The present disclosure provides a design method for a distributed hydrological cycle model based on a multi-source complementary water supply mode, the method including the following steps: S1, nested hydrological response unit (HRU) division; S2, HRU attribute design; S3, design of a multi-source complementary water supply module; and S4, improvement on a SWAT model. Based on the Soil and Water Assessment Tool (SWAT) model, the present disclosure develops a distributed natural-artificial hydrological dynamic reciprocal simulation model. The model is endowed with the functions of simulating dynamic reciprocation of natural water cycle and artificial water cycle, and integration of development, utilization and regulation of water resources, thereby simulating a natural-artificial hydrological cycle based on modes of urban multi-source water supply and multi-source irrigation water supply.

A self-adapting digital twin of a wellbore environment can be created. The self-adapting digital twin incorporates a standalone Ensemble Kalman Filter (EnKF) module with a constant parameter digital twin developed for a fixed environment. The standalone EnKF module receives streaming measurement data from multiple sensors and prediction data from the digital twin and executes the standalone EnKF module using the streaming measurement data and the prediction data from the digital twin. The results of executing the standalone EnKF module are input parameter corrections for the digital twin that are communicated to the digital twin. Output predictions of the digital twin are used to modify operational parameters of an oil or gas recovery process.

A system includes a processor and a memory. The memory includes instructions that are executable by the processor to cause the processor to receive basin data of a target basin including an area of the target basin, a number of exploration wells in the target basin, and a number of discovery wells in the target basin. Additionally, the instructions are executable to cause the processor to provide the basin data as input to a trained machine-learning model to determine a predicted trajectory of exploration efficiency of the target basin. Further, the instructions are executable to cause the processor to, in response to providing the basin data as input to the trained machine-learning model, receive an output from the trained machine-learning model indicating the predicted trajectory of exploration efficiency in the target basin.

A method may obtain static reservoir data for a grid model. The method may further include determining, using the static reservoir data, dynamic reservoir data for the grid model. The method may further include determining various storage capacities and various flow capacities for various model layers within the grid model using the static reservoir data. The method may further include determining various upscaling groups among the model layers based on the flow capacities and the storage capacities. The method may further include generating up scaled static data using the upscaling groups, the static reservoir data, and the grid model. The method may further include generating upscaled dynamic data using the upscaling groups, the dynamic reservoir data, and the grid model. The method may further include performing a reservoir simulation using a coarsened grid model including the upscaled static data and the upscaled dynamic data.

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.

Disclosed is a magnetotelluric inversion method based on a fully convolutional neural network. The magnetotelluric inversion method includes: constructing a multi-dimensional geoelectric model; constructing a fully convolutional neural network structure model to obtain initialized fully convolutional neural network model parameters; training and testing the fully convolutional neural network structure model based on the training sets and the test sets to obtain optimized fully convolutional neural network structure model parameters; determining whether training of the fully convolutional neural network structure model is completed according to fitting error changes corresponding to the training sets and the test sets; and finally, inputting measured apparent resistivity into a trained fully convolutional neural network structure model for inversion, and further optimizing the fully convolutional neural network structure model by analyzing precision of an inversion result until an inversion fitting error satisfies a set error requirement.

A method may include obtaining a real-time petrophysical data derived from a plurality of well logs during drilling and utilizing the real-time petrophysical data to quantify a formation damage profile using a resistivity tornado chart and a wellbore modeling. The method further includes utilizing the resistivity tornado chart to determine a depth of invasion inside a formation at each depth in a wellbore by using ratios between different resistivity logs obtained while drilling and creating a synthetic wellbore model by using a fluid flow equation for the wellbore modeling and calculating a time-specific invasion profile to determine a condition at a flowback time. The method further includes performing a computational fluid dynamics investigation in order to identify invaded fluid flow characteristics from the formation to the wellbore and calculating a duration needed to flowback an obtained invaded volume for removal of the formation damage based on a fluid flow behavior.

Systems and methods include a method for generating a high-resolution advanced three-dimensional (3D) transient model that models multiple wells by integrating pressure transient data into a static geological model. A crude 3D model is generated from a full-field geological model that models production for multiple wells in an area. A functional 3D model is generated from the crude 3D model. An intermediate 3D model is generated by calibrating the functional 3D model with single-well data. An advanced 3D transient model is generated by calibrating multi-well data in the functional 3D model.

A full probability-based seismic risk analysis method for a tunnel under fault dislocation comprises: evaluating a magnitude-frequency relationship of a fault; obtaining a probabilistic seismic risk curve of a fault dislocation; calculating a series of bending moments of a tunnel lining under different fault dislocations; obtaining a series of damage index values R.sub.M of the tunnel; obtaining a vulnerability model of the tunnel damaged by fault dislocation; calculating a probabilistic risk that the tunnel crossing the fault is damaged due to the dislocation of the active fault; obtaining a probability P that the damage state is equal to or higher than a certain damage state within a specified period; and using the results to guide the assessment of the seismic risk of the tunnel crossing the fault. Modeling and analysis can be performed according to the actual situation of the tunnel crossing the fault with different factors.