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.

A computer implemented method for analyzing a reservoir grid modeling a reservoir geological formation is provided in which the reservoir grid corresponds to a 3D grid of cells associated to respective values of at least one geological property. The method includes obtaining a 4D seismic image of the reservoir geological formation. A skeleton of the 4D seismic image is calculated, and the skeleton extends between at least one origin and a plurality of extremities. Each point of the skeleton is associated to a value of the at least one geological property of the reservoir grid. Flow time values are calculated for a fluid flowing from the origin to the extremities along the skeleton, based on the at least one geological property values associated to the points of the skeleton. The reservoir grid is calculated based on the flow time values.

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 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.

Techniques for generating a geological model from 3D seismic data and rock property data are disclosed. Rock property data and 3D seismic data are received. Based on the rock property data and the 3D seismic data, an adaptive geological model is generated. The adaptive geological model includes a characteristic geological property. Synthetic seismic data is generated from a first region of interest of the adaptive geological model. The synthetic seismic data is adapted to facilitate a comparison between the first region of interest and a corresponding region of interest of the received 3D seismic data. The characteristic geological property is adjusted until the comparison indicates a result that is within a predetermined threshold region of the corresponding value from the rock properties. A validated geologic model is then generated.

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.

A three-dimensional electrical resistivity tomography method and system belonging to the field of geological geophysical prospecting, the method including the steps of prospecting a region containing a geological anomaly with at least two prospecting modes respectively to acquire two-dimensional resistivity data of a corresponding detection plane; unifying coordinate systems of resistivity data points acquired in all prospecting modes, and extracting data points with the same coordinates; carrying out data fusion on extracted resistivity data at the same position by utilizing a principal component analysis method; and carrying out three-dimensional coordinate conversion on resistivity data acquired after fusion to form a three-dimensional model.

A computer implemented method for analyzing a reservoir grid modeling a reservoir geological formation is provided in which the reservoir grid corresponds to a 3D grid of cells associated to respective values of at least one geological property. The method includes obtaining a 4D seismic image of the reservoir geological formation. A skeleton of the 4D seismic image is calculated, and the skeleton extends between at least one origin and a plurality of extremities. Each point of the skeleton is associated to a value of the at least one geological property of the reservoir grid. Flow time values are calculated for a fluid flowing from the origin to the extremities along the skeleton, based on the at least one geological property values associated to the points of the skeleton. The reservoir grid is calculated based on the flow time values.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to generate generating geo-cellular models with improved lacunae. In one aspect, a method includes receiving a seismic dataset of a surveyed subsurface, and the seismic dataset includes seismic porosities in depth of the surveyed subsurface. Seismic porosities resampled into a three dimensional (3D) geological fine layer model grid. Seismic porosities at well locations are extracted using the 3D geological fine layer model grid. Log porosities and the seismic porosities are upscaled into coarse layers, and the coarse layers are identical for all the well locations. Match factors are determined based on differences between the upscaled log porosities and the downscaled seismic porosities. Co-krig the log porosities are correlated with the 3D geological fine layer model grid using the match factors as a soft constraint to produce a final 3D model.

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.