A method, apparatus and system is provided for assessing risk for well completion, comprising: obtaining, using an input interface, a Below Rotary Table hours and a plurality of well-field parameters for one or more planned runs, determining, using at least one processor, one or more non-productive time values that correspond to the one or more planned runs based upon the well-field parameters, developing, using at least one processor, a non-productive time distribution and a Below Rotary Table distribution via one or more Monte Carlo trials; and outputting, using a graphic display, a risk transfer model results based on a total BRT hours from the Below Rotary Table and the non-productive time distribution produced from the one or more Monte Carlo trials.

A method, apparatus and system is provided for assessing risk for well completion, comprising: obtaining, using an input interface, a Below Rotary Table hours and a plurality of well-field parameters for one or more planned runs, determining, using at least one processor, one or more non-productive time values that correspond to the one or more planned runs based upon the well-field parameters, developing, using at least one processor, a non-productive time distribution and a Below Rotary Table distribution via one or more Monte Carlo trials; and outputting, using a graphic display, a risk transfer model results based on a total BRT hours from the Below Rotary Table and the non-productive time distribution produced from the one or more Monte Carlo trials.

A method, apparatus and system is provided for assessing risk for well completion, comprising: obtaining, using an input interface, a Below Rotary Table hours and a plurality of well-field parameters for one or more planned runs, determining, using at least one processor, one or more non-productive time values that correspond to the one or more planned runs based upon the well-field parameters, developing, using at least one processor, a non-productive time distribution and a Below Rotary Table distribution via one or more Monte Carlo trials; and outputting, using a graphic display, a risk transfer model results based on a total BRT hours from the Below Rotary Table and the non-productive time distribution produced from the one or more Monte Carlo trials.

A system, device, and method for predicting an active set of compounds that bind to a biomolecular target is disclosed. The system and device contain modules allowing for the prediction of an active set of compounds. A core identification module can identify the core of an initial lead compound. A core hopping module is used to identify potential lead compounds having different cores compared to the core of an initial lead compound. A scoring module can use computational techniques to calculate the relative binding free energy of each identified potential lead compound. An activity prediction module can use the relative binding free energy calculations to predict an active set of compounds that bind to the biomolecular target. Empirical analysis can be used to inform the accuracy and completeness of the predicted active set of compounds.

A system, device, and method for predicting an active set of compounds that bind to a biomolecular target is disclosed. The system and device contain modules allowing for the prediction of an active set of compounds. A core identification module can identify the core of an initial lead compound. A core hopping module is used to identify potential lead compounds having different cores compared to the core of an initial lead compound. A scoring module can use computational techniques to calculate the relative binding free energy of each identified potential lead compound. An activity prediction module can use the relative binding free energy calculations to predict an active set of compounds that bind to the biomolecular target. Empirical analysis can be used to inform the accuracy and completeness of the predicted active set of compounds.

The invention relates to the technical field of oilfield exploration, and discloses a 4D quantitative and intelligent diagnosis method and system for spatio-temporal evolution of oil-gas reservoir damage types and extent. The method includes: determining a characteristic parameter characterizing reservoir damage by each of a plurality of factors based on a spatio-temporal evolution simulation equation of reservoir damage by each of the plurality of factors; and determining an effective characteristic parameter characterizing the damage extent of the reservoir based on the characteristic parameter characterizing reservoir damage rby each of the plurality of factors. The invention can quantitatively simulate the characteristic parameters of reservoir damage caused by the various factors and a total characteristic parameter of the reservoir damage. Therefore for a well without reservoir damage, performing quantitative prediction of reservoir damage and spatio-temporal deduction of damage laws is of scientific guidance significance for preventing reservoir damage, and formulating development plans for oil pools and subsequent well stimulation measures, and for a well with reservoir damage, also performing quantitative diagnosis of reservoir damage and spatio-temporal deduction of damage laws achieves optimal design of a declogging measure and improvement or restoration of oil-gas well production and water well injection capacity.

Methods, systems, and apparatus for training quantum evolutions using sub-logical controls. In one aspect, a method includes the actions of accessing quantum hardware, wherein the quantum hardware includes a quantum system comprising one or more multi-level quantum subsystems; one or more control devices that operate on the one or more multi-level quantum subsystems according to one or more respective control parameters that relate to a parameter of a physical environment in which the multi-level quantum subsystems are located; initializing the quantum system in an initial quantum state, wherein an initial set of control parameters form a parameterization that defines the initial quantum state; obtaining one or more quantum system observables and one or more target quantum states; and iteratively training until an occurrence of a completion event.

Metallic alloy development has been traditionally based on experimental or theoretical equilibrium phase diagrams and the like. The synthesis, processing and mechanical testing of small and large real samples are a challenging task requiring huge amount of effort in terms of time, money, resource, tedious testing and processing equipment and man-hour for which conventional Calphad calculations etc. alone do not help much in their local structure and related property prediction. Embodiments of the present disclosure provide simulation systems and methods for structure evolution and property prediction Molecular Dynamics (MD) combined with accelerated Monte Carlo techniques, wherein information on atomic elements and composition specific to alloy material is obtained to generate a MD potential file that is further used to generate a 3D structure file by executing a structure equilibration technique. An optimized evolved 3D structure file is then generated that has atomic positions output and/or thermodynamic output for predicting properties.

Disclosed is a target-based drug screening method using inverse quantitative structure-(drug)performance relationships (QSPR) analysis and molecular dynamics simulation. The method includes modeling a molecular structure of a test compound group against a target molecule, obtaining a quantitative structure-(drug)performance relationships (QSPR) of the test compound group, acquiring the optimal pharmacophore of a novel target-based drug through a numerical inversion of the QSPR, and selecting drug candidates having a molecular structure similar to the optimum pharmacophore from the test compound group.

Disclosed is a target-based drug screening method using inverse quantitative structure-(drug)performance relationships (QSPR) analysis and molecular dynamics simulation. The method includes modeling a molecular structure of a test compound group against a target molecule, obtaining a quantitative structure-(drug)performance relationships (QSPR) of the test compound group, acquiring the optimal pharmacophore of a novel target-based drug through a numerical inversion of the QSPR, and selecting drug candidates having a molecular structure similar to the optimum pharmacophore from the test compound group.