A new guideline for gas well production optimization using well testing data

2018 ◽  
Author(s):  
Abu Dawud H.
Keyword(s):  
Production Optimization ◽  
Well Testing ◽  
Gas Well ◽  
Well Production ◽  
Testing Data

scholarly journals Determine the Inflow Performance Relationship of Water Producing Gas Well Using Multiobjective Optimization Method

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiao-Hua Tan ◽  
Jian-Yi Liu ◽  
Jia-Hui Zhao ◽  
Xiao-Ping Li ◽  
Guang-Dong Zhang ◽  
...  
Keyword(s):  
Multiobjective Optimization ◽  
Mass Conservation ◽  
Optimization Method ◽  
Well Testing ◽  
Gas Reservoirs ◽  
Two Phase ◽  
Gas Well ◽  
Well Production ◽  
Single Well ◽  
Inflow Performance

During the development of water drive gas reservoirs, the phenomena of gas escaping from water and water separating out from gas will change the seepage characteristics of formation fluid. Therefore, the traditional gas-water two-phase inflow performance relationship (IPR) models are not suitable for calculating the water producing gas well inflow performance relationship in water drive gas reservoirs. Based on the basic theory of fluid mechanics in porous medium, using the principle of mass conservation, and considering the process of dissolution and volatilization of gas and water formation, this paper establishes a new mathematical model of gas-water two-phase flow. Multiobjective optimization method is used to automatically match the sample well production data in water drive gas reservoirs and then we can achieve the sample well’s productivity equation, relative permeability curve, water influx intensity, and single well controlled reserves. In addition, the influence of different production gas water ratios (GWR) and gas-soluble water coefficients on absolute open flow rate (qAOF) is discussed. This method remedied the limitation of well testing on site and was considered to be a new way to analyze the production behaviors in water producing gas well.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Sign in / Sign up

Export Citation Format

Share Document