Systems and methods of determining an equivalent black oil model are disclosed. In one embodiment, a method of determining an equivalent black oil model of a reservoir includes generating three-dimensional PVT properties using a compositional model, calculating original fluid in place and reservoir performance characteristics from the three-dimensional PVT properties, and converting the three-dimensional PVT properties to a two-dimensional PVT table. The method further includes, until an equivalency metric is satisfied, generating one or more grouped PVT property tables, initializing a black oil model with the one or more grouped PVT property tables, calculating estimated fluid in place and estimated reservoir performance characteristics using the black oil model, and comparing the estimated fluid in place and the estimated reservoir performance characteristics with the original fluid in place and the reservoir performance characteristics to determine whether the equivalency metric is satisfied.

Fluid flow dynamics modeling methods and system are provided. In some embodiments, such methods include determining a bottomhole pressure for the unconventional reservoir based, at least in part, on a tubing head pressure for one or more wells penetrating at least a portion of the unconventional reservoir, one or more fluid properties of a fluid in the unconventional reservoir, and a well production volume for the one or more wells; determining a Productivity Index (PI) for the unconventional reservoir, based, at least in part, on the one or more fluid properties and measured well data for the one or more wells, wherein the measured well data includes a well production rate and a well flowing pressure; and determining a fluid depletion of the unconventional reservoir based, at least in part, on the bottomhole pressure and the PI.

Systems and methods include a computer-implemented method for predicting values. A numerical simulation model is generated based on observed production rates and flowing pressure and build-up (FPBU) test rates. A simulated diagnostic plot is generated using simulated FPBU data extracted from the numerical simulation model. Simulation model properties of the numerical simulation model are adjusted until the simulated diagnostic plot matches within a tolerance to an observed FPBU diagnostic plot. Predicted values including a static pressure, a water cut, and a gas-oil rate (GOR) are predicted using the simulated FPBU data. Observed data of a reservoir is reviewed and quality-checked based on comparing the predicted values within a tolerance of the observed data.

A method of determining at least one property of a liquid system using a modeling system, the liquid system including at least one molecule in a solvent, the modeling system including a processor, comprises generating a quantum model of the liquid system using the processor of the modeling system, the quantum model including a device region and a lead region, the device region being spherical, paraboloid, cubic or arbitrary in shape and encompassing the at least one molecule and a portion of the solvent of the liquid system, the lead region encompassing a region of the solvent surrounding the device region, determining a first property of the device region by solving a first quantum equation for the device region using the processor of the system, determining the first property of the lead region by solving the first quantum equation under open boundary conditions for the lead region using the processor of the system, and combining the first property of the device region with the first property of the lead region to arrive at a total first property for the liquid system using the processor of the system.

Provided is a particle behavior simulation method and a particle behavior simulation system for representing behavior of each particle in a large-scale particle system quickly and accurately. The particle behavior simulation system is configured to execute a coarse-graining processing of reconstituting a plurality of particles that mix with each other and constitute a predetermined powder as a particle system into a plurality of particle groups each including a plurality of the particles, an acting force calculation processing of calculating an acting force acting on each of the particle groups, and a mixing state calculation processing of calculating a parameter representing a mixing state of constituent particles of each of the particle groups after a predetermined time based on the calculated acting force.

For device groups (3) each including devices (31) included in a processing plant (1) processing fluid and each having an occupied area (30) set therefor, connection information representing two specific device groups (3) are connected and paired via a pipe (4) and pipe information required for calculating use amount of a pipe material are set. Then, a first step, including arranging the device groups (3) in an installation area (10) of the processing plant (1) so that an outer edge of the occupied area (30) and a long side of a pipe-rack arrangement area (20) contact with each other, and a second step, including calculating a total use amount of the pipe material of the pipe (4) supported by a pipe rack (21, 22, 23), are repeated. From results obtained by changing the arrangement of the device groups (3), arrangements having small total use amounts of pipe materials are selected.

An estimated gas leak flow rate can be determined using a teaching set of concentration profiles, a regression model implemented by a machine-learning subsystem, and a subset of attributes measured within an environment. The teaching set of concentration profiles can include gas flow rates associated with relevant attributes. The regression model can be transformed into a gas leak flow regression model via the machine-learning subsystem using the teaching set. The subset of attributes measured within the environment can be applied to the gas leak flow regression model to determine other attributes absent from the subset of attributes and an estimated gas flow rate for the environment. A gas leak attenuation action can be performed in response to the estimated gas flow rate.

A runoff yield calculation method and device based on double free reservoirs, and a storage medium are provided, the method includes: forming a four-layer vadose zone structure by making a tension water storage layer be located under a deep vadose zone based on a three-layer vadose zone structure of a Xin'anjiang model; dividing a space occupied by free water in the four-layer vadose zone structure into an upper free reservoir and a lower free reservoir; calculating a time interval runoff yield by using a saturation excess runoff method; and dividing, based on a runoff yield structure of the double free reservoirs, the time interval runoff yield into a surface runoff, an interflow and a subsurface runoff. The method proposes a runoff yield structure of double free reservoirs, which can be well applied to semi-arid and semi humid watersheds with deeper buried depth of shallow groundwater.

Fluid flow dynamics modeling methods and system are provided. In some embodiments, such methods include determining a bottomhole pressure for the unconventional reservoir based, at least in part, on a tubing head pressure for one or more wells penetrating at least a portion of the unconventional reservoir, one or more fluid properties of a fluid in the unconventional reservoir, and a well production volume for the one or more wells; determining a Productivity Index (PI) for the unconventional reservoir, based, at least in part, on the one or more fluid properties and measured well data for the one or more wells, wherein the measured well data includes a well production rate and a well flowing pressure; and determining a fluid depletion of the unconventional reservoir based, at least in part, on the bottomhole pressure and the PI.

According to an embodiment, an information processing device of an embodiment includes a memory and one or more processors coupled to the memory. The memory stores therein time-series data including at least one of a dependent variable and an independent variable. The one or more processors are configured to: generate a nonlinear function based on at least one of the dependent variable and the independent variable; generate a linear regression equation in which the nonlinear function is a basis function; estimate a coefficient of the linear regression equation; calculate a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence; correct the coefficient based on the degree of influence; and output the linear regression equation expressed by the corrected coefficient.