PERPUSTAKAAN BIG

  • Beranda
  • Informasi
  • Berita
  • Bantuan
  • Area Pustakawan
  • Area Anggota
  • Pilih Bahasa :
    Bahasa Arab Bahasa Bengal Bahasa Brazil Portugis Bahasa Inggris Bahasa Spanyol Bahasa Jerman Bahasa Indonesia Bahasa Jepang Bahasa Melayu Bahasa Persia Bahasa Rusia Bahasa Thailand Bahasa Turki Bahasa Urdu
Image of Optimization of shale gas fracturing parameters based on artificial intelligence algorithm

Text

Optimization of shale gas fracturing parameters based on artificial intelligence algorithm

Shihao Qian - Nama Orang; Zhenzhen Dong - Nama Orang; Qianqian Shi - Nama Orang; Wei Guo - Nama Orang; Xiaowei Zhang - Nama Orang; Zhaoxia Liu - Nama Orang; Lingjun Wang - Nama Orang; Lei Wu - Nama Orang; Tianyang Zhang - Nama Orang; Weirong Li - Nama Orang;

Resource-rich shale gas plays a pivotal role in new energy types. The key to scientifically and efficiently developing shale gas fields is to clarify the main factors that affect the production of shale gas wells. In this paper, according to the shale gas reservoir characteristic of the Fuling marine Longmaxi Formation, a single-well geological model was established using the reservoir numerical simulation software CMG. Then, 10,000 different reservoir models were randomly generated for different formation physical parameters, completion parameters, and fracturing parameters using the Monte Carlo method, and these 10,000 models were simulated numerically. The machine learning model uses a dataset of 10,000 different geological, completion, and fracturing parameters as input and 10,000 production curves as output. Multiple machine learning regression methods were used to train and test the dataset, and the optimal method (GBDT algorithm) was selected, and the accuracy R2 of the test set of the GBDT prediction model is 0.96. A fracturing parameter optimization workflow was constructed by combining a production prediction model with a particle swarm optimizer (PSO). The process can quickly optimize the fracturing parameters and predict the production for each time by targeting the cumulative gas production under different geological conditions. The optimized parameters are Fracture Spacing, Fracture Width, Intrinsic Permeability, Fracture Half-length, Langmuir Pressure, and Langmuir Volume. The initial predicted cumulative gas production was 4.59 × 108 m3, which was optimized to 4.90 × 108 m3. The proposed PSO-GBDT proxy model can instantly predict the production of shale gas wells with considerable accuracy, reliability, and efficiency, which is a vital tool for optimizing fracture design. This investigation provides a solid foundation for predicting the production of unconventional gas reservoirs and for parameter optimization.


Ketersediaan
306551Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
Penerbit
Beijing : KeAi Communications Co. Ltd.., 2023
Deskripsi Fisik
16 hlm PDF, 13.443 KB
Bahasa
Inggris
ISBN/ISSN
2666-5441
Klasifikasi
551
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.4, December 2023
Subjek
Prediction
Shale gas
Parameter optimization
GBDT
PSO
Info Detail Spesifik
-
Pernyataan Tanggungjawab
-
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Optimization of shale gas fracturing parameters based on artificial intelligence algorithm
    Resource-rich shale gas plays a pivotal role in new energy types. The key to scientifically and efficiently developing shale gas fields is to clarify the main factors that affect the production of shale gas wells. In this paper, according to the shale gas reservoir characteristic of the Fuling marine Longmaxi Formation, a single-well geological model was established using the reservoir numerical simulation software CMG. Then, 10,000 different reservoir models were randomly generated for different formation physical parameters, completion parameters, and fracturing parameters using the Monte Carlo method, and these 10,000 models were simulated numerically. The machine learning model uses a dataset of 10,000 different geological, completion, and fracturing parameters as input and 10,000 production curves as output. Multiple machine learning regression methods were used to train and test the dataset, and the optimal method (GBDT algorithm) was selected, and the accuracy R2 of the test set of the GBDT prediction model is 0.96. A fracturing parameter optimization workflow was constructed by combining a production prediction model with a particle swarm optimizer (PSO). The process can quickly optimize the fracturing parameters and predict the production for each time by targeting the cumulative gas production under different geological conditions. The optimized parameters are Fracture Spacing, Fracture Width, Intrinsic Permeability, Fracture Half-length, Langmuir Pressure, and Langmuir Volume. The initial predicted cumulative gas production was 4.59 × 108 m3, which was optimized to 4.90 × 108 m3. The proposed PSO-GBDT proxy model can instantly predict the production of shale gas wells with considerable accuracy, reliability, and efficiency, which is a vital tool for optimizing fracture design. This investigation provides a solid foundation for predicting the production of unconventional gas reservoirs and for parameter optimization.
Komentar

Anda harus masuk sebelum memberikan komentar

PERPUSTAKAAN BIG
  • Informasi
  • Layanan
  • Pustakawan
  • Area Anggota

Tentang Kami

Perpustakaan Badan Informasi Geospasial (BIG) adalah sebuah perpustakaan yang berada di bawah Badan Informasi Geospasial Indonesia. Perpustakaan ini memiliki koleksi yang berkaitan dengan informasi geospasial, termasuk peta, data geospasial, dan literatur terkait. Selengkapnya

Cari

masukkan satu atau lebih kata kunci dari judul, pengarang, atau subjek

Donasi untuk SLiMS Kontribusi untuk SLiMS?

© 2025 — Senayan Developer Community

Ditenagai oleh SLiMS
Pilih subjek yang menarik bagi Anda
  • Batas Wilayah
  • Ekologi
  • Fotogrametri
  • Geografi
  • Geologi
  • GIS
  • Ilmu Tanah
  • Kartografi
  • Manajemen Bencana
  • Oceanografi
  • Penginderaan Jauh
  • Peta
Icons made by Freepik from www.flaticon.com
Pencarian Spesifik