Keyword reference¶
The keywords that are recognized by the Everest configuration system are described below. Each keyword is followed by its data type in brackets, usually some basic type. It may also be a list, which indicates that the keyword can be repeated as a yaml list. If a keyword is required, this is also indicated in brackets after the keyword name.
EverestConfig
Type: objectNo Additional Properties
Controls
Type: arrayDefines a list of controls.
Controls should have unique names each control defines
a group of control variables
Must contain a minimum of 1 items
Each item of this array must be:
ControlConfig
Type: objectNo Additional Properties
Name
Type: stringControl name
Type
Type: enum (of string)Only two allowed control types are accepted
well_control: Standard built-in Everest control type designed for field optimization
generic_control: Enables the user to define controls types to be employed for customized optimization jobs.
Must be one of:
- "well_control"
- "generic_control"
Variables
List of control variables
No Additional Items
Each item of this array must be:
variable control
Type: objectNo Additional Properties
Name
Type: stringControl variable name
Control Type
Default: nullThe type of control. Set to "integer" for discrete optimization. This may be
ignored if the algorithm that is used does not support different control types.
Must be one of:
- "real"
- "integer"
Enabled
Default: nullIf True, the variable will be optimized, otherwise it will be fixed to the
initial value.
Auto Scale
Default: nullCan be set to true to re-scale variable from the range
defined by [min, max] to the range defined by scaled_range (default [0, 1]).
Scaled Range
Default: nullCan be used to set the range of the variable values
after scaling (default = [0, 1]).
This option has no effect on discrete controls.
Must contain a minimum of 2 items
Must contain a maximum of 2 items
Tuple Validation
Item at 1 must be:
Item at 2 must be:
Min
Default: nullMinimal value allowed for the variable
initial_guess is required to be greater than this value.
Max
Default: nullMax value allowed for the variable
initial_guess is required to be less than this value.
Perturbation Magnitude
Default: nullSpecifies the perturbation magnitude for this particular variable.
This feature adds flexibility to combine controls into more logical
of structures at the same time allowing the variable to contain time (
how long rate applies for) & value (the actual rate).
NOTE: In most cases this should not be configured, and the default value should be used.
Value must be strictly greater than 0
The backend used by Everest for sampling points
SamplerConfig
Type: objectNo Additional Properties
Backend
Default: null(deprecated) The backend used by Everest for sampling points.
The sampler backend provides the methods for sampling the points used to
estimate the gradient. The default is the built-in 'scipy' backend.
Backend Options
Default: nullSpecifies a dict of optional parameters for the sampler backend.
This dict of values is passed unchanged to the selected method in the backend.
Additional Properties of any type are allowed.
Type: objectMethod
Default: nullThe sampling method or distribution used by the sampler backend.
Initial Guess
Default: nullStarting value for the control variable, if given needs to be in the interval [min, max]
Index
Default: nullIndex should be given either for all of the variables or for none of them
Value must be greater or equal to 0
No Additional Items
Each item of this array must be:
ControlVariableGuessListConfig
Type: objectNo Additional Properties
Name
Type: stringControl variable name
Control Type
Default: nullThe type of control. Set to "integer" for discrete optimization. This may be
ignored if the algorithm that is used does not support different control types.
Must be one of:
- "real"
- "integer"
Enabled
Default: nullIf True, the variable will be optimized, otherwise it will be fixed to the
initial value.
Auto Scale
Default: nullCan be set to true to re-scale variable from the range
defined by [min, max] to the range defined by scaled_range (default [0, 1]).
Scaled Range
Default: nullCan be used to set the range of the variable values
after scaling (default = [0, 1]).
This option has no effect on discrete controls.
Must contain a minimum of 2 items
Must contain a maximum of 2 items
Tuple Validation
Item at 1 must be:
Item at 2 must be:
Min
Default: nullMinimal value allowed for the variable
initial_guess is required to be greater than this value.
Max
Default: nullMax value allowed for the variable
initial_guess is required to be less than this value.
Perturbation Magnitude
Default: nullSpecifies the perturbation magnitude for this particular variable.
This feature adds flexibility to combine controls into more logical
of structures at the same time allowing the variable to contain time (
how long rate applies for) & value (the actual rate).
NOTE: In most cases this should not be configured, and the default value should be used.
