A method may include obtaining various well logs or various core samples regarding a geological region of interest. The method may further include determining various permeability values, various porosity values, and various dolomite volume fraction values regarding the geological region of interest using the well logs or the core samples. The dolomite volume fraction values may correspond to a percentage of dolomite in a total mineral volume. The method may further include determining, using the porosity values, various permeability thresholds corresponding to various predetermined reservoir qualities. The method may further include generating, using the permeability thresholds, the permeability values, and the dolomite volume fraction values, a reservoir model including various dolomite boundaries defining the predetermined reservoir qualities. The method may further include determining a hydrocarbon trap prediction using the reservoir model.

The disclosure relates to a system, downhole device and method for monitoring a wellbore, in particular in a lateral section, during stimulation, with an equipment enabling retrieval in many conditions. The method includes monitoring the wellbore with a distributed fiber optic sensor to determine one or more characteristics of the stimulation operation using detected backscattered optical signals on the distributed fiber optic sensor. The cable is retrieved by exerting a traction force. The disclosure also relates to a downhole device for receiving a cable and retainers to maintain the cable and having a weakpoint configured to break when subjected to a force along the longitudinal axis greater than a predetermined threshold. The system comprises the cable having the distributed fiber optic sensor, the downhole device and a surface monitoring system for determining the characteristic of the stimulation operation using detected backscattered optical signals on the distributed fiber optic sensor.

A method includes receiving well log data for a plurality of wells. A flag is generated based at least partially on the well log data. The wells are sorted into groups based at least partially on the well log data, the flag, or both. A model is built for each of the wells based at least partially on the well log data, the flag, and the groups.

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 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 method may include obtaining fracture image data regarding a geological region of interest. The method may further include determining various fractures in the fracture image data using a first artificial neural network and a pixel-searching process. The method may further include determining a fracture model using the fractures, a second artificial neural network, and borehole image data. The method may further include determining various fracture permeability values using the fracture model and a third artificial neural network. The method may further include determining various matrix permeability values for the geological region of interest using core sample data. The method may further include generating a coarsened grid model for the geological region of interest using a fourth artificial neural network, the matrix permeability values, and the fracture permeability values.

A method for fracture dynamic hydraulic properties estimation and reservoir simulation may include obtaining a first set of images of a first fracture. The method may include obtaining a first set of fracture detections from the first set of images, generating a plurality of numerical calculations based on the first set of fracture detections, and generating a second model based on the plurality of numerical calculations and the first set of fracture detections. The method may further include obtaining a second set of images of a second fracture of a new reservoir, generating a second set of fracture detections of the second fracture, and generating dynamic hydraulic estimations of the second fracture. The method may also include generating a three-dimensional reservoir simulation and determining a plurality of recovery schemes for the new reservoir.

The present disclosure relates to a method for determination of a real subsoil geological formation. In at least one embodiment, the method includes receiving a model representing the real subsoil, receiving a proportions cube describing geological properties of the model, determining a permeability value for a plurality of locations of the model based on the received proportions cube, determining a gradient of the flow speed based on the permeability value determined for a plurality of locations of the model, and determining a fluvial formation based on the gradient of the flow speed.

Systems and methods for modeling dispersion curves are disclosed. The method includes obtaining an acoustic dataset along a well that accesses a hydrocarbon reservoir. The method further includes determining a set of depth windows along the well and determining a first subset of dispersion curves for a first subset of depth windows using a dispersion model. The method still further includes initializing a second subset of dispersion curves for a second subset of depth windows using a nearest neighbor search of the first subset of dispersion curves. The method still further includes determining slowness-frequency pairs for the second subset of depth windows using the acoustic dataset and updating the second subset of dispersion curves using a recursive scanning method. The method still further includes characterizing rock properties near the well based, at least in part, on the first subset of dispersion curves and the second subset of dispersion curves.

Systems and methods for assessing seismic risk. The system and methods disclose deriving a model that is used to assess seismic risk of operations at a given location. A first location is identified for which at least one training seismic risk value is known from independent sources. A plurality of training input parameters associated with the first location is received. The at least one training seismic risk value is received. A process model is derived that relates the plurality of training input parameters to the at least one training seismic risk value by determining influence values of the training input parameters. A second location is identified for which a seismic risk is to be determined. A plurality of working input parameters associated with the second location is received. The process model is applied to the plurality of working input parameters to determine a seismic risk value at the second location.