Understanding Shale Gas Production Mechanisms Through Reservoir Simulation

2014 ◽  
Author(s):  
Hao Sun ◽  
Adwait Chawathe ◽  
Hussein Hoteit ◽  
Xundan Shi ◽  
Lin Li
Keyword(s):  
Shale Gas ◽  
Reservoir Simulation ◽  
Gas Production ◽  
Production Mechanisms

The impact of kerogen properties on shale gas production: A reservoir simulation sensitivity analysis

2017 ◽  
Vol 48 ◽  
pp. 13-23 ◽  
Author(s):  
Shihao Wang ◽  
Andrew E. Pomerantz ◽  
Wenyue Xu ◽  
Alexander Lukyanov ◽  
Robert L. Kleinberg ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Shale Gas ◽  
Reservoir Simulation ◽  
Gas Production ◽  
The Impact

Investigations Into Devonian Shale Gas Production Mechanisms in Southern Ohio

1984 ◽  
Author(s):  
T.W. Thompson ◽  
R.A. McBane ◽  
Gary Sitler ◽  
Jon Strawn ◽  
Mark Moody
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Devonian Shale ◽  
Production Mechanisms

scholarly journals Integrated Approach to Drilling Project in Unconventional