A method, comprising: receiving a dataset (208.1-208.4) of well measurements collected from a plurality of wells in a field (100, 200, 300), by wire-line or in-well collection techniques (106.3); using at least one processing unit: extracting from the measurements for each of the plurality of wells corresponding collected samples of the well; learning a self-organized map using the collected samples; generating a synthetic dataset by computing a plurality of synthetic samples using the self-organized map; generating a plurality of models, each model generated from one of the synthetic samples of the synthetic dataset; and propagating the plurality of models generated from the synthetic dataset to the plurality of wells, wherein propagating the plurality of models includes, for each of the plurality of collected samples in the received dataset: computing a distance between the collected sample and each generated synthetic sample; once all distances have been calculated, determining the synthetic sample with the minimum distance to the collected sample; and assigning to the well of the collected sample, the model computed with the determined minimum distance synthetic sample, receiving a dataset (208.1-208.4) of well measurements collected from a plurality of wells in a field (100, 200, 300), by wire-line or in-well collection techniques (106.3); using at least one processing unit: extracting from the measurements for each of the plurality of wells corresponding collected samples of the well; learning a self-organized map using the collected samples; generating a synthetic dataset by computing a plurality of synthetic samples using the self-organized map; generating a plurality of models, each model generated from one of the synthetic samples of the synthetic dataset; and propagating the plurality of models generated from the synthetic dataset to the plurality of wells, wherein propagating the plurality of models includes, for each of the plurality of collected samples in the received dataset: computing a distance between the collected sample and each generated synthetic sample; once all distances have been calculated, determining the synthetic sample with the minimum distance to the collected sample; and assigning to the well of the collected sample, the model computed with the determined minimum distance synthetic sample, extracting from the measurements for each of the plurality of wells corresponding collected samples of the well; learning a self-organized map using the collected samples; generating a synthetic dataset by computing a plurality of synthetic samples using the self-organized map; generating a plurality of models, each model generated from one of the synthetic samples of the synthetic dataset; and propagating the plurality of models generated from the synthetic dataset to the plurality of wells, wherein propagating the plurality of models includes, for each of the plurality of collected samples in the received dataset: computing a distance between the collected sample and each generated synthetic sample; once all distances have been calculated, determining the synthetic sample with the minimum distance to the collected sample; and assigning to the well of the collected sample, the model computed with the determined minimum distance synthetic sample, computing a distance between the collected sample and each generated synthetic sample; once all distances have been calculated, determining the synthetic sample with the minimum distance to the collected sample; and assigning to the well of the collected sample, the model computed with the determined minimum distance synthetic sample,