| 摘要 |
Apparatus, computer readable medium, program code, and methods for performing a parameter recalibration on parameters for an electrical submersible pump application model, are provided. An example of a method can include generating a data set containing a plurality of winning data sets or rows containing ESP application model input parameters and associated theoretical output parameters for an ESP application model substantially matching a corresponding set of measured ESP output parameters, through application of a self-organizing map analysis on a substantial number of training data sets. An averaging analysis is performed on the plurality of winning data sets or rows to obtain a set of ESP application model input parameters that can be used to recalibrate the ESP application model. |
| 主权项 |
1. A method of analyzing and recalibrating parameters for an electrical submersible pump (ESP) application model, the method comprising the steps of:
generating, by a processor, a data set comprising a plurality of sets of ESP application model input parameters and associated theoretical output parameters for an ESP application model substantially matching a corresponding set of measured ESP output parameters defining a plurality of winning data sets or rows; and performing an averaging analysis, by the processor, on the plurality of winning data sets or rows to obtain a set of ESP application model input parameters usable to recalibrate the ESP application model, wherein the step of generating a data set comprises performing the following steps for each of a plurality of training data sets:
performing a cluster analysis, by the processor, on one of the plurality of training data sets in order to segregate the training data set into a plurality of clusters, each cluster containing a plurality of data rows from the training data set, each data row in the same one of the plurality of clusters having at least similar characteristics;mapping, by the processor, the set of measured ESP output parameters to one of the plurality of clusters responsive to a similarity analysis performed on the theoretical output parameter within each cluster to identify a winning cluster, the winning cluster being the one cluster of at least several data rows collectively having a set of output parameters most closely matching the corresponding set of measured ESP output parameters; andidentifying, by the processor, a winning data row within the winning cluster that yields outputs parameters which are the most closest to the corresponding set of measured ESP output parameters to define one of the plurality of winning data rows. |
