A method for improved prediction and enhancement of hydrocarbon recovery from ultra-tight/unconventional reservoirs for both the primary production and any subsequent solvent huff'n'puff periods based on facilitating the diffusion process may include steps of defining one or more initial properties of a reservoir and integrating characterization data of the reservoir; defining a wellbore trajectory for each of at least one well and one or more parameters associated with a completion/reservoir stimulation design; specifying operating conditions for a current development cycle; performing diffusion-based dynamic fracture/reservoir simulation for calculating hydrocarbon recovery and efficiency of a hydrocarbon process; and; determining whether to commence or continue enhanced oil recovery (EOR) or enhanced gas recovery (EGR) cycles.

A method for history matching a reservoir model based on actual production data from the reservoir over time generates an ensemble of reservoir models using geological data representing petrophysical properties of a subterranean reservoir. Production data corresponding to a particular time instance is acquired from the subterranean reservoir. Normal score transformation is performed on the ensemble and on the acquired production data to transform respective original distributions into normal distributions. The generated ensemble is updated based on the transformed acquired production data using an ensemble Kalman filter (EnKF). The updated generated ensemble and the transformed acquired production data are transformed back to respective original distributions. Future reservoir behavior is predicted based on the updated ensemble.

The selection of a candidate formation realization(s) from a plurality of formation realizations may be done with a classification and regression tree (CART) analysis taking into account petrophysical and geological properties. For example, a method may include applying a CART analysis to a plurality of formation realizations using a first formation property as a predictor in the CART analysis, wherein the plurality of formation realizations are for a second formation property and are based on at least one measured formation property, thereby yielding an association between the first and second properties for each of the plurality of formation realizations; analyzing a strength of the association for each of the plurality of formation realizations; and selecting a candidate formation realization from the plurality of formation realizations based on the strength of the association. The identified candidate formation realization(s) may then be used to develop the parameters of subsequent wellbore operations.

Methods for predicting hydrocarbon production rates for a hydrocarbon reservoir include receiving data from a hydrocarbon reservoir. The data includes reservoir characterization data, well log data, and hydraulic fracturing data. A physics-constrained machine learning model predicts a hydrocarbon production rate for the hydrocarbon reservoir as a function of time. The physics-constrained machine learning model includes an artificial neural network and a hydrocarbon fluid flow model. Predicting the hydrocarbon production rate includes generating, by the artificial neural network, multiple parameters of the hydrocarbon fluid flow model based on the data from the hydrocarbon reservoir. The hydrocarbon fluid flow model provides the predicted hydrocarbon production rate as a function of time based on the parameters. A display device of the computer system presents the predicted hydrocarbon production rate for the hydrocarbon reservoir as a function of time.

A method for determining a favorable time window of an infill well of an unconventional oil and gas reservoir, which comprises the following steps: S1, establishing a three-dimensional geological model with physical properties and geomechanical parameters; S2, establishing a natural fracture network model in combination with indoor core-logging-seismic monitoring; S3, calculating complex fractures in hydraulic fracturing of parent wells; S4, establishing an unconventional oil and gas reservoir model and calculating a current pore pressure field; S5, establishing a dynamic geomechanical model and calculating a dynamic geostress field; S6, calculating complex fractures in horizontal fractures of the infill well in different production times of the parent wells based on pre-stage complex fractures and the current geostress field; S7, analyzing a microseismic event barrier region and its dynamic changes in infill well fracturing; and S8, analyzing the productivity in different infill times, and determining an infill time window.

Methods for modeling hydraulic fractures using a geomechanical model of a formation and a completion design that includes a plurality of hydraulic fractures. A quantity of equivalent hydraulic fractures is selected to represent the plurality of hydraulic fractures. An equivalence ratio is determined using the quantity of equivalent hydraulic fractures and a fracture regime based on the geomechanical model and the completion design. The equivalence ratio is then used to update the geomechanical model and the completion design used as modeling inputs. The modeling results can then be modified using the equivalence ratio to compute a total surface area and fracture width of the plurality of hydraulic fractures.

A method of performing oilfield operations at a wellsite is disclosed. The wellsite is positioned about a subterranean formation having a wellbore therethrough and a fracture network therein. The fracture network includes natural fractures. The method involves generating fracture parameters including a hydraulic fracture network based on wellsite data including a mechanical earth model, generating reservoir parameters including a reservoir grid based on the wellsite data and the generated fracture wellsite parameters, forming a finite element grid from the fracture and reservoir parameters by coupling the hydraulic fracture network to the reservoir grid, generating integrated geomechanical parameters including estimated microseismic events based on the finite element grid, and performing fracture operations and production operations based on the integrated geomechanical parameters.

The present disclosure relates to an ensemble-based reservoir characterization method using multiple Kalman gains and dynamic data selection. The method includes preparing available data; generating initial ensembles by using the prepared static data; clustering and separating the generated initial models on the basis of a distance-based method; selecting the dynamic data; dynamically simulating the selected dynamic data by using the generated ensembles; calculating multiple Kalman gains by using initial models clustered in the same group as the selected dynamic data; updating ensemble members by means of the selected dynamic data and the multiple Kalman gains; and predicting a movement of a reservoir by using the updated models, and evaluating uncertainty thereof. Therefore, multiple Kalman gains are calculated and a final model is obtained using the selected dynamic data, and a reliable uncertainty evaluation and a future movement prediction can be performed within a short time by using the final model.

System and method of developing a hydrocarbon reservoir that includes the following: determining, for each of the layers, a reservoir model defining layers representing a portion of a hydrocarbon reservoir; determining points of intersection; generating, for each of the layers, a two-dimensional (2D) unstructured mesh layer including a triangulated mesh; generating, for each pair of adjacent 2D unstructured mesh layers of the reservoir model, a three-dimensional (3D) tetrahedral mesh; generating, for the 3D tetrahedral mesh, a 3D triangulated tetrahedral mesh; generating, for the 3D triangulated tetrahedral mesh, a 3D vertically-refined triangulated tetrahedral mesh; generating, for the 3D vertically-refined triangulated tetrahedral mesh, a 3D dual mesh; and generating, using the 3D dual mesh, a simulation of the hydrocarbon reservoir represented by the layers.

In some embodiments, a system, as well as a method and an article, may operate to generate map values for a plurality of parameters corresponding to respective grid blocks of a reservoir, wherein the values have been previously generated based on an initial simulation result from a model of the reservoir; to generate a sector surrogate model that includes a subset of grid blocks of the reservoir based on a criterion for identifying grid blocks that negatively affect simulation of the reservoir; to provide data inputs to execute a simulation of the reservoir using the sector surrogate model; and to generate revised data inputs, based on results of the simulation of the reservoir using the sector surrogate model, to use in a subsequent simulation using the model of the reservoir. Additional apparatus, systems, and methods are disclosed.