A plating support system is provided and includes a simulator that predicts an in-plane uniformity value of a plating film formed on a substrate based on assumed conditions for an electroplating treatment of the substrate; a numerical analysis data storage unit that stores numerical analysis data in which each assumed condition is associated with the in-plane uniformity value for plural assumed conditions; a regression analysis unit that estimates a model that the in-plane uniformity value is an objective variable and variables of assumed conditions are explanatory variables by regression analysis based on the numerical analysis data; and an implement condition search unit that uses the estimated model to search for implement conditions that are recommended values of the assumed conditions related to the in-plane uniformity of the plating film formed in the electroplating treatment of the substrate to be plated.

A computer device for characterising execution time by a processor, comprising a memory (8) which receives benchmark program data, sets of characterisation configuration data and sets of execution case data, and a constructor (4) which determines, for each set of execution case data, a set of worst-case configuration data of the processor and a set of initialisation values based on a set of execution case data, and determining a reference execution time by executing the benchmark program according to the set of execution case data using the processor configured with the set of configuration data with the set of initialisation values, all the reference execution times forming a set of reference execution times. The constructor (4) determines, for each set of characterisation configuration data, a set of characterisation execution times comprising a number of characterisation execution times equal to the number of elements of the set of reference execution times and each characterisation execution time being determined by executing the benchmark program using the processor configured with a set of characterisation configuration data and with a set of initialisation values representing the benchmark program and the processor. The constructor (4) determines a set of characterisation coefficients by applying an algorithm for determining the maximum likelihood between the set of reference execution times (M0) and the sets of characterisation execution times (M[k]), and the device returns the set of characterisation configuration data and the set of characterisation coefficients.

Embodiments disclosed herein generally relate to a method and system for oil and gas predictive analytics. A computer system receives a set of production information for a well located in a region. The computing system generates a set of general reference groups comprising one or more reference wells for the region. The computing system determines whether the set of production information for the well includes the threshold amount of production information. The computing system selects a subset of reference wells from the general reference groups based on one or more traits of the well. The computing system generates a reference curve based on the set of production information associated with each reference well in the subset of reference wells. The computing system fits a decline curve to the reference curve, to determine an estimated ultimate recovery of the well.

A system may measure, using a measurement device, a response associated with a sample to an excitation. Then, the system may compute, using the measured response and the excitation as inputs to a predetermined predictive model, model parameters on a voxel-by-voxel basis in a forward model with multiple voxels that represent the sample. The forward model may simulate response physics occurring within the sample to a given excitation. For example, the forward model may be based on differential or phenomenological equations that approximates the response physics. Moreover, the system may determine an accuracy of the model parameters by comparing at least the measured response and a calculated predicted value of the response using the forward model, the model parameters and the excitation. When the accuracy exceeds a predefined value, the system may provide the model parameters as an output to: a user, another electronic device, a display, and/or a memory.

Embodiments disclosed herein generally relate to a method and system for oil and gas predictive analytics. A computer system receives a set of production information for a well located in a region. The computing system generates a set of general reference groups comprising one or more reference wells for the region. The computing system determines whether the set of production information for the well includes the threshold amount of production information. The computing system selects a subset of reference wells from the general reference groups based on one or more traits of the well. The computing system generates a reference curve based on the set of production information associated with each reference well in the subset of reference wells. The computing system fits a decline curve to the reference curve, to determine an estimated ultimate recovery of the well.

A method of obtaining a model of a sub-surface region, the model comprising a three dimensional matrix of sub-surface material property values. The method comprises providing a first approximation of said model, and performing simulations using the model to generate (i) a reference upgoing energy wave data set d.sub.0, where the acquisition parameters used in the simulation are a set of known acquisition parameters, and (ii) a plurality of perturbed upgoing energy wave data sets d.sub.0 where the acquisition parameters are obtained by randomly perturbing said set of known acquisition parameters. The method further comprises forming a matrix D, the columns of which are the differences d.sub.i−d.sub.0 between the ith perturbed data set and the reference data set, applying an analysis to the matrix D to describe the matrix using a set of vectors, and selecting the most important vectors from said set of vectors to obtain a subset of vectors that describe said matrix D to an approximation. An iterative procedure is performed to improve said first approximation of said model, or a further approximation model, wherein, at each step of the procedure, a model update Δm is determined, the model update being that model update that minimises a measure of the data misfit between said upgoing energy wave data set d.sub.rec and a data set simulated using the updated model, said measure using said subset of vectors to account for uncertainty in the upgoing energy wave data set d.sub.rec.

In this disclosure, quantum algorithms are presented for simulating Hamiltonian time-evolution e.sup.−i(A+B)t in the interaction picture of quantum mechanics on a quantum computer. The interaction picture is a known analytical tool for separating dynamical effects due to trivial free-evolution A from those due to interactions B. This is especially useful when the energy-scale of the trivial component is dominant, but of little interest. Whereas state-of-art simulation algorithms scale with the energy ∥A+B∥≤∥A∥+∥B∥ of the full Hamiltonian, embodiments of the disclosed approach generally scale linearly with the sum of the Hamiltonian coefficients from the low-energy component B and poly-logarithmically with those from A.

A comprehensive reconstruction method for long-series sediment data in data-lacking areas includes steps of: collecting hydrological and sediment data of a target river section; calculating sediment data in data-rich years with a flow-sediment content annual relationship curve method; calculating sediment data in only water quality and sediment test years with a correlation method between water quality and sediment data and hydrological station sediment data; calculating sediment data in data-lacking years with an adjacent station same year flow-sediment content relationship curve method; and calculating sediment data in remaining years with a multi-year average flow-sediment content relationship curve method. The method comprehensively adopts four methods to reconstruct the long-series sediment data based on sediment actual observation and characteristics in the data-lacking areas, which can make up for the limitations and deficiencies between the four methods, and the required data is easier to collect than those in the conventional methods.

The present disclosure discloses a method for calculating a safe drilling fluid density in a fractured formation, including the following steps: S1, performing image processing to identify a downhole fracture; S2, establishing three-dimensional (3D) geological models based on parameters of the downhole fracture, and establishing a drilling wellbore model based on a size and length of a wellbore; S3, assigning the model with material parameters, boundary conditions, and upper and lower bounds of an initial drilling fluid density, and calculating accuracy; S4, solving the 3D geological models using a 3-dimension distinct element code (3DEC) and determining stability of a well wall; S5, determining upper and lower bounds of a drilling fluid density using dichotomy; S6, repeating steps S4 to S5; and S7, after set accuracy conditions are reached, saving and outputting the safe drilling fluid density.

A computer-implemented method for designing a dispersive optical component includes: (i) defining a loss function within a simulation space composed of multiple voxels, the simulation space encompassing optical interfaces of the component, the loss function corresponding to a target dispersion profile for the component including a relationship between a scattering angle and a wavelength of an incident electromagnetic field for different operative wavelengths; (ii) defining an initial structure for the optical interfaces, at least some of the voxels corresponding to each optical interface having a dimension smaller than a smallest operative wavelength of the component; and (iii) determining, using a computer system, a structure for each optical interface using a finite-difference time domain solver to solve Maxwell's equations so that a loss determined according to the loss function is above a specified threshold.