A framework for history matching - StreamSim Technologies, Inc.

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Questions & Answers Technology “How does Mepo check the quality of the history match?” The quality of the HM is simply quantified by the difference between the measured and simulated values. All types of measurement values such as pressures (BHP, RFT), production rates, WCT and GOR, can be included. The following definition of the objective function is used in Mepo: Q denotes the quality, or objective function i references an objective element, e.g. the oil rate at a particular well k references the time step at which an observed value exists w is the weight of the objective element i at time step k i,k defines observed value of the objective element i at time step k oi,k ci,k σi defines calculated value of the objective element i at time step k is the standard deviation (the measurement error) of the objective element i n,m refers to all model parameters x model parameter and mean value C covariance matrix “How does Mepo assess the uncertainty in the production forecasts?” Uncertainties are assessed by identifying several simulation models that belong to different parts of the search space, all having acceptable matches. In addition, experimental design is used to assure a large initial variation of parameter levels. “Which optimisation methods are used in Mepo?“ A pool of optimisation algorithms is implemented to assist the engineer in the HM study. An evolution strategy is used for global optimisation. Local search methods like a Simplex algorithm and a gradient method are used for fine tuning applications or small size problems. A Bayesian approach is included to identify parameter sets with a good potential to further improve the match. An evolution strategy belongs to the class of evolutionary algorithms, which use only the objective function value to determine new search steps, and do not require any gradient information from the optimisation problem. They can therefore be used in cases where gradient information is not available, and where traditional algorithms fail because of significant non-linearities or discontinuities in the search space. Evolutionary algorithms have proven to be robust and easy to adopt to different engineering problems. The nature of evolutionary algorithms is to use parallel structures in generating parent-to-child sequences. This principal feature can be easily transferred to parallel structures of an optimisation program allowing parallel computing to be used. “Which parameters can be varied in the history matching study?” In principle, there are no limits to which parameters one can alter, both discrete and continuous parameters can be varied. Fault locations, relative permeability curves, stochastic realisations, PVT data and grids are examples of discrete parameters often used in an HM study. Uncertainties in continuous parameters like permeability, porosity, aquifer size and productivity index are also common. A generalised pre-processing concept allows the inclusion of basically every simulation parameter as a design or optimisation parameter.
Questions & Answers Technology Hardware & Software “Which simulator output parameters can be optimised?” Most often the optimisation is based on pressure values (BHP, RFT), production rates (OPR, GPR, etc.) and ratios (GOR, WCT). Mepo includes an advanced post-processor which reads and interprets all standard Eclipse output files (summary files, user files, RFT files, etc.). All parameters accessible through these files can be included in the objective formulation. Customised post- processing scripts can be easily linked to Mepo to include other parameters into the objective formulation (e.g. NPV, gas volume in selected regions). “Is there any limitation to the size of the simulation model?” In principle - no, the benefits of Mepo are scalable and speed depends on the number of CPUs and available licenses. Regardless of the number of licenses, the uncertainties in the simulation model can be assessed and Mepo will assist the engineer towards a match. To minimise the impact on your resources, Scandpower Petroleum Technology can offer expert advice and deliver pre-installed clusters of computers with the necessary operating system and simulation software (e.g. Eclipse and/or 3DSL). “Can I weight measurements or observed values?” Yes, measured values and time periods can be weighted. To examine which parameters or time periods to weight, the data can be loaded into Mepo and analysed by visualising weight factors together with the measured data. Visualisation of weight factors and measured data. “Which reservoir simulators is Mepo linked to?” Any simulator can be launched by Mepo. Effective definition of the objective function requires advanced post-processing tools to read and interpret the simulator output. Postprocessing tools are available through Mepo for the Eclipse simulator output. Any simulator with an Eclipse compatible output format can be used on the same level as Eclipse, e.g. 3DSL, Frontsim, Eclipse 300, etc. Customised post-processing scripts can easily be linked to Mepo to read output data from any simulator to be used in the Mepo workflow. “What kind of hardware and software is required?” Mepo is scalable, and does not require a minimum or maximum number of processors. Mepo can run on a stand-alone one-processor machine or on a multi-processor machine in a network. Mepo is best utilised through parallel optimisation in a cluster of computers/CPUs, where you have access to several simulator licenses. Eclipse ® is a trademark of Schlumberger Frontsim ® is a trademark of Schlumberger 3DSL ® is a trademark of Streamsim Technologies
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