Neural-Net Identification of Flow Regime With Band Spectra of Flow-Generated Sound

Autor:	Alix Thomas, Alex van der Spek
Rok vydání:	1999
Předmět:	Identification (information) geography Fuel Technology geography.geographical_feature_category Artificial neural network Flow (mathematics) Computer science Speech recognition Acoustics Energy Engineering and Power Technology Geology Sound (geography) Spectral line
Zdroj:	SPE Reservoir Evaluation & Engineering. 2:489-498
ISSN:	1930-0212 1094-6470
DOI:	10.2118/59067-pa
Popis:	Summary Multiphase production log interpretation requires that the flow regime along hole in the wellbore is known. Flow regime is the cased-hole analog of lithology. Knowledge of the flow regime will help to interpret tool signals, will help to evaluate the flow rate on a per phase basis, and will reduce post-processing load. The flow regime can be classified correctly by a neural net in up to 87% of all cases using 1/3 octave band spectra of flow-generated sound plus the pipe inclination angle. Without the inclination an 88% correct classification can be achieved. A neural net trained on commercially available tool data (noise cuts) appears to be too sensitive to the wellbore inclination. Hence, application of automated neural net interpretation of noise logs requires a new generation of noise logging tools.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8182c9e436f56f4b1ca5fb30d40c9abf https://doi.org/10.2118/59067-pa Zobrazit plný text záznamu