Shale Gas Production and Environmental Concerns

2016 ◽  
pp. 225-237 ◽  
Author(s):  
Igor S. Zonn ◽  
Sergey S. Zhiltsov ◽  
Aleksander V. Semenov
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Environmental Concerns

An overview of Canadian shale gas production and environmental concerns

2014 ◽  
Vol 126 ◽  
pp. 64-76 ◽  
Author(s):  
Christine Rivard ◽  
Denis Lavoie ◽  
René Lefebvre ◽  
Stephan Séjourné ◽  
Charles Lamontagne ◽  
...  
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Environmental Concerns

Environmental Concerns of Shale Gas Production

Shale Gas ◽  
2017 ◽  
pp. 137-144
Author(s):  
A.M. Dayal
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Environmental Concerns

Environmental Concerns of Shale Gas Production in China

2014 ◽  
Vol 36 (6) ◽  
pp. 638-642 ◽  
Author(s):  
P. Lu ◽  
T. Yuan ◽  
Q. Feng ◽  
Y. Sun
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Environmental Concerns

scholarly journals Geochemical Characteristics of Shallow Groundwater in Jiaoshiba Shale Gas Production Area: Implications for Environmental Concerns

Water ◽  
2016 ◽  
Vol 8 (12) ◽  
pp. 552 ◽  
Author(s):  
Yiman Li ◽  
Tianming Huang ◽  
Zhonghe Pang ◽  
Yingchun Wang ◽  
Chao Jin
Keyword(s):  
Shale Gas ◽  
Shallow Groundwater ◽  
Gas Production ◽  
Geochemical Characteristics ◽  
Environmental Concerns ◽  
Production Area

Safety Distance of Facilities and Personnel in Shale Gas Production Wellsite

2020 ◽  
Author(s):  
Hong Liu ◽  
Bolong He ◽  
Baizhong Hu ◽  
Ye Zhang ◽  
Xiaobin Xiong ◽  
...  
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Safety Distance

scholarly journals New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production

Fuels ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 286-303
Author(s):  
Vuong Van Pham ◽  
Ebrahim Fathi ◽  
Fatemeh Belyadi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Shale Gas ◽  
Field Data ◽  
Marcellus Shale ◽  
Hybrid Approach ◽  
Gas Production ◽  
Bayesian Optimization ◽  
Real Field ◽  
Engineering Problems

The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.

scholarly journals Quantitative study of the influence of organic-rich and natural fracture-rich regions on shale gas production

2020 ◽  
Vol 570 ◽  
pp. 042041
Author(s):  
S M Wei ◽  
Y Jin ◽  
Y Xia ◽  
Y Q Zhang
Keyword(s):  
Quantitative Study ◽  
Shale Gas ◽  
Gas Production ◽  
Natural Fracture

An assessment of the effect of partisan ideology on shale gas production and the implications for environmental regulations

2021 ◽  
pp. 100907
Author(s):  
Boying Li ◽  
Mingbo Zheng ◽  
Xinxin Zhao ◽  
Chun-Ping Chang
Keyword(s):  
Shale Gas ◽  
Environmental Regulations ◽  
Gas Production

Lithology and sedimentary heterogeneity of the Longmaxi Shale in the southern Sichuan Basin, China

2021 ◽  
pp. 1-49
Author(s):  
Boling Pu ◽  
Dazhong Dong ◽  
Ning Xin-jun ◽  
Shufang Wang ◽  
Yuman Wang ◽  
...  
Keyword(s):  
Continental Shelf ◽  
Deep Water ◽  
Shale Gas ◽  
Sichuan Basin ◽  
Sedimentary Environment ◽  
Gas Production ◽  
Carbonaceous Shale ◽  
Gas Wells ◽  
Longmaxi Shale ◽  
Siliceous Shale

Producers have always been eager to know the reasons for the difference in the production of different shale gas wells. The Southern Sichuan Basin in China is one of the main production zones of Longmaxi shale gas, while the shale gas production is quite different in different shale gas wells. The Longmaxi formation was deposited in a deep water shelf that had poor circulation with the open ocean, and is composed of a variety of facies that are dominated by fine-grained (clay- to silt-size) particles with a varied organic matter distribution, causing heterogeneity of the shale gas concentration. According to the different mother debris and sedimentary environment, we recognized three general sedimentary subfacies and seven lithofacies on the basis of mineralogy, sedimentary texture and structures, biota and the logging response: (1) there are graptolite-rich shale facies, siliceous shale facies, calcareous shale facies, and a small amount of argillaceous limestone facies in the deep - water shelf in the Weiyuan area and graptolite-rich shale facies and carbonaceous shale facies in the Changning area; (2) there are argillaceous shale facies and argillaceous limestone facies in the semi - deep - water continental shelf of the Weiyuan area and silty shale facies in the Changning area; (3) argillaceous shale facies are mainly developed in the shallow muddy continental shelf in the Weiyuan area, while silty shale facies mainly developed in the shallow shelf in the Changning area. Judging from the biostratigraphy of graptolite, the sedimentary environment was different in different stages.

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