Accordingly, there are disclosed herein geologic modeling methods and systems employing reference-based inversion of seismic image volumes. An illustrative method embodiment includes: (a) obtaining a measured seismic image volume; (b) determining a reference seismic image volume based on a reference model; (c) deriving a synthesized seismic image volume from a geologic model; (d) detecting at least one geologic model region where the synthesized seismic image volume and the measured seismic image volume are mismatched; (e) finding a reference model region where the reference seismic image volume best matches the measured seismic image volume; (f) replacing content of the at least one geologic model region with content of the reference model region to obtain an improved geologic model; and (g) outputting the improved geologic model.

Systems, methods, and computer program products can be used for determining the amount of oil removed by a miscible gas flood. One of the methods includes identifying locations of oil within a volume representing a reservoir rock sample. The method includes identifying locations of gas within the volume. The method also includes determining the amount of oil removed based on locations within the volume where oil is either coincident with the gas or is connected to the gas by a continuous oil path.

A system for analysis of a porous formation is disclosed. Such a system can provide electrical signals for one or more of the porous formation or a representation of the porous formation; can determine, using the electrical signals, permittivity and conductivity measures for the porous formation or the representation of the porous formation; and can model the permittivity and conductivity measures to generate a first estimation model associated with pore features for the porous formation, so that a downhole rock formation can be evaluated for pore connectivity, permeability, and Archie's texture parameters using estimation models.

Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving historical production data associated with a hydrocarbon well; preprocessing the historical production data to remove noise from the historical production data; using one or more machine-learning algorithms and the preprocessed historical production to train a simulation model to simulate a flow-after-flow test for the hydrocarbon well; and testing the simulation model to determine that the simulation model passes predetermined testing criteria.

A method may include obtaining a request to determine automatically a depth of a formation top for a well in a geological region of interest. The method may include obtaining various well logs regarding the well and various wells in the geological region of interest. The method may include determining various depth values using the various well logs and a statistical interpolation method. The method may further include determining a final depth of the well using the various depth values and a searching method.

A method includes, in a computer, generating a discretized model of the subsurface formation in space and time. The discretized model comprises at least one physical parameter of the formation and a relationship between the physical parameter and the physical property. For each spatial location and at each time in the discretized model, a time independent solution to the relationship is calculated. A context is defined of a selected number of grid cells surrounding each spatial location. Dimensionality reduction is performed on each context. Each dimensionality reduced context is input into the computer as a separate earth model to train a machine learning system to determine a relationship between the dimensionality reduced context and the physical property. The trained machine learning system is used to estimate the physical property at each spatial location and at each time.

A lithology prediction that uses a geological age model as an input to a machine learning model. The geological age model is capable of separating and recoding different seismic packages derived from the horizon interpretation. Once the machine learning model has been trained, a validation may be performed to determine the quality of the machine learning model. The quality may be improved by refining the training of the machine learning model. The lithology prediction generated by the machine learning model that utilizes the geological age model provides an improved lithology prediction that more accurately reflects the subterranean formation of an area of interest.

Methods and apparatus for evaluating an earth formation for generating a numerical model comprising an expression equating a representation for the effective conductivity under Archie's law with respect to direct current with a function for the effective conductivity representative of the mixing model with respect to direct current; and solving a system of equations to obtain values for the Archie parameters including at least i) the expression; ii) a second expression equating a first order variation of the representation with a first order variation of the function with respect to water saturation of the formation (Sw); and iii) a third expression equating a first order variation of the representation with a first order variation of the function with respect to porosity of the formation (φ). The second and third expressions may equate derivatives of the representation with derivatives of the function.

Methods and systems for augmented geological service characterization are described. An embodiment of a method includes generating a geological service characterization process in response to one or more geological service objectives and a geological service experience information set. Such a method may also include augmenting the geological service characterization process by machine learning in response to a training information set. Additionally, the method may include generating an augmented geological service characterization process in response to the determination information.

A method for generating one or more reservoir models using machine learning is provided. Generating reservoir models is typically a time-intensive idiosyncratic process. However, machine learning may be used to generate one or more reservoir models that characterize the subsurface. The machine learning may use geological data, geological concepts, reservoir stratigraphic configurations, and one or more input geological models in order to generate the one or more reservoir models. As one example, a generative adversarial network (GAN) may be used as the machine learning methodology. The GAN includes two neural networks, including a generative network (which generates candidate reservoir models) and a discriminative network (which evaluates the candidate reservoir models), contest with each other in order to generate the reservoir models.