an enhanced image; performing extended modeling based on the enhanced image gathers to generate a modeled enhanced dataset; differencing the modeled enhanced dataset and the modeled seismic dataset to determine a data residual; inverting the data residual to generate a model residual; updating the earth model based on the model residual to create an updated earth model; performing seismic imaging of the seismic dataset using the updated earth model to create an improved seismic image. The method may be executed by a computer system.

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.

A method may include obtaining an initial velocity model regarding a subterranean formation of interest. The method may further include generating various seismic migration gathers with different cross-correlation lag values based on a migration-velocity analysis and the initial velocity model. The method may further include selecting a predetermined cross-correlation lag value automatically using the seismic migration gathers and based on a predetermined criterion. The method may further include determining various velocity boundaries within the initial velocity model using a trained model, wherein the trained model is trained by human-picked boundary data and augmented boundary data. The method may further include updating, by the computer processor, the initial velocity model using the velocity boundaries, the automatically-selected cross-correlation lag value, and the migration-velocity analysis to produce an updated velocity model. The method may further include generating an image of the subterranean formation of interest using the updated velocity model.

A method to estimate a depth profile of a weathering layer in a subterranean formation of a field is disclosed. The method includes obtaining gravity survey data of the field, generating an equivalent source density profile based on the gravity survey data, wherein the equivalent source density profile describes a set of equivalent gravitational sources to substitute rock layers of the subterranean formation, generating an equivalent source gravity response based on the equivalent source density profile, wherein the equivalent source gravity response excludes a gravity contribution from the weathering layer, calculating a separated weathering layer gravity response based on a difference between the gravity survey data and the equivalent source gravity response, wherein the separated weathering layer gravity response corresponds to the gravity contribution from the weathering layer, and generating a modeled weathering layer depth profile based on the separated weathering layer gravity response.