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To effectively match them to real fabrics, we introduce a new appearance matching framework to determine their parameters.
In such situations, numerical models are better suited, using a history matching framework to assist with reservoir and fluid characterization.
The estimated observation error standard deviations are also within expected levels (between 5% and 10%), significantly contributing to the robustness of the proposed EM history matching framework.
We have developed an integrated reservoir history matching framework using ensemble based techniques incorporating seismic data for enhancing reservoir characterization and improving history matches.
We present a multi-data ensemble Kalman filter-based history matching framework for the simultaneous incorporation of various reservoir data such as seismic, electromagnetics, gravimetry and InSAR for best possible characterization of the reservoir formation.
In this paper, an optimal model matching framework, where both the feedback and feedforward controllers form the decision variables, is presented which facilitates achieving better performance in terms of the resolution bandwidth and robustness to modeling uncertainties in the closed-loop device.
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The narrative was reviewed several times (constant comparative technique) to identify all instances of the coding framework, and items not matching the framework, to determine whether and how to expand or merge thematic codes (axial coding).
In this section, we introduce our proposed partner-matching framework.
The challenges of designing a partner-matching framework are as follows: 1.
This motivates us to design a partner-matching framework for users to find good partners.
In this paper, we proposed a partner-matching framework to address this issue.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com