Value must be strictly greater than 0
The backend used by Everest for sampling points
SamplerConfig
Type: objectNo Additional Properties
Backend
Default: null(deprecated) The backend used by Everest for sampling points.
The sampler backend provides the methods for sampling the points used to
estimate the gradient. The default is the built-in 'scipy' backend.
Backend Options
Default: nullSpecifies a dict of optional parameters for the sampler backend.
This dict of values is passed unchanged to the selected method in the backend.
Additional Properties of any type are allowed.
Type: objectMethod
Default: nullThe sampling method or distribution used by the sampler backend.
Initial Guess
Type: array of number Default: nullList of Starting values for the control variable
No Additional ItemsEach item of this array must be:
Initial Guess
Default: nullInitial guess for the control group all control variables with initialguess not
defined will be assigned this value. Individual initialguess values in the control
variables will overwrite this value.
Control Type
Type: enum (of string) Default: "real"The type of the controls for the control group. Individual control types in the
control variables will override this value. Set to "integer" for discrete
optimization. This may be ignored if the algorithm that is used does not support
different control types.
Must be one of:
- "real"
- "integer"
Enabled
Type: boolean Default: trueIf True, all variables in this control group will be optimized. If set to False
the value of the variables will remain fixed.
Min
Default: nullDefines left-side value in the control group range [min, max].
This value will be overwritten by the control variable min value if given.
The initial guess for both the group and the individual variables needs to be contained
in the resulting [min, max] range
Max
Default: nullDefines right-side value in the control group range [min, max].
This value will be overwritten by the control variable max value if given.
The initial guess for both the group and the individual variables needs to be contained
in the resulting [min, max] range
Perturbation Type
Type: enum (of string) Default: "absolute"Example: absolute or relative
Specifies the perturbation type for a set of controls of a certain type. The
perturbation type keyword defines whether the perturbation magnitude
(perturbation_magnitude) should be considered as an absolute value or relative
to the dynamic range of the controls.
NOTE: currently the dynamic range is computed with respect to all controls, so
defining relative perturbation type for control types with different dynamic
ranges might have unintended effects.
Must be one of:
- "absolute"
- "relative"
Perturbation Magnitude
Default: nullSpecifies the perturbation magnitude for a set of controls of a certain type.
This controls the size of perturbations (standard deviation of a
normal distribution) of controls used to approximate the gradient.
The value depends on the type of control and magnitude of the variables.
For continuous controls smaller values should give a better gradient,
whilst for more discrete controls larger values should give a better
result. However, this is a balance as too large or too small
of values also cause issues.
NOTE: In most cases this should not be configured, and the default value should be used.
Scaled Range
Type: array Default: [0.0, 1.0]Can be used to set the range of the control values
after scaling (default = [0, 1]).
This option has no effect on discrete controls.
Must contain a minimum of 2 items
Must contain a maximum of 2 items
Tuple Validation
Item at 1 must be:
Item at 2 must be:
A sampler specification section applies to a group of controls, or to an
individual control. Sampler specifications are not required, with the
following behavior, if no sampler sections are provided, a normal
distribution is used.
If at least one control group or variable has a sampler specification, only
the groups or variables with a sampler specification are perturbed.
Controls/variables that do not have a sampler section will not be perturbed
at all. If that is not desired, make sure to specify a sampler for each
control group and/or variable (or none at all to use a normal distribution
for each control).
Within the sampler section, the shared keyword can be used to direct the
sampler to use the same perturbations for each realization.
SamplerConfig
Type: objectNo Additional Properties
Backend
Default: null(deprecated) The backend used by Everest for sampling points.
The sampler backend provides the methods for sampling the points used to
estimate the gradient. The default is the built-in 'scipy' backend.
Backend Options
Default: nullSpecifies a dict of optional parameters for the sampler backend.
This dict of values is passed unchanged to the selected method in the backend.
Additional Properties of any type are allowed.
Type: objectMethod
Default: nullThe sampling method or distribution used by the sampler backend.
Objective Functions
Type: arrayList of objective function specifications
Must contain a minimum of 1 items
Each item of this array must be:
ObjectiveFunctionConfig
Type: objectNo Additional Properties
Name
Type: stringWeight
Default: nullweight determines the importance of an objective function relative to the other
objective functions.
Ultimately, the weighted sum of all the objectives is what Everest tries to optimize.
