Applicability of Flow Assurance Solutions in Russian Oil & Gas Developments: System Design, Operability and Production Optimization

2006 ◽  
Author(s):  
Leonid A. Dykhno ◽  
Ajay P. Mehta ◽  
Alexey Vladimirovich Moiseyenkov
Keyword(s):  
System Design ◽  
Production Optimization ◽  
Flow Assurance ◽  
Oil Gas

Intelligent Production Monitoring with Continuous Deep Learning Models

2021 ◽  
Author(s):  
Anton Gryzlov ◽  
Sergey Safonov ◽  
Muhammad Arsalan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Multiphase Flow ◽  
Pressure Sensors ◽  
Production Optimization ◽  
Learning Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Production Monitoring ◽  
Oil Gas

Abstract Monitoring of production rates is essential for reservoir management, history matching, and production optimization. Traditionally, such information is provided by multiphase flow meters or test separators. The growth of the availability of data, combined with the rapid development of computational resources, enabled the inception of digital techniques, which estimate oil, gas, and water rates indirectly. This paper discusses the application of continuous deep learning models, capable of reproducing multiphase flow dynamics for production monitoring purposes. This technique combines time evolution properties of a dynamical system and the ability of neural networks to quantitively describe poorly understood multiphase phenomena and can be considered as a hybrid solution between data-driven and mechanistic approaches. The continuous latent ordinary differential equation (Latent ODE) approach is compared to other known machine learning methods, such as linear regression, ensemble-based model, and recurrent neural network. In this work, the application of Latent ordinary differential equations for the problem of multiphase flow rate estimation is introduced. The considered example refers to a scenario, where the topside oil, gas, and water flow rates are estimated using the data from several downhole pressure sensors. The predictive capabilities of different types of machine learning and deep learning instruments are explored using simulated production data from a multiphase flow simulator. The results demonstrate the satisfactory performance of the continuous deep learning models in comparison to other machine learning methods in terms of accuracy, where the normalized root mean squared error (RMSE) and mean absolute error (MAE) of prediction below 5% were achieved. While LODE demonstrates the significant time required to train the model, it outperforms other methods for irregularly sampled time-series, which makes it especially attractive to forecast values of multiphase rates.

Development of Tightly-Coupled Integrated Asset Model For Use in Production Optimization, De-Bottlenecking and Flow Assurance of Multiple Gas Reservoirs

2015 ◽  
Author(s):  
Seyamak Gholam Zadeh ◽  
Diyar Barzanji ◽  
Nigel Brock ◽  
Budour Omar Saeed Ateeq ◽  
Gerard Bloch ◽  
...  
Keyword(s):  
Production Optimization ◽  
Flow Assurance ◽  
Gas Reservoirs ◽  
Tightly Coupled

Short-term steady-state production optimization of offshore oil platforms: wells with dual completion (gas-lift and ESP) and flow assurance

Top ◽  
2021 ◽  
Author(s):  
Eduardo Rauh Müller ◽  
Eduardo Camponogara ◽  
Laio Oriel Seman ◽  
Eduardo Otte Hülse ◽  
Bruno Ferreira Vieira ◽  
...  
Keyword(s):  
Steady State ◽  
Production Optimization ◽  
Flow Assurance ◽  
Short Term ◽  
Oil Platforms ◽  
Offshore Oil Platforms ◽  
State Production ◽  
Offshore Oil ◽  
Gas Lift
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