Shale Gas Well Production Optimization using Modified RTA Method - Prediction of the Life of a Well

2019 ◽  
Author(s):  
Seunghwan Baek ◽  
I. Yucel Akkutlu ◽  
Baoping Lu ◽  
Shidong Ding ◽  
Wenwu Xia
Keyword(s):  
Shale Gas ◽  
Production Optimization ◽  
Gas Well ◽  
Well Production

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

scholarly journals Shale gas well productivity potential evaluation based on data-driven methods: case study in the WY block

2022 ◽  
Author(s):  
Chaodong Tan ◽  
Hanwen Deng ◽  
Wenrong Song ◽  
Huizhao Niu ◽  
Chunqiu Wang
Keyword(s):  
Shale Gas ◽  
Production Optimization ◽  
Data Driven ◽  
Operating Parameters ◽  
Geological Condition ◽  
Gas Wells ◽  
Gas Well ◽  
Geological Conditions ◽  
Potential Evaluation ◽  
Productivity Potential

AbstractEvaluating the productivity potential of shale gas well before fracturing reformation is imperative due to the complex fracturing mechanism and high operation investment. However, conventional single-factor analysis method has been unable to meet the demand of productivity potential evaluation due to the numerous and intricate influencing factors. In this paper, a data-driven-based approach is proposed based on the data of 282 shale gas wells in WY block. LightGBM is used to conduct feature ranking, K-means is utilized to classify wells and evaluate gas productivity according to geological features and fracturing operating parameters, and production optimization is realized through random forest. The experimental results show that shale gas productivity potential is basically determined by geological condition for the total influence weights of geologic properties take the proportion of 0.64 and that of engineering attributes is 0.36. The difference between each category of well is more obvious when the cluster number of well is four. Meanwhile, those low production wells with good geological conditions but unreasonable fracturing schemes have the greatest optimization space. The model constructed in this paper can classify shale gas wells according to their productivity differences, help providing suggestions for engineers on productivity evaluation and the design of fracturing operating parameters of shale gas well.

Key Factors in Shale Gas Modeling and Simulation

2014 ◽  
Vol 59 (4) ◽  
pp. 987-1004 ◽  
Author(s):  
Łukasz Klimkowski ◽  
Stanisław Nagy
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Production Performance ◽  
Hydraulic Fractures ◽  
Well Performance ◽  
Key Factors ◽  
Gas Well ◽  
Well Production ◽  
Multi Stage ◽  
The Impact

Abstract Multi-stage hydraulic fracturing is the method for unlocking shale gas resources and maximizing horizontal well performance. Modeling the effects of stimulation and fluid flow in a medium with extremely low permeability is significantly different from modeling conventional deposits. Due to the complexity of the subject, a significant number of parameters can affect the production performance. For a better understanding of the specifics of unconventional resources it is necessary to determine the effect of various parameters on the gas production process and identification of parameters of major importance. As a result, it may help in designing more effective way to provide gas resources from shale rocks. Within the framework of this study a sensitivity analysis of the numerical model of shale gas reservoir, built based on the latest solutions used in industrial reservoir simulators, was performed. The impact of different reservoir and hydraulic fractures parameters on a horizontal shale gas well production performance was assessed and key factors were determined.

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