Note that, in case the weights do not sum up to 1, they are normalized before being
used in the optimization process. Weights may be zero or negative, but should add up to
a positive value.
Scale
Default: nullscale is a division factor defined per objective function.
The value of each objective function is divided by the related scaling value.
When optimizing with respect to multiple objective functions, it is important
that the scaling is set so that all the scaled objectives have the same order
of magnitude. Ultimately, the scaled objectives are used in computing
the weighted sum that Everest tries to optimize.
Auto Scale
Default: nullauto_normalize can be set to true to automatically
determine the scaling factor from the objective value in batch 0.
If scale is also set, the automatic value is divided by its value.
Type
Default: nulltype can be set to the name of a method that should be applied to calculate a
total objective function from the objectives obtained for all realizations.
Currently, the only values supported are "mean" and "stddev", which calculate
the mean and the negative of the standard deviation over the realizations,
respectively. The negative of the standard deviation is used, since in general
the aim is to minimize the standard deviation as opposed to the mean, which is
preferred to be maximized.
OptimizationConfig
Type: objectOptimizer options
No Additional PropertiesAlgorithm
Type: string Default: "optpp_q_newton"Algorithm used by Everest. Defaults to
optppqnewton, a quasi-Newton algorithm in Dakota's OPT PP library.
Convergence Tolerance
Default: nullDefines the threshold value on relative change
in the objective function that indicates convergence.
The convergencetolerance specification provides a real value for controlling
the termination of iteration. In most cases, it is a relative convergence tolerance
for the objective function; i.e., if the change in the objective function between
successive iterations divided by the previous objective function is less than
the amount specified by convergencetolerance, then this convergence criterion is
satisfied on the current iteration.
Since no progress may be made on one iteration followed by significant progress
on a subsequent iteration, some libraries require that the convergence tolerance
be satisfied on two or more consecutive iterations prior to termination of
iteration.
(From the Dakota Manual.)
Backend
Default: null(deprecated) The optimization backend used.".
Currently, backends are included to use Dakota or SciPy ("dakota" and "scipy").
The Dakota backend is the default, and can be assumed to be installed. The SciPy
backend is optional, and will only be available if SciPy is installed on the
system.
Backend Options
Default: nullDict of optional parameters for the optimizer backend.
This dict of values is passed unchanged to the selected algorithm in the backend.
Note that the default Dakota backend ignores this option, because it requires a
list of strings rather than a dictionary. For setting Dakota backend options, see
the 'option' keyword.
Additional Properties of any type are allowed.
Type: objectConstraint Tolerance
Default: nullDetermines the maximum allowable value of
infeasibility that any constraint in an optimization problem may possess and
still be considered to be satisfied.
It is specified as a positive real value. If a constraint function is greater
than this value then it is considered to be violated by the optimization
algorithm. This specification gives some control over how tightly the
constraints will be satisfied at convergence of the algorithm. However, if the
value is set too small the algorithm may terminate with one or more constraints
being violated.
(From the Dakota Manual.)
Directs the optimizer to use CVaR estimation.
When this section is present Everest will use Conditional Value at Risk (CVaR)
to minimize risk. Effectively this means that at each iteration the objective
and constraint functions will be calculated as the mean over the sub-set of the
realizations that perform worst. The size of this set is specified as an
absolute number or as a percentile value. These options are selected by setting
either the numberofrealizations option, or the percentile option,
which are mutually exclusive.
CVaRConfig
Type: objectNo Additional Properties
Number Of Realizations
Default: nullThe number of realizations used for CVaR estimation.
Sets the number of realizations that is used to calculate the total objective.
This option is exclusive with the percentile option.
Percentile
Default: nullThe percentile used for CVaR estimation.
Sets the percentile of distribution of the objective over the realizations that
is used to calculate the total objective.
This option is exclusive with the numberofrealizations option.
Value must be greater or equal to 0.0 and lesser or equal to 1.0
Max Batch Num
Default: nullLimits the number of batches of simulations
during optimization, where 0 represents unlimited simulation batches.
When maxbatchnum is specified and the current batch index is greater than
maxbatchnum an exception is raised.
Value must be strictly greater than 0
Max Function Evaluations
Default: nullLimits the maximum number of function evaluations.
The maxfunctionevaluations controls the number of control update steps the optimizer
will allow before convergence is obtained.
See max_iterations for a description.
