Cristin-resultat-ID: 1760215
Sist endret: 19. november 2020, 14:28
NVI-rapporteringsår: 2019
Resultat
Vitenskapelig artikkel
2019

Kick Detection and Influx Size Estimation during Offshore Drilling Operations using Deep Learning

Bidragsytere:
  • Andreas Kvalbein Fjetland
  • Jing Zhou
  • Kuruge Darshana Abeyrathna og
  • Jan Einar Gravdal

Tidsskrift

IEEE Conference on Industrial Electronics and Applications
ISSN 2158-2297
e-ISSN 2158-2297
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2019
Open Access

Importkilder

Scopus-ID: 2-s2.0-85073053224

Beskrivelse Beskrivelse

Tittel

Kick Detection and Influx Size Estimation during Offshore Drilling Operations using Deep Learning

Sammendrag

An uncontrolled or unobserved influx or kick during drilling has the potential to induce a well blowout, one of the most harmful incidences during drilling both in regards to economic and environmental cost. Since kicks during drilling are serious risks, it is important to improve kick and loss detection performance and capabilities and to develop automatic flux detection methodology. There are clear patterns during a influx incident. However, due to complex processes and sparse instrumentation it is difficult to predict the behaviour of kicks or losses based on sensor data combined with physical models alone. Emerging technologies within Deep Learning are however quite adapt at picking up on, and quantifying, subtle patterns in time series given enough data. In this paper, a new model is developed using Long Short-Term Memory (LSTM), a Recurrent Deep Neural Network, for kick detection and influx size estimation during drilling operations. The proposed detection methodology is based on simulated drilling dataand involves detecting and quantifying the influx of fluids between fractured formations and the well bore. The results show that the proposed methods are effective both to detect and estimate the influx size during drilling operations, so that corrective actions can be taken before any major problem occurs.

Bidragsytere

Andreas Kvalbein Fjetland

  • Tilknyttet:
    Forfatter
    ved Institutt for ingeniørvitenskap ved Universitetet i Agder

Jing Zhou

  • Tilknyttet:
    Forfatter
    ved Institutt for ingeniørvitenskap ved Universitetet i Agder
Aktiv cristin-person

Darshana Abeyrathna

Bidragsyterens navn vises på dette resultatet som Kuruge Darshana Abeyrathna
  • Tilknyttet:
    Forfatter
    ved Institutt for informasjons- og kommunikasjonsteknologi ved Universitetet i Agder
Aktiv cristin-person

Jan Einar Gravdal

  • Tilknyttet:
    Forfatter
    ved NORCE Energi og teknologi ved NORCE Norwegian Research Centre AS
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