A model-based approach for process monitoring in oil production industry

2016 ◽  
Author(s):  
Edurne Irisarri ◽  
Marcelo V. Garcia ◽  
Federico Perez ◽  
Elisabet Estevez ◽  
Marga Marcos
Keyword(s):  
Process Monitoring ◽  
Oil Production ◽  
Model Based ◽  
Production Industry

Prediction and Optimization of WAG Flooding by Using LSTM Neural Network Model in Middle East Carbonate Reservoir

2021 ◽  
Author(s):  
Ruijie Huang ◽  
Chenji Wei ◽  
Baozhu Li ◽  
Jian Yang ◽  
Suwei Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Numerical Simulation ◽  
Short Term Memory ◽  
Oil Production ◽  
Simulation Method ◽  
Analysis Data ◽  
Carbonate Reservoir ◽  
Parameters Optimization ◽  
Model Based

Abstract Production prediction continues to play an increasingly significant role in reservoir development adjustment and optimization, especially in water-alternating-gas (WAG) flooding. As artificial intelligence continues to develop, data-driven machine learning method can establish a robust model based on massive data to clarify development risks and challenges, predict development dynamic characteristics in advance. This study gathers over 15 years actual data from targeted carbonate reservoir and establishes a robust Long Short-Term Memory (LSTM) neural network prediction model based on correlation analysis, data cleaning, feature variables selection, hyper-parameters optimization and model evaluation to forecast oil production, gas-oil ratio (GOR), and water cut (WC) of WAG flooding. In comparison to traditional reservoir numerical simulation (RNS), LSTM neural networks have a huge advantage in terms of computational efficiency and prediction accuracy. The calculation time of LSTM method is 864% less than reservoir numerical simulation method, while prediction error of LSTM method is 261% less than RNS method. We classify producers into three types based on the prediction results and propose optimization measures aimed at the risks and challenges they faced. Field implementation indicates promising outcome with better reservoir support, lower GOR, lower WC, and stabler oil production. This study provides a novel direction for application of artificial intelligence in WAG flooding development and optimization.

Model-based process monitoring using robust generalized linear models

2005 ◽  
Vol 43 (7) ◽  
pp. 1337-1354 ◽  
Author(s):  
D. Jearkpaporn ◽  
D. C. Montgomery * ◽  
G. C. Runger ◽  
C. M. Borror
Keyword(s):  
Process Monitoring ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Model Based

2.2.2 Complexity and change in the subsea oil production industry

2004 ◽  
Vol 14 (1) ◽  
pp. 257-272
Author(s):  
Luca Abele Piciaccia ◽  
Tore Faanes ◽  
Hans Jørgen Lindland
Keyword(s):  
Oil Production ◽  
Production Industry

scholarly journals A Model to Manage Crude Palm Oil Production System

2019 ◽  
Vol 255 ◽  
pp. 02001 ◽  
Author(s):  
Inyang John ◽  
Andrew-Munot Magdalene ◽  
Syed Shazali Syed Tarmizi ◽  
Johnathan Tanjong Shirley
Keyword(s):  
Simulation Model ◽  
Production Process ◽  
Conceptual Model ◽  
Palm Oil ◽  
Production System ◽  
Oil Production ◽  
Future Research ◽  
Crude Palm Oil ◽  
Model Based ◽  
The Future

This paper reviews key production process for crude palm oil and highlights factors that highly influence the production of crude palm oil. This paper proposes a generic conceptual model for crude palm production process considering these factors. The conceptual model could be modified to consider other factors not included in this paper. The future research would be to construct a simulation model based on the conceptual model proposed in this paper and analyse the effect of these factors on the performance of crude palm oil production system.

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