Value must be strictly greater than 0
Max Iterations
Default: nullLimits the maximum number of iterations.
The difference between an iteration and a batch is that an iteration corresponds to
a complete accepted batch (i.e., a batch that provides an improvement in the
objective function while satisfying all constraints).
Value must be strictly greater than 0
Min Pert Success
Default: nullspecifies the minimum number of successfully completed
evaluations of perturbed controls required to compute a gradient. The optimization
process will stop if this minimum is not reached, and otherwise a gradient will be
computed based on the set of successful perturbation runs. The minimum is checked for
each realization individually.
A special case is robust optimization with perturbation_num: 1. In that case the
minimum applies to all realizations combined. In other words, a robust gradient may then
still be computed based on a subset of the realizations.
The user-provided value is reset to perturbation_num if it is larger than this number
and a message is produced. In the special case of robust optimization case with
perturbation_num: 1 the maximum allowed value is the number of realizations specified
by realizations instead.
Value must be strictly greater than 0
Min Realizations Success
Default: nullMinimum number of realizations
The minimum number of realizations that should be available for the computation
of either expected function values (both objective function and constraint
functions) or of the expected gradient. Note that this keyword does not apply
to gradient computation in the robust case with 1 perturbation in which the
expected gradient is computed directly.
The optimization process will stop if this minimum is not reached, and otherwise
the expected objective function value (and expected gradient/constraint function
values) will be computed based on the set of successful contributions. In other
words, a robust objective function, a robust gradient and robust constraint
functions may then still be computed based on a subset of the realizations.
The user-provided value is reset to the number of realizations specified by
realizations if it is larger than this number and a message is produced.
Note that it is possible to set the minimum number of successful realizations equal
to zero. Some optimization algorithms are able to handle this and will proceed even
if all realizations failed. Most algorithms are not capable of this and will adjust
the value to be equal to one.
Value must be greater or equal to 0
Options
Default: nullspecifies non-validated, optional
passthrough parameters for the optimizer
| Examples used are
| - maxrepetitions = 300
| - retryiffail
| - classicalsearch 1
No Additional Items
Each item of this array must be:
Additional Properties of any type are allowed.
Type: objectPerturbation Num
Default: nullThe number of perturbed control vectors per realization.
The number of simulation runs used for estimating the gradient is equal to the
the product of perturbation_num and model.realizations.
Value must be strictly greater than 0
Speculative
Type: boolean Default: falsespecifies whether to enable speculative computation.
The speculative specification enables speculative computation of gradient and/or
Hessian information, where applicable, for parallel optimization studies. By
speculating that the derivative information at the current point will be used
later, the complete data set (all available gradient/Hessian information) can be
computed on every function evaluation. While some of these computations will be
wasted, the positive effects are a consistent parallel load balance and usually
shorter wall clock time. The speculative specification is applicable only when
parallelism in the gradient calculations can be exploited by Dakota (it will be
ignored for vendor numerical gradients). (From the Dakota Manual.)
Parallel
Type: boolean Default: truewhether to allow parallel function evaluation.
By default Everest will evaluate a single function and gradient evaluation at
a time. In case of gradient-free optimizer this can be highly inefficient,
since these tend to need many independent function evaluations at each
iteration. By setting parallel to True, multiple functions may be evaluated in
parallel, if supported by the optimization algorithm.
The default is to use parallel evaluation if supported.
ModelConfig
Type: objectConfiguration of the Everest model
No Additional PropertiesRealizations
Type: array of integerRealizations to use in optimization ensemble.
This is a list [0, 1, ..., n-1] of all realizations in the ensemble, or a string
consisting of comma separated values and ranges, i.e '1, 2, 5-8, 10'.
Must contain a minimum of 1 items
Each item of this array must be:
Value must be greater or equal to 0
Realizations Weights
Type: array of numberList of weights, one per realization.
If specified, it must be a list of numeric values, one per realization.
No Additional ItemsEach item of this array must be:
EnvironmentConfig
Type: objectThe environment of Everest, specifies which folders are used for simulation and output, as well as the level of detail in Everest-logs
No Additional PropertiesSimulation Folder
Type: string Default: "simulation_folder"Folder used for simulation by Everest
Output Folder
Type: string Default: "everest_output"Folder for outputs of Everest
Log Level
Type: enum (of string) Default: "info"Defines the verbosity of logs output by Everest.
