scholarly journals Machine learning-informed ensemble framework for evaluating shale gas production potential: Case study in the Marcellus Shale

2020 ◽  
Vol 84 ◽  
pp. 103679
Author(s):  
Derek Vikara ◽  
Donald Remson ◽  
Vikas Khanna
Keyword(s):  
Machine Learning ◽  
Shale Gas ◽  
Marcellus Shale ◽  
Gas Production ◽  
Production Potential ◽  
Potential Case

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.

Quantitative Prediction of Radon Concentration at Wellhead in Shale Gas Development

SPE Journal ◽  
2016 ◽  
Vol 22 (01) ◽  
pp. 235-243 ◽  
Author(s):  
Wei Tian ◽  
Xingru Wu ◽  
Tong Shen ◽  
Zhenyu Zhang ◽  
Sumeer Kalra
Keyword(s):  
Natural Gas ◽  
Numerical Simulations ◽  
Shale Gas ◽  
Radon Concentration ◽  
Marcellus Shale ◽  
Gas Production ◽  
Influential Factors ◽  
Natural Fracture ◽  
Quantitative Prediction ◽  
Radon Gas

Summary Hydraulic fracturing has been applied as an effective method to increase gas production from shale formations; however, this method has also raised concerns about its adverse impacts on environment. For example, in the Marcellus shale formation, some measured radon-gas concentrations exceeded the safe standard. Therefore, it is important to quantitatively evaluate radon concentration from fractured wells. However, existing researches have not successfully conducted a systematic and predictive study on the relationship between shale gas production and radon concentration at the wellhead of a hydraulically fractured well. To address this issue and quantitatively determine the radon concentration, we present the mechanisms of radon-gas generation and releasing, and conducted numerical simulations on its transport process in the subsurface formation system. The concentration of radon in produced gas is related with the original sources where the natural gas is extracted. Radon, generated from the radium alpha decay process, is trapped in pore spaces before the reservoir development. With the fluid flowing through the subsurface network, released radon will move to surface with the produced streams such as natural gas and flowback water. Our study shows that the radon concentration at wellhead could be significant. Influential factors such as natural-fracture-network properties, formation petrophysical parameters, and fracture dimension are investigated with sensitivity studies through numerical simulations. Analysis results suggest that radon wellhead concentration is strongly related with production rate. Thus, careful production design and protection are necessary to reduce radon hazard regarding the public and environmental impact.

Boom or bust? Mapping out the known unknowns of global shale gas production potential

2015 ◽  
Vol 49 ◽  
pp. 581-587 ◽  
Author(s):  
Jérôme Hilaire ◽  
Nico Bauer ◽  
Robert J. Brecha
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Production Potential ◽  
Known Unknowns

scholarly journals Coupling numerical simulation and machine learning to model shale gas production at different time resolutions

2015 ◽  
Vol 25 ◽  
pp. 380-392 ◽  
Author(s):  
Amirmasoud Kalantari-Dahaghi ◽  
Shahab Mohaghegh ◽  
Soodabeh Esmaili
Keyword(s):  
Machine Learning ◽  
Numerical Simulation ◽  
Shale Gas ◽  
Gas Production

scholarly journals Emerging unconventional shale plays in Western Australia

2013 ◽  
Vol 53 (1) ◽  
pp. 313 ◽  
Author(s):  
K. Ameed R. Ghori
Keyword(s):  
Shale Gas ◽  
Early Stage ◽  
Marcellus Shale ◽  
Gas Production ◽  
Source Rocks ◽  
Lower Permian ◽  
Upper Ordovician ◽  
Canning Basin ◽  
The Us ◽  
Gas Potential

Production of shale gas in the US has changed its position from a gas importer to a potential gas exporter. This has stimulated exploration for shale-gas resources in WA. The search started with Woodada Deep–1 (2010) and Arrowsmith–2 (2011) in the Perth Basin to evaluate the shale-gas potential of the Permian Carynginia Formation and the Triassic Kockatea Shale, and Nicolay–1 (2011) in the Canning Basin to evaluate the shale-gas potential of the Ordovician Goldwyer Formation. Estimated total shale-gas potential for these formations is about 288 trillion cubic feet (Tcf). Other petroleum source rocks include the Devonian Gogo and Lower Carboniferous Laurel formations of the Canning Basin, the Lower Permian Wooramel and Byro groups of the onshore Carnarvon Basin, and the Neoproterozoic shales of the Officer Basin. The Canning and Perth basins are producing petroleum, whereas the onshore Carnarvon and Officer basins are not producing, but they have indications for petroleum source rocks, generation, and migration from geochemistry data. Exploration is at a very early stage, and more work is needed to estimate the shale-gas potential of all source rocks and to verify estimated resources. Exploration for shale gas in WA will benefit from new drilling and production techniques and technologies developed during the past 15 years in the US, where more than 102,000 successful gas production wells have been drilled. WA shale-gas plays are stratigraphically and geochemically comparable to producing plays in the Upper Ordovician Utica Shale, Middle Devonian Marcellus Shale and Upper Devonian Bakken Formation, Upper Mississippian Barnett Shale, Upper Jurassic Haynesville-Bossier formations, and Upper Cretaceous Eagle Ford Shale of the US. WA is vastly under-explored and emerging self-sourcing shale plays have revived onshore exploration in the Canning, Carnarvon, and Perth basins.

Petrophysical Evaluation of Shale Gas Reservoirs: A Field Case Study of Marcellus Shale

2019 ◽  
Author(s):  
Levent Taylan Ozgur Yildirim ◽  
John Yilin Wang ◽  
Derek Elsworth
Keyword(s):  
Shale Gas ◽  
Marcellus Shale ◽  
Gas Reservoirs ◽  
Field Case ◽  
Shale Gas Reservoirs ◽  
Petrophysical Evaluation

scholarly journals The concept of well integrity in gas production activities

2016 ◽  
Vol 23 (2) ◽  
pp. 205-213 ◽  
Author(s):  
Peter Reichetseder
Keyword(s):  
Drinking Water ◽  
Shale Gas ◽  
Environmental Protection Agency ◽  
Marcellus Shale ◽  
The United States ◽  
Gas Production ◽  
Well Integrity ◽  
Drinking Water Resources ◽  
The Us ◽  
Geological Situation

Abstract Shale gas production in the US, predominantly from the Marcellus shale, has been accused of methane emissions and contaminating drinking water under the suspicion that this is caused by hydraulic fracturing in combination with leaking wells. Misunderstandings of the risks of shale gas production are widespread and are causing communication problems. This paper discusses recent preliminary results from the US Environmental Protection Agency (EPA) draft study, which is revealing fact-based issues: EPA did not find evidence that these mechanisms have led to widespread, systemic impacts on drinking water resources in the United States, which contrasts many broad-brushed statements in media and public. The complex geological situation and extraction history of oil, gas and water in the Marcellus area in Pennsylvania is a good case for learnings and demonstrating the need for proper analysis and taking the right actions to avoid problems. State-of-the-art technology and regulations of proper well integrity are available, and their application will provide a sound basis for shale gas extraction.

Predicting gas production using machine learning methods: A Case Study

2019 ◽  
Author(s):  
Tania Mukherjee ◽  
Thomas Burgett ◽  
Telha Ghanchi ◽  
Colleen Donegan ◽  
Taylor Ward
Keyword(s):  
Machine Learning ◽  
Gas Production ◽  
Learning Methods ◽  
Machine Learning Methods
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