Systems, methods, and computer-readable media for determining the production rate of oil produced from each of a plurality of oil-bearing geological layers in an oil field. In some embodiments, the method comprises allocating injected fluid into each layer of a plurality of oil-bearing geological layers to a plurality of paths from injection sites of injection wells to production wells in each layer by balancing the mass of fluid injected into and the total fluid recovered from each oil-bearing geological layer. In some embodiments, the method comprises calculating estimated geological properties for each path in the plurality of paths to match total oil and injection fluid recovered at each production well in the plurality of production wells. In some embodiments, the method comprises using the estimated geological properties, calculating an oil production rate for each path between an injector well and a production well in a geological layer.

A free-space well connection method of determining parameters for modeling a reservoir is disclosed. The method is conducted by a computer system (100) having a processor (110) and non-transitory memory (120) that stores data including instructions to be executed by the processor (110), the processor (110) executing a modeling module (102) stored in the memory (120), the modeling module (102) having data representing a grid with a well-cell (202) and at least one link-cell (204), each of the at least one link-cell (204.sub.i) having a common face (Γ.sub.i) with the well-cell (202), the well-cell (202) and the at least one link-cell (204) being a local cell array. The method comprises steps of modeling, by the modeling module (102), the local cell array as having an infinite outer boundary by modeling the grid as an infinite space around the local cell array for determination of parameters for the well-cell (202) and determining, by the modeling module (102), one or more of a well connection transmissibility factor (T.sub.w) and at least one inter-cell transmissibility multiplier (M.sub.i).

Performance of computers is improved during determination of pressure distribution among grid cells of a reservoir model during reservoir simulation by computer processing. Convergence can prove difficult to obtain when conditions cause a coefficient matrix involved in the processing to become indefinite. An indefinite coefficient matrix can occur either due to physical conditions in the reservoir related to vapor liquid equilibria, or due to nonphysical conditions created numerically due to improper derivatives. The conventional previously taken corrective action of time step cutting is avoided when convergence becomes difficult during reservoir simulation. Time step cutting has proven to be very costly, in terms of computer usage and time, for very large reservoirs having models involving millions, billions or more number of unknown parameter values.

System and methods of controlling diverter placement during stimulation treatments are provided. Data relating to at least one downhole parameter is obtained for a current treatment stage within a subsurface formation. A response of the diverter to be injected during a diversion phase of the current stage on the downhole parameter is estimated, based on the obtained data and a diagnostic data model. Values for diversion control parameters are calculated, based on the estimated response. As the diverter is injected into the formation, an actual response of the diverter is monitored. Upon determining that a difference between the actual and estimated response exceeds an error tolerance threshold, the model is updated. The model is further updated over subsequent iterations of the diversion phase when the actual response is less than the estimated response. Subsequent diversion phases are performed over a remainder of the current stage, based on the updated model.

A reservoir model for values of a formation property is simulated using a turning bands method with distributed computing. A distributed computing system simulates the reservoir on separate machines in parallel in several stages. First, line distributions are simulated independently on turning bands. The reservoir model is partitioned into tiles and unconditional simulations are run on each tile in parallel using the corresponding simulated turning bands. The unconditional simulations within each tile are conditioned on known formation values to generate conditional simulations. Conditional simulations are aggregated across tiles to create the simulated reservoir model.

Methods for determining reservoir characteristics of a well can include receiving a first core from the well; performing an experiment to determine the wave velocity associated with a first direction of the first core, the experiment including: transmitting an ultrasonic wave through the first core in the first direction; receiving the transmitted ultrasonic wave; and determining a directional wave velocity of the first core based on the transmitted ultrasonic wave and the received transmitted ultrasonic wave, wherein the directional wave velocity represents a wave velocity along the first direction; rotating the first core about a longitudinal axis of the first core; and performing the experiment along a second direction of the first core.

Some implementations provide a method including: accessing measurement data that characterize one or more features of a reservoir, wherein the measurement data are from more than well locations of the reservoir and from a range of depths inside the reservoir; detecting portions of the measurement data that characterize the one or more features with a statistical metric that is below a pre-determined threshold; based on removing the portions of the measurement data, identifying a plurality of layers along the range of depths of the reservoir; within each layer of the plurality of layers, grouping the measurements data among a plurality of clusters, each corresponding to a flow unit (FU) and determined by a machine learning algorithm; generating a three-dimensional (3D) permeability model of the reservoir based on the FU of each layer and a saturation height function; and simulating a performance of the reservoir based on the 3D permeability model.

A method for estimating a thickness of a deep reservoir may include obtaining seismic data relating to the deep reservoir. The method may include performing spectral decomposition to obtain one or more frequency components from the seismic data. The method may include identifying a number of mono-frequency horizons corresponding to high frequencies in the seismic data, determining whether the deep reservoir is a thin reservoir based on the number of mono-frequency horizons, and estimating the thickness of the deep reservoir when the deep reservoir is determined to be the thin reservoir.

Method for characterizing subterranean formation is described. One method involves injecting a fluid into an active well of the subterranean formation at a pressure sufficient to induce one or more hydraulic fractures. Measuring, via a pressure sensor, a poroelastic pressure response caused by inducing of the one or more hydraulic fractures. The pressure sensor is in at least partial hydraulic isolation with the one or more hydraulic fractures.

Systems and methods are disclosed for generating subsurface data as a function of position and time. Exemplary implementations may include obtaining a first initial subsurface model and a first set of subsurface parameters, obtaining training subsurface property data and a first training subsurface dataset, generating a first conditioned subsurface model, and storing the first conditioned subsurface model.