The default log level is info. All supported log levels are:
debug: Detailed information, typically of interest only when diagnosing
problems.
info: Confirmation that things are working as expected.
warning: An indication that something unexpected happened, or indicative of some
problem in the near future (e.g. disk space low). The software is still
working as expected.
error: Due to a more serious problem, the software has not been able to perform
some function.
critical: A serious error, indicating that the program itself may be unable to
continue running.
Must be one of:
- "debug"
- "info"
- "warning"
- "error"
- "critical"
Random Seed
Type: integer Default: nullRandom seed (must be positive)
Wells
Type: arrayA list of well configurations, all with unique names.
No Additional ItemsEach item of this array must be:
WellConfig
Type: objectNo Additional Properties
Name
Type: stringThe unique name of the well
Drill Date
Default: nullIdeal date to drill a well.
The interpretation of this is up to the forward model. The standard tooling will
consider this as the earliest possible drill date.
Drill Time
Type: integer Default: 0specifies the time it takes
to drill the well under consideration.
Value must be strictly greater than 0
Definitions
Type: objectSection for specifying variables.
Used to specify variables that will be replaced in the file when encountered.
| scratch: /scratch/ert/
| numreals: 10
| minsuccess: 13
| fixed_wells: [Prod1, Inj3]
Some keywords are pre-defined by Everest,
| realization: <GEO_ID>
| configpath: <CONFIG_PATH>
| runpath_file: <RUNPATH_FILE>
| eclbase: <ECLBASE>
and environment variables are exposed in the form 'os.NAME', for example:
| os.USER: $USER
| os.HOSTNAME: $HOSTNAME
| ...
Additional Properties of any type are allowed.
Type: objectInput Constraints
Type: arrayList of input constraints
No Additional ItemsEach item of this array must be:
InputConstraintConfig
Type: objectNo Additional Properties
Weights
Type: objectIf we are trying to constrain only one control (i.e the z
control) value, with an upper bound of 0.2, only control values (x, y, z) that
satisfy the following equation will be allowed: x-0 * 0 + y-1 * 0 + z-2 * 1 > 0.2
upper_bound: 0.2
weights:
- point_3D.x.0: 0
- point_3D.y.1: 0
- point_3D.z.2: 1
Each additional property must conform to the following schema
Type: numberTarget
Default: nullOnly control values that satisfy the following
equation will be allowed: sum of (<control> * weight) = target
Lower Bound
Type: number Default: -InfinityOnly control values that satisfy the following
equation will be allowed: sum of (<control> * weight) >= lower_bound`
Upper Bound
Type: number Default: InfinityOnly control values that satisfy the following
equation will be allowed: sum of (<control> * weight) <= upper_bound`
Output Constraints
Type: arrayA list of output constraints with unique names.
No Additional ItemsEach item of this array must be:
OutputConstraintConfig
Type: objectNo Additional Properties
Name
Type: stringThe unique name of the output constraint.
Target
Default: nullDefines the equality constraint
(f(x) - b) / c = 0,
where b is the target, f is a function of the control vector x, and c is the
scale (scale).
Auto Scale
Type: boolean Default: falseIf set to true, Everest will automatically
determine the scaling factor from the constraint value in batch 0.
If scale is also set, the automatic value is multiplied by its value.
Lower Bound
Type: number Default: -InfinityDefines the lower bound
(greater than or equal) constraint
(f(x) - b) / c >= 0,
where b is the lower bound, f is a function of the control vector x, and c is
the scale (scale).
Upper Bound
Type: number Default: InfinityDefines the upper bound (less than or equal) constraint:
(f(x) - b) / c <= 0,
where b is the upper bound, f is a function of the control vector x, and c is
the scale (scale).
Scale
Default: nullScaling of constraints (scale).
scale is a normalization factor which can be used to scale the constraint
to control its importance relative to the (singular) objective and the controls.
Both the upperbound and the function evaluation value will be scaled with this number.
That means that if, e.g., the upperbound is 0.5 and the scaling is 10, then the
function evaluation value will be divided by 10 and bounded from above by 0.05.
Install Jobs
Type: arrayA list of jobs to install
No Additional ItemsEach item of this array must be:
InstallJobConfig
Type: objectNo Additional Properties
Name
Type: stringname of the installed job
Source
Default: nullsource file of the ert job.
source will be deprecated, please use executable instead.
