A method is herein presented to statistically combine multiple seismic attributes for generating a map of the spatial density of fractures. According to an embodiment a first step involves interpreting the formation of interest in 3D seismic volume first to create its time structure map. The second step is creating depth structure of the formation of interest from its time structure map. In this application geostatistical methods have been used for depth conversional, although other methods could be used instead. The third step is extraction of a number of attributes, such as phase, frequency and amplitudes, from the time structure map. The next step is to project the fracture density onto the top of the target formation. The final step is to combine these attributes using a statistical method known as Multi-variant non-linear regression to predict fracture density.

In certain embodiments, a method includes generating a set of average velocity controls based on received seismic data, generating a depth to basement model based on received potential fields data, and generating an average velocity model using an interpolation model to interpolate the set of average velocity controls and the depth to basement model. The method may also include generating a variogram model based on the set of average velocity controls, and generating the average velocity model using the interpolation model to interpolate the average velocity controls, the variogram model, and the depth to basement model. The interpolation model may be Kriging with external drift. The external drift may be based on the depth to basement model. Additionally, the method includes generating a structural map of a subsurface formation using the average velocity model.

A method including obtaining, by a computer processor, at least one key log in each of a set of training wells located, at least partially, within a hydrocarbon reservoir, identifying a target formation bounding surface in each of the set of training wells, and generating an initial depth surface for the target formation bounding surface from the target formation bounding surface in each of the set of training wells. The method further including, determining from the initial depth surface an initial depth estimate of the target formation bounding surface at a location of a current well, forming an objective function based, at least in part on a correlation between each key log in each of the set of training wells, and each corresponding key log in the current well, and optimizing the objective function by varying a depth shift between each of the set of training wells and the current well, to determine an optimum depth shift that produces an extremum of the objective function. The method still further including combining the initial depth estimate of the target formation bounding surface at the location of the current well with the optimum depth shift to produce a final depth estimate of the target formation bounding surface at the location of the current well.

The present disclosure relates to systems, methods, and non-transitory computer-readable media for dynamically utilizing offset drill-well data generated within a threshold geographic area to determine formation-top trends and identify formation-top depths at a subject drill-well site. To do so, in some embodiments, the disclosed systems estimate a variogram for observed formation-top depths of a subset of offset drill-wells, and, in turn, map a predicted response from the estimated variogram. For example, using weighted combinations (e.g., with Kriging weights) of the formation-top depths of the subset of offset drill-wells, the disclosed systems can map a continuous surface of a formation and identify a top-depth thereof. Moreover, the disclosed system can do so for multiple formations at the subject drill-well site, and (in real-time in response to a user input) provide for display at a client device, the associated formation-top depths, various predicted drilling events and/or predicted drilling metrics.