Water Coning Control in Gas Well with Strong Water Drive Mechanism Reservoir

Analysis of chemical volume and composition to overcome liquid loading in gas well

E3S Web of Conferences ◽

10.1051/e3sconf/20186703009 ◽

2018 ◽

Vol 67 ◽

pp. 03009

Author(s):

Abdul Wahid ◽

Muhamad Taufiq Hidayat

Keyword(s):

Natural Gas ◽

Water Content ◽

Production Rate ◽

Gas Production ◽

Reservoir Pressure ◽

Liquid Loading ◽

Critical Rate ◽

Gas Well ◽

Natural Gas Production ◽

Drive Mechanism

Many problems often occur in producing natural gas from well. Due to the existence of water content in natural gas or water drive mechanism, liquid (especially water) is also produced from gas well, following natural gas production. When gas critical rate is higher than gas production rate due to reservoir pressure decline, it will cause liquid accumulation in the bottom of well, avoiding natural gas to be well lifted from well bottom to surface. It is liquid loading. Chemical injection of 0.4 liquid that consists of ethoxy sulphate, alkane sulphonate, and petroleum sulphonate is effective to overcome liquid loading in natural gas well thus causing an increase in natural gas production by 57%.

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189279 ◽

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

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