Executable
Default: nullExecutable to run
Install Workflow Jobs
Type: arrayA list of workflow jobs to install
No Additional ItemsEach item of this array must be:
InstallJobConfig
Type: objectNo Additional Properties
Name
Type: stringname of the installed job
Source
Default: nullsource file of the ert job.
source will be deprecated, please use executable instead.
Executable
Default: nullExecutable to run
Install Data
Type: arrayA list of install data elements from the install_data config
section. Each item marks what folders or paths need to be copied or linked
in order for the evaluation jobs to run.
Each item of this array must be:
InstallDataConfig
Type: objectNo Additional Properties
Source
Type: string Path to file or directory that needs to be copied or linked in the evaluation
execution context.
Target
Type: string Relative path to place the copy or link for the given source.
Link
Type: boolean Default: false If set to true will create a link to the given source at the given target,
if not set the source will be copied at the given target.
Install Templates
Type: arrayAllow the user to define the workflow establishing the model
chain for the purpose of sensitivity analysis, enabling the relationship
between sensitivity input variables and quantities of interests to be
evaluated.
Each item of this array must be:
InstallTemplateConfig
Type: objectNo Additional Properties
Template
Type: stringOutput File
Type: stringExtra Data
Default: nullServerConfig
Type: objectDefines Everest server settings, i.e., which queue system,
queue name and queue options are used for the everest server.
The main reason for changing this section is situations where everest
times out because it can not add the server to the queue.
This makes it possible to reduce the resource requirements as they tend to
be low compared with the forward model.
Queue system and queue name defaults to the same as simulator, and the
server should not need to be configured by most users.
This is also true for the --include-host and --exclude-host options
that are used by the SLURM driver.
Note that changing values in this section has no impact on the resource
requirements of the forward models.
Queue System
Default: nullDefines which queue system the everest submits jobs to
LocalQueueOptions
Type: objectNo Additional Properties
Name
Type: const Default: "local"Specific value:
"local"
Max Running
Type: integer Default: 0Value must be greater or equal to 0
Num Cpu
Type: integer Default: 1Value must be greater or equal to 0
Realization Memory
Type: integer Default: 0Value must be greater or equal to 0
Job Script
Type: string Default: "/home/docs/checkouts/readthedocs.org/user_builds/everest/envs/14.4.3/bin/fm_dispatch.py"Submit Sleep
Default: nullValue must be greater or equal to 0
Project Code
Default: nullActivate Script
Default: nullLsfQueueOptions
Type: objectNo Additional Properties
Name
Type: const Default: "lsf"Specific value:
"lsf"
Max Running
Type: integer Default: 0Value must be greater or equal to 0
Num Cpu
Type: integer Default: 1Value must be greater or equal to 0
Realization Memory
Type: integer Default: 0Value must be greater or equal to 0
Job Script
Type: string Default: "/home/docs/checkouts/readthedocs.org/user_builds/everest/envs/14.4.3/bin/fm_dispatch.py"Submit Sleep
Default: nullValue must be greater or equal to 0
Project Code
Default: nullActivate Script
Default: nullBhist Cmd
Default: nullMust be at least 1 characters long
Bjobs Cmd
Default: nullMust be at least 1 characters long
Bkill Cmd
Default: nullMust be at least 1 characters long
Bsub Cmd
Default: nullMust be at least 1 characters long
Exclude Host
Default: nullLsf Queue
Default: nullMust be at least 1 characters long
Lsf Resource
Default: nullSlurmQueueOptions
