Multiple Realizations

Modern computing clusters make the use of multiple realization work-flows practical from a raw compute perspective. However, running thousands of simulation cases using a traditional simulator, designed originally for serial operation, will result in a large number of output files that need processing, to generate useful results. This processing step is still largely done in an ad hoc manner, often in serial, and hence limits the possible applications as well as failing to utilize the performance available with the cluster.

By implementing a multiple realization engine within 6X, using modern parallel techniques, Ridgeway Kite offers a step change in the usability, reliability and performance of these work-flows without the need of an additional software application.

6X offers the following Multiple Realization capabilities:

  • Sensitivities & Monte Carlo
  • Experimental Design
  • History Matching
  • Development Optimization
  • Optimization under Uncertainty
  • Objective Function Evaluation
  • Probabilistic Multiple Solutions
  • Uncertainty Quantification (Forecasting)

All Multiple Realization keywords in 6X have the prefix MR, e.g. MRSENSITIVITY defines the parameters for a sensitivity run.

 

Sensitivities & Monte Carlo

Study the sensitivity of a model to uncertain variables. The user can define either an individual, combination or Monte Carlo set of runs. There are options to:

  • Create a response surface (of all or a selected set of summary vectors)
  • Output statistics from the response surface
  • Output statistics from the simulation runs
  • Create Tornado, variable sensitivity and distribution plots

6xtornado

6xvarsens

 

Experimental Design

Establish cause-and-effect relationships between the model uncertainties and the simulation results. The following Experimental Designs have been implemented:

  • Full Factorial (L2 & L3)
  • Orthogonal Vectors
  • Central Composite
  • Plackett-Burman
  • Box-Behnken
  • Latin Hypercube

There are options to:

  • Create a response surface (of all (or a selected set) of summary vectors)
  • Output statistics from the response surface
  • Output statistics from the simulation runs
  • Create Tornado, variable sensitivity and distribution plots

6xfrequency

 

History Matching

Match a simulation model to a set of observed pressure, volume or rate data. The user defines a history match objective function and an optimizer. The starting point of a history match is an Experimental Design. 6X has two optimizers: Kriging Genetic Algorithm; and, Estimation of Distribution Algorithm (Equal Width). The optimizers generate a set of additional runs to be performed at each iteration (loop). There are options to:

  • Choose from a standard set of match quality components: volumes, rates, and pressures
  • Select the type of the error terms: signed, absolute, and SQRT
  • Create complex match quality definition by scripting variables and above standard match components
  • Include an Investigation phase (before the Optimizer) where an additional set of runs are generated via an Artificial Neural Network or Kriging
  • Stop and control the Optimizer by setting the maximum number of loops, the number or runs at each loop, the elapsed time and a tolerance
  • Output the results to html, csv and/or Excel files
  • Keep the n best runs overall or per loop

6xhm

 

Development Optimization

Similarly, for Development Optimization, the user defines a development optimization objective function and an optimizer. The development optimization objective function is far more complex than a history match objective function as it includes CAPEX and OPEX costs as well as price details. The starting point of a development optimization is an Experimental Design. 6X has two optimizers: Kriging Genetic Algorithm; and, Estimation of Distribution Algorithm (Equal Width). The optimizers generate a set of additional runs to be performed at each iteration (loop). There are options to:

  • Choose from a standard set of objective function types:
    • DP: discounted/undiscounted production volume
    • RF: Recovery factor
    • NPV: Net Present Value
    • IRR: Internal rate of return
    • MAXEXP: Maximum exposure (maximum negative cashflow)
    • PAYBACK: the payback period
  • Create complex objective functions by scripting variables and the above standard objective functions
  • Perform optimisation under uncertainty, in which, the objective function is calculated from the objective function’s response surface across a set of subsurface models
  • Include an Investigation phase (before the Optimizer) where an additional set of runs are generated via an Artificial Neural Network or Kriging
  • Stop and control the Optimizer by setting the maximum number of loops, the number or runs at each loop, the elapsed time and a tolerance
  • Output the results to html, csv and/or Excel files
  • Keep the n best runs overall or per loop

6xdo

 

Objective Function Evaluation

Evaluate a pre-defined objective function on a set of simulation cases. The user defines either a history match objective function or a development optimization objective function. There are options to:

  • Evaluate the objective function for a set of cases from either a sensitivity or Experimental Design, a set of parameter values entered in the variable definition line or from a file, or from a base case deck with no variables
  • Choose from a history matching or optimisation type objective function (as discussed above)
  • Create a response surface of the objective function
  • Output statistics from the response surface
  • Output the results to html, csv and/or Excel files

 

Probabilistic Multiple Solutions

Performs clustering on cases obtained in a History Match or Development Optimization and selects a set of distinctive cases from the clusters. The selected cases from history matching can be used in optimisation under uncertainty and the selected cases from development optimisation can be used as multiple development scenarios. There are options to:

  • Define the kriging model
  • Define the clustering method, e.g. the number of clusters and what case to choose from each cluster (best case or centroid )
  • Forward simulate the centroid cases
  • Output the results to html, csv and/or Excel files

 

Uncertainty Quantification (Forecasting)

Performs forecasting on a set of cases defined by an Experimental Design or Sensitivity study. From a calculated response surface more runs are generated and the results of these runs are used to improve the response surface until convergence to a tolerance. Distributions and statistics can then be obtained from the resulting response surface. There are options to:

  • Define a response parameter from one or multiple mnemonics, well names and report dates in simulation results
  • Use Linux wild cards for matching mnemonics, group and well names, and dates
  • Select the type of the error terms: signed, absolute, and SQRT
  • Choose from maximum, average or standard deviation aggregation type of error for runs in a loop
  • Stop the iteration by setting the maximum number of loops and a tolerance for error
  • Output cumulative selected percentiles and full probability distribution for response parameters
  • Output the results to html, csv and/or Excel files

6xfore

Keep up to date with Ridgeway Kite

See 6X at UrTec 2018 / 22nd March 2018

Ridgeway Kite will share a booth (#2609) with Nitec and Digital Formation at the Unconventional Resources Technology Conference in Houston, Texas from 23rd to 25th July 2018. Tommy Miller, Chet Ozgen, Bill Baksi & Peter Forster will be in attendance.

Bill Baksi joins RKS / 1st January 2018

Bill Baksi joins RKS as Sales & Marketing Manager, based in Houston. Bill has over 30 years experience in strategic marketing, sales and business development for software products and services in the O&G industry.

RKS at SPE ATCE 2017 / 21st September 2017

Ridgeway Kite will have a booth (#939) at the SPE ATCE 2017 in San Antonio, Texas from October 9th-11th. Tommy Miller and Peter Forster will attend. Come and see our new developments in 6X – especially in modelling Unconventional resources.

Peter Forster joins RKS / 1st August 2017

Peter Forster joins RKS on 1st August 2017 as Technical Support, based in Houston. Peter has over 30 years experience in oil and gas and was most recently the business development manager for reservoir simulation at Schlumberger.