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Applications of Artificial Intelligence in Oil and Gas Development

  • Hong Li
  • Haiyang YuEmail author
  • Nai Cao
  • He Tian
  • Shiqing Cheng
Original Paper

Abstract

Artificial intelligence has been back on the stage of research works in all the walks in recently years, the sharply increase of AI-based work have shown its potential to be a future direction for almost all disciplines. In oil and gas industry, AI technology is also doubtlessly a new shining star that draws attention from researchers devoted themselves into it. In order to dig up more about the applications of artificial intelligence in oilfield development for a hint of the future trend of this exciting technology in oil and gas industry, literature investigation of a large amount of AI-based work reported has been conducted in this work. Based on the investigation, the application of AI in important issues in oilfield development including oilfield production dynamic prediction, developing plan optimization, residual oil identification, fracture identification, and enhanced oil recovery are specifically investigated and summarized, the backs and cons of existing AI algorithms has been compared. Based on the analysis and discussion, current situation of the application of AI in oilfield development is concluded, and suggestions and potential directions of future work AI application in oil and gas developing are provided.

Abbreviations

ANFIS

Adaptive network-based fuzzy inference system

ANN

Artificial neural network

APSO

Adaptive particle swarm optimization

BDA

Big data analytics

BP

Back propagation

CNN

Convolutional neural network

DM

Data mining

FCM

Fuzzy clustering method

GA

Genetic algorithm

GNN

Graph neural network

HIS

Hybrid intelligent system

Iot

Internet of things technology

IPSO

Improved particle swami optimization

LSSVM

Least squares support vector machine

MAPE

Mean absolute percent error

ML

Machine learning

MLPNN

Multi-layer perceptron neural network

MSE

Mean squared error

NARX

Nonlinear auto regressive model with eXogenous

PCA

Principal component analysis

PNN

Polynomial neural network

PSO

Particle swarm optimization

QPSO

Quantum particle swarm optimization

RMSE

Root mean squared error

SD

Standard deviation

SOM

Self-organizing maps

SRM

Surrogate regulation model

SVM

Support vector machine

WOB

Weight on bit

Notes

Acknowledgements

The authors are grateful for financial support from the National Natural Science Foundation of China (51874317) and the National Science and Technology Major Projects of China (Grant Nos. 2016ZX05037003).

Compliance with Ethical Standards

Conflict of interest

All authors declare no conflict of interest.

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Copyright information

© CIMNE, Barcelona, Spain 2020

Authors and Affiliations

  • Hong Li
    • 1
  • Haiyang Yu
    • 1
    Email author
  • Nai Cao
    • 1
  • He Tian
    • 1
  • Shiqing Cheng
    • 1
  1. 1.State Key Laboratory of Petroleum Resources and ProspectingChina University of PetroleumBeijingChina

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