Type: objectNo Additional Properties
Name
Type: const Default: "slurm"Specific value:
"slurm"
Max Running
Type: integer Default: 0Value must be greater or equal to 0
Num Cpu
Type: integer Default: 1Value must be greater or equal to 0
Realization Memory
Type: integer Default: 0Value must be greater or equal to 0
Job Script
Type: string Default: "/home/docs/checkouts/readthedocs.org/user_builds/everest/envs/14.4.3/bin/fm_dispatch.py"Submit Sleep
Default: nullValue must be greater or equal to 0
Project Code
Default: nullActivate Script
Default: nullSbatch
Type: string Default: "sbatch"Must be at least 1 characters long
Scancel
Type: string Default: "scancel"Must be at least 1 characters long
Scontrol
Type: string Default: "scontrol"Must be at least 1 characters long
Sacct
Type: string Default: "sacct"Must be at least 1 characters long
Squeue
Type: string Default: "squeue"Must be at least 1 characters long
Exclude Host
Type: string Default: ""Include Host
Type: string Default: ""Partition
Default: nullMust be at least 1 characters long
Squeue Timeout
Type: number Default: 2Value must be strictly greater than 0
Max Runtime
Default: nullValue must be greater or equal to 0
TorqueQueueOptions
Type: objectNo Additional Properties
Name
Type: const Default: "torque"Specific value:
"torque"
Max Running
Type: integer Default: 0Value must be greater or equal to 0
Num Cpu
Type: integer Default: 1Value must be greater or equal to 0
Realization Memory
Type: integer Default: 0Value must be greater or equal to 0
Job Script
Type: string Default: "/home/docs/checkouts/readthedocs.org/user_builds/everest/envs/14.4.3/bin/fm_dispatch.py"Submit Sleep
Default: nullValue must be greater or equal to 0
Project Code
Default: nullActivate Script
Default: nullQsub Cmd
Default: nullMust be at least 1 characters long
Qstat Cmd
Default: nullMust be at least 1 characters long
Qdel Cmd
Default: nullMust be at least 1 characters long
Queue
Default: nullMust be at least 1 characters long
Cluster Label
Default: nullMust be at least 1 characters long
Job Prefix
Default: nullMust be at least 1 characters long
Keep Qsub Output
Type: boolean Default: falseSimulatorConfig
Type: objectSimulation settings
No Additional Propertiesqueue_system:
max_running: 3
name: local
Cores Per Node
Default: nulldefines the number of CPUs when running
the forward models. This can for example be used in conjunction with the Eclipse
parallel keyword for multiple CPU simulation runs. This keyword has no effect
when running with the local queue.
This number is specified in Ert as NUM_CPU.
Value must be strictly greater than 0
Delete Run Path
Type: boolean Default: falseWhether the batch folder for a successful simulation needs to be deleted.
Max Runtime
Default: nullMaximum allowed running time of a forward model. When
set, a job is only allowed to run for max_runtime seconds.
A value of 0 means unlimited runtime.
Value must be greater or equal to 0
Max Memory
Default: nullMaximum allowed memory usage of a forward model. When
set, a job is only allowed to use max_memory of memory.
max_memory may be an integer value, indicating the number of bytes, or a
string consisting of a number followed by a unit. The unit indicates the
multiplier that is applied, and must start with one of these characters:
* b, B: bytes
* k, K: kilobytes (1024 bytes)
* m, M: megabytes (1024**2 bytes)
* g, G: gigabytes (1024**3 bytes)
* t, T: terabytes (1024**4 bytes)
* p, P: petabytes (1024**5 bytes)
Spaces between the number and the unit are ignored, and so are any
characters after the first. For example: 2g, 2G, and 2 GB all resolve
to the same value: 2 gigabytes, equaling 2 * 1024**3 bytes.
If not set, or a set to zero, the allowed amount of memory is unlimited.
