Barium Isotopes Track the Source of Dissolved Solids in Produced Water from the Unconventional Marcellus Shale Gas Play

2020 ◽  
Vol 54 (7) ◽  
pp. 4275-4285 ◽  
Author(s):  
Zachary G. Tieman ◽  
Brian W. Stewart ◽  
Rosemary C. Capo ◽  
Thai T. Phan ◽  
Christina L. Lopano ◽  
...  
Keyword(s):  
Shale Gas ◽  
Produced Water ◽  
Marcellus Shale ◽  
Dissolved Solids

Temporal Changes in Microbial Ecology and Geochemistry in Produced Water from Hydraulically Fractured Marcellus Shale Gas Wells

2014 ◽  
Vol 48 (11) ◽  
pp. 6508-6517 ◽  
Author(s):  
Maryam A. Cluff ◽  
Angela Hartsock ◽  
Jean D. MacRae ◽  
Kimberly Carter ◽  
Paula J. Mouser
Keyword(s):  
Microbial Ecology ◽  
Shale Gas ◽  
Produced Water ◽  
Marcellus Shale ◽  
Temporal Changes ◽  
Gas Wells

CHEMICAL FORMS OF INORGANIC ELEMENTS IN WASTEWATER FROM A MARCELLUS SHALE-GAS WELL

2016 ◽  
Author(s):  
Douglas B. Kent ◽  
◽  
Matthias Kohler ◽  
Meagan Mnich ◽  
Christopher H. Conaway ◽  
...  
Keyword(s):  
Shale Gas ◽  
Marcellus Shale ◽  
Chemical Forms ◽  
Inorganic Elements ◽  
Gas Well

DETERMINING SOURCES OF BARIUM AND OTHER DISSOLVED SOLIDS IN OIL AND GAS PRODUCED WATER USING STABLE BARIUM ISOTOPES

2019 ◽  
Author(s):  
Brian W. Stewart ◽  
◽  
Zachary G. Tieman ◽  
Rosemary C. Capo ◽  
Rebecca M. Matecha ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Produced Water ◽  
Dissolved Solids

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.

Organic compounds in Weiyuan shale gas produced water: Identification, detection and rejection by ultrafiltration-reverse osmosis processes

2021 ◽  
Vol 412 ◽  
pp. 128699
Author(s):  
Yu Sun ◽  
Minghuo Wu ◽  
Tiezheng Tong ◽  
Peng Liu ◽  
Peng Tang ◽  
...  
Keyword(s):  
Reverse Osmosis ◽  
Organic Compounds ◽  
Shale Gas ◽  
Produced Water

Application of three different water treatment technologies to shale gas produced water

2016 ◽  
Vol 20 (2) ◽  
pp. 104-110 ◽  
Author(s):  
Eunyoung Jang ◽  
Seongpil Jeong ◽  
Eunhyea Chung
Keyword(s):  
Water Treatment ◽  
Shale Gas ◽  
Produced Water ◽  
Treatment Technologies

scholarly journals Lung Cancer Risk from Radon in Marcellus Shale Gas in Northeast U.S. Homes

Risk Analysis ◽  
2016 ◽  
Vol 36 (11) ◽  
pp. 2105-2119 ◽  
Author(s):  
Austin L. Mitchell ◽  
W. Michael Griffin ◽  
Elizabeth A. Casman
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Shale Gas ◽  
Lung Cancer Risk ◽  
Marcellus Shale

A data analytic workflow to forecast produced water from Marcellus shale

2019 ◽  
Vol 61 ◽  
pp. 293-302 ◽  
Author(s):  
Amin Ettehadtavakkol ◽  
Ali Jamali
Keyword(s):  
Produced Water ◽  
Marcellus Shale ◽  
Data Analytic
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