Implementations described and claimed herein provide systems and methods for reservoir modeling. In one implementation, an input dataset comprising seismic data is received for a particular subsurface reservoir. Based on the input dataset and utilizing a deep learning computing technique, a plurality of trained reservoir models may be generated based on training data and/or validation information to model the particular subsurface reservoir. From the plurality of trained reservoir models, an optimized reservoir model may be selected based on a comparison of each of the plurality of reservoir models to a dataset of measured subsurface characteristics.

Systems and methods for picking seismic stacking velocity based on structures in a subterranean formation include: receiving seismic data representing a subterranean formation; generating semblance spectrums from the seismic data representing the subterranean formation; smoothing the semblance spectrums; and picking stacking velocities based on the smoothed semblance spectrums.

A graphical representation of an image of a subterranean formation along with log properties of the formation provided in a single graphical representation. Logged formation property values are coded into graphic representations of images of the formation in order to provide a graphical representation which allows the user to visually perceive the formation images and the logged formation properties simultaneously. A method may include receiving an image of a formation, the image including image values based on the formation, and also receiving a log property of the formation, the log property including log property values based on the formation. The log property values of the formation are correlated to corresponding locations in the image. A transfer function with the image values and the correlated log property values as inputs is determined. Based on the transfer function, a joint graphical representation of the image and the log property is rendered.

Systems and methods to estimate a pore pressure of source rock include a pore pressure estimation processor, an executable, or both, and are operable to (i) calculate an estimate pore pressure based on overburden gradient data, a compaction velocity profile, hydrocarbon maturity, and an unloading velocity profile, (ii) determine a total organic content (TOC) estimate of the source rock based on a bulk density at a vertical depth measured using the density logging tool, (iii) determine a correction factor based on (a) the TOC estimate and (b) vitrinite ratio R.sub.o data, and (iv) update the estimated pore pressure in real-time based on the correction factor.

Systems, methods, and computer readable media for the determination of hydrogen sulfide (H.sub.2S) concentration and distribution in carbonate reservoirs using a mechanical earth model. Hydrogen sulfide (H2S) concentration in a carbonate reservoir n may be measured and correlated with horizontal maximum stresses of stress ratios determined using mechanical earth model for a strike-slip fault regime. The hydrogen sulfide (H2S) concentration at different depths in the carbonate reservoir may be determined using the correlation.

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.

A system, computer-readable medium, and method for determining a potential drilling location, of which the method includes obtaining data representing a subterranean domain. The data includes at least seismic data. The method also includes inverting the seismic data, creating a petroleum systems model of the subterranean domain based at least in part on a result of inverting the seismic data, simulating a dynamic reservoir model of the subterranean domain based at least in part on the petroleum systems model, and identifying the potential drilling location based on a combination of the inverting of the seismic data, creating the petroleum systems model, and simulating the dynamic reservoir model.

A method is presented wherein inversion for formation anisotropic constants is achieved using borehole sonic data.

In a first step, a defined scope value is selected for each of a plurality of hydrodynamic input parameters. A simulated topographical result is generated using the selected scope values and a forward model. A detailed seismic interpretation is generated to represent specific seismic features or observed topography. A calculated a misfit value representing a distance between the simulated topographical result and a detailed seismic interpretation is minimized. An estimated optimized sand ratio and optimized hydrodynamic input parameters are generated. In a second step, a genetic algorithm is used to determine a proportion of each grain size in the estimated optimized sand ratio. A misfit value is used that is calculated from thickness and porosity data extracted from well data and a simulation result generated by the forward model to generate optimized components of different grain sizes. Optimized hydrodynamic input parameters and optimized components of different grain sizes are generated.