Consiglio Nazionale delle Ricerche

Tipo di prodottoContributo in atti di convegno
TitoloBit bounce detection using neural networks
Anno di pubblicazione2004
Formato
  • Elettronico
  • Cartaceo
Autore/iG. Bernasconi, V. Rampa, M. Vassallo
Affiliazioni autoriDipartimento di Elettronica e Informazione, Politecnico di Milano; Istituto di Elettronica e d i Ingegneria dell'Informazione e delle Telecomunicazioni, CNR
Autori CNR e affiliazioni
  • VITTORIO RAMPA
Lingua/e
  • inglese
AbstractReal-time monitoring of bottom-hole assembly (BHA) conditions and drill-bit dynamic behavior is a critical factor in improving drilling efficiency. In fact, during rotary perforation, bit and drill-string dynamics can produce anomalous drilling conditions such as bit whirl, stick slip and bit bounce, just to name a few. These dangerous drilling conditions reduce the bit penetration rate and the safety of the operations. Measurement-while-drilling (MWD) may be employed to monitor BHA conditions and prevent unsafe operations, by detecting anomalous drilling conditions directly from the acquired signals, and to support real-time decisions. This paper presents a neural network system for real-time detection of the bit bounce phenomenon. The design of the neural network and its validation test are performed exploiting drill-bit data recorded downhole.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da358
Pagine a361
Pagine totali4
Rivista-
Numero volume della rivista-
Serie/CollanaExpanded abstracts with biographies
Attiva dal 1983 al 2004
Editore: Society of Exploration Geophysicists, - Tulsa, OK
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1052-3812
Titolo chiave: Expanded abstracts with biographies
Titolo proprio: Expanded abstracts with biographies :
Titolo abbreviato: Expand. abstr. biogr.
Titoli alternativi:
  • Expanded abstracts of the technical program with authors' biographies
  • SEG Annual Meeting expanded technical program abstracts with biographies
Titolo del volumeProceedings of the 74th SEG Annual Meeting
Numero volume della serie/collana23
Curatore/i del volume-
ISBN-
DOI10.1190/1.1839727
Editore
  • Society of Exploration Geophysicists, Tulsa, OK (Stati Uniti d'America)
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)-
Parole chiaveReal-time monitoring, Measurement-while-drilling, Bottom-hole assembly, Bit bounce detection
Link (URL, URI)http://library.seg.org/getabs/servlet/GetabsServlet?prog=normal&id=SEGEAB000023000001000358000001&idtype=cvips&gifs=yes&ref=no
Titolo convegno/congresso74th SEG Annual Meeting
Luogo convegno/congressoDenver, USA
Data/e convegno/congresso10-15/10/2004
RilevanzaInternazionale
RelazioneContributo
Titolo parallelo-
Note/Altre informazioni-
Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
Moduli CNR
    Progetti Europei-
    Allegati
    • Articolo pubblicato
      Descrizione: Bit bounce detection using neural networks

    Dati storici
    I dati storici non sono modificabili, sono stati ereditati da altri sistemi (es. Gestione Istituti, PUMA, ...) e hanno solo valore storico.
    Area disciplinareInformation Technology & Communications Systems
    Area valutazione CIVRIngegneria industriale e informatica
    Descrizione sintetica del prodottoReal-time monitoring of bottom-hole assembly (BHA) conditions and drill-bit dynamic behavior is a critical factor in improving drilling efficiency. In fact, during rotary perforation, bit and drill-string dynamics can produce anomalous drilling conditions such as bit whirl, stick slip and bit bounce, just to name a few. These dangerous drilling conditions reduce the bit penetration rate and the safety of the operations. Measurement-while-drilling (MWD) may be employed to monitor BHA conditions and prevent unsafe operations, by detecting anomalous drilling conditions directly from the acquired signals, and to support real-time decisions. This paper presents a neural network system for real-time detection of the bit bounce phenomenon. The design of the neural network and its validation test are performed exploiting drill-bit data recorded downhole.