A method can include providing data for at least one shale gas formation; performing a statistical analysis on the data for each of the at least one shale gas formation; providing a simulation model; history matching the simulation model for each of the at least one shale gas formation based at least in part on the performed statistical analysis to generate a history matched model for each of the at least one shale gas formation; and forecasting production for another shale gas formation by plugging in data for the other shale gas formation into each generated history matched model. Various other apparatuses, systems, methods, etc., are also disclosed.

Examples of methods and systems are disclosed. The methods may include, obtaining a seismic dataset regarding a subsurface region of interest and obtaining a well log for each of a plurality of wellbores penetrating the subsurface region of interest. The methods may also include determining a geological surface from the seismic dataset, wherein the geological surface includes seismic-estimated orientation data estimated at a plurality of points on the geological surface. The methods may further include determining an intersection point for each of the plurality of wellbores with the geological surface, wherein the intersection point includes well-measured orientation data. The methods may still further include generating an updated geological surface by updating the seismic-estimated orientation data at the plurality of points on the geological surface based, at least in part, on the well-measured orientation data.

A method, including: performing, with a computer, within-seismic-attribute clustering for each of a plurality of seismic attribute datasets for N different attributes, N being greater than or equal to two; identifying an anchor attribute and N1 subordinate attributes from the N different attributes; linking, with a computer, objects within the seismic attribute data sets corresponding to the N1 subordinate attributes to related objects within the seismic attribute data set corresponding to the anchor attribute; and identifying, with a computer, cross-attribute clusters, wherein the objects of any subordinate attribute that are linked to a same object of the anchor attribute are part of a single cross-attribute cluster.

A technique includes determining at least one attribute of a slowness vector associated with a seismic gather based on pressure data and an indication of particle motion that is measured by at least one seismic sensor while in tow.

A method including: generating an updated subsurface property model of a subsurface region, with a computer, from an initial estimate of the subsurface property model by performing an iterative inversion, which includes inverting geophysical data to infer the updated subsurface property model, wherein the generating the updated subsurface property model includes linearly remapping the initial estimate of the subsurface property model based on an inverse of a regularization operator, included with the initial estimate of the subsurface property model, into one in which the regularization operator is represented by an identity matrix, performing a unitary matrix decomposition in order to group the identity matrix with sparse matrices output from the unitary matrix decomposition, and performing a search over at least one trade-off parameter to reduce a misfit between simulated data generated from a most recent estimate of the subsurface property model and the geophysical data until a predetermined stopping criteria is satisfied; and generating, with a computer, an image of the subsurface region using the updated subsurface property model.

Defining flow control device configurable positions include executing a reservoir simulator on a reservoir model to obtain a collection of flow control device settings defined in continuous space. For each number of multiple numbers of clusters, a cluster analysis is individually performed on the collection to obtain a set of flow control device configurable positions. The set includes the number of flow control device configurable positions and its corresponding inflow area or diameter. Performing the cluster analysis across the numbers generates multiple sets for the multiple numbers of clusters. The sets of flow control device configurable positions are compared to obtain a selected set of flow control device configurable positions, which is presented in a completion design.

Storing and retrieving subsurface rock physical property models for well drilling by generating estimated subsurface rock physical property models based on a respective set of seismic data, determining a stratigraphic classification for each model based on seismic interpretation data; storing the models associated classification in a database, determining characteristics of the seismic data used to generate the models, selecting from the database a subset of the models based on a similarity of the stratigraphic classification, determining measures of similarity between the characteristics of the seismic data used to generate each of subset of the models and a new set of seismic data and retrieving an estimated subsurface rock physical property model from the subset models based on the similarity measures.

The methods may include obtaining a seismic dataset regarding a subsurface region of interest and obtaining a well log for each of multiple wellbores penetrating the subsurface region of interest. The methods may also include determining a geological surface from the seismic dataset, wherein the geological surface includes seismic-estimated orientation data estimated at multiple points on the geological surface. The methods may further include determining an intersection point for each of the multiple wellbores with the geological surface, wherein the intersection point includes well-measured orientation data. The methods may still further include generating an updated geological surface by updating the seismic-estimated orientation data at the multiple points on the geological surface based, at least in part, on the well-measured orientation data.

Processes can be employed to select cartographic reference system (CRS) recommendations from a CRS model where the CRS recommendations are matched to received seismic data. A learning mode can be used to build the CRS model where seismic data is matched to CRS. The learning mode can be automated using natural language processing system to parse the meta data for the seismic data, such as the name, area, or code, or label. The CRS model can be updated using an output from a user system, such as when a user manually matches a CRS to seismic data. The matched seismic data to CRS can be used as input to a user system or a computing system, such as a borehole operation system.

A method for a physics-driven deep learning-based inversion coupled to fluid flow simulators may include obtaining measured data for a subsurface region, obtaining prior subsurface data for the subsurface region, and obtaining a physics-driven standard regularized joint inversion for at least two model parameters. The method may further include obtaining a case-based deep learning inversion characterized by a contracting path and an expansive path. The method may further include forming the physics-driven deep learning inversion with the physics-driven standard regularized joint inversion, the case-based deep learning inversion, and a coupling operator based on a penalty function. The method may further include forming a feedback loop between the physics-driven standard regularized joint inversion and the case-based deep learning inversion for re-training the case-based deep learning inversion. The method may further include generating an inversion solution for reservoir monitoring.