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Data-driven approaches for time series prediction of daily production in the Sulige tight gas field, China

The Sulige tight gas field is presently the largest gas field in China. Owing to the ultralow permeability and strong heterogeneity of the reservoirs in Sulige, the number of production wells has exceeded 3,000, keeping the stable gas supply in the decade. Thus, the daily production prediction of gas wells is significant for monitoring production and for implementing and evaluating stimulation measures. Therefore, on the basis of the three data-driven time series approaches, the daily production of 1692 wells over 10 years was mining for the daily production prediction of wells in Sulige. The jointed deep long short-term memory and fully connected neural network (DLSTM-FNN) model was proposed by introducing the recurrent neural network's sequential expression ability and was compared with random forest (RF) and support vector regression (SVR). After the daily production predictions of thousands of wells in Sulige, the proposed DLSTM-FNN model significantly improved the time series prediction accuracy and efficiency in the short training samples and had strong availability and practicability in the Sulige tight gas field.

Ketersediaan
255551Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
Penerbit
Beijing : KeAi Communications Co. Ltd..,
Deskripsi Fisik
6 hlm PDF, 1.029 KB
Bahasa
Inggris
ISBN/ISSN
2666-5441
Klasifikasi
551
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.2, December 2021
Subjek
Random forest
Support vector machine
Prediction of production time series
Long short-term memory neural network
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Data-driven approaches for time series prediction of daily production in the Sulige tight gas field, China
    The Sulige tight gas field is presently the largest gas field in China. Owing to the ultralow permeability and strong heterogeneity of the reservoirs in Sulige, the number of production wells has exceeded 3,000, keeping the stable gas supply in the decade. Thus, the daily production prediction of gas wells is significant for monitoring production and for implementing and evaluating stimulation measures. Therefore, on the basis of the three data-driven time series approaches, the daily production of 1692 wells over 10 years was mining for the daily production prediction of wells in Sulige. The jointed deep long short-term memory and fully connected neural network (DLSTM-FNN) model was proposed by introducing the recurrent neural network's sequential expression ability and was compared with random forest (RF) and support vector regression (SVR). After the daily production predictions of thousands of wells in Sulige, the proposed DLSTM-FNN model significantly improved the time series prediction accuracy and efficiency in the short training samples and had strong availability and practicability in the Sulige tight gas field.
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