Queue System
Default: nullDefines which queue system the everest submits jobs to
LocalQueueOptions
Type: objectNo Additional Properties
Name
Type: const Default: "local"Specific value:
"local"
Max Running
Type: integer Default: 0Value must be greater or equal to 0
Num Cpu
Type: integer Default: 1Value must be greater or equal to 0
Realization Memory
Type: integer Default: 0Value must be greater or equal to 0
Job Script
Type: string Default: "/home/docs/checkouts/readthedocs.org/user_builds/everest/envs/14.4.3/bin/fm_dispatch.py"Submit Sleep
Default: nullValue must be greater or equal to 0
Project Code
Default: nullActivate Script
Default: nullLsfQueueOptions
Type: objectNo Additional Properties
Name
Type: const Default: "lsf"Specific value:
"lsf"
Max Running
Type: integer Default: 0Value must be greater or equal to 0
Num Cpu
Type: integer Default: 1Value must be greater or equal to 0
Realization Memory
Type: integer Default: 0Value must be greater or equal to 0
Job Script
Type: string Default: "/home/docs/checkouts/readthedocs.org/user_builds/everest/envs/14.4.3/bin/fm_dispatch.py"Submit Sleep
Default: nullValue must be greater or equal to 0
Project Code
Default: nullActivate Script
Default: nullBhist Cmd
Default: nullMust be at least 1 characters long
Bjobs Cmd
Default: nullMust be at least 1 characters long
Bkill Cmd
Default: nullMust be at least 1 characters long
Bsub Cmd
Default: nullMust be at least 1 characters long
Exclude Host
Default: nullLsf Queue
Default: nullMust be at least 1 characters long
Lsf Resource
Default: nullSlurmQueueOptions
Type: objectNo Additional Properties
Name
Type: const Default: "slurm"Specific value:
"slurm"
Max Running
Type: integer Default: 0Value must be greater or equal to 0
Num Cpu
Type: integer Default: 1Value must be greater or equal to 0
Realization Memory
Type: integer Default: 0Value must be greater or equal to 0
Job Script
Type: string Default: "/home/docs/checkouts/readthedocs.org/user_builds/everest/envs/14.4.3/bin/fm_dispatch.py"Submit Sleep
Default: nullValue must be greater or equal to 0
Project Code
Default: nullActivate Script
Default: nullSbatch
Type: string Default: "sbatch"Must be at least 1 characters long
Scancel
Type: string Default: "scancel"Must be at least 1 characters long
Scontrol
Type: string Default: "scontrol"Must be at least 1 characters long
Sacct
Type: string Default: "sacct"Must be at least 1 characters long
Squeue
Type: string Default: "squeue"Must be at least 1 characters long
Exclude Host
Type: string Default: ""Include Host
Type: string Default: ""Partition
Default: nullMust be at least 1 characters long
Squeue Timeout
Type: number Default: 2Value must be strictly greater than 0
Max Runtime
Default: nullValue must be greater or equal to 0
TorqueQueueOptions
Type: objectNo Additional Properties
Name
Type: const Default: "torque"Specific value:
"torque"
Max Running
Type: integer Default: 0Value must be greater or equal to 0
Num Cpu
Type: integer Default: 1Value must be greater or equal to 0
Realization Memory
Type: integer Default: 0Value must be greater or equal to 0
Job Script
Type: string Default: "/home/docs/checkouts/readthedocs.org/user_builds/everest/envs/14.4.3/bin/fm_dispatch.py"Submit Sleep
Default: nullValue must be greater or equal to 0
Project Code
Default: nullActivate Script
Default: nullQsub Cmd
Default: nullMust be at least 1 characters long
Qstat Cmd
Default: nullMust be at least 1 characters long
Qdel Cmd
Default: nullMust be at least 1 characters long
Queue
Default: nullMust be at least 1 characters long
Cluster Label
Default: nullMust be at least 1 characters long
Job Prefix
Default: nullMust be at least 1 characters long
Keep Qsub Output
Type: boolean Default: falseResubmit Limit
Type: integer Default: 1 Defines how many times should the queue system retry a forward model.
A forward model may fail for reasons that are not due to the forward model
itself, like a node in the cluster crashing, network issues, etc. Therefore, it
might make sense to resubmit a forward model in case it fails.
resumbit_limit defines the number of times we will resubmit a failing forward model.
If not specified, a default value of 1 will be used.
Value must be greater or equal to 0
Forward Model
Type: arrayList of jobs to run
No Additional ItemsEach item of this array must be:
ForwardModelStepConfig
Type: objectJob
Type: stringName of the forward model step
Results
Default: nullSummaryResults
Type: objectFile Name
Type: stringOutput file produced by the forward model
Type
Type: const Default: "summary"Specific value:
"summary"
Keys
Default: "*"List of keys to include in the result. Defaults to '*' indicating all keys
Specific value:
"*"
No Additional Items
Each item of this array must be:
GenDataResults
Type: objectFile Name
Type: stringOutput file produced by the forward model
Type
Type: const Default: "gen_data"Specific value:
"gen_data"
WorkflowConfig
Type: objectWorkflows to run during optimization
No Additional PropertiesPre Simulation
Type: array of stringList of workflow jobs triggered pre-simulation
No Additional ItemsEach item of this array must be:
Post Simulation
Type: array of stringList of workflow jobs triggered post-simulation
No Additional ItemsEach item of this array must be:
ExportConfig
Type: objectSettings to control the exports of a optimization run by everest.
No Additional PropertiesKeywords
Type: array of stringList of eclipse keywords to be exported.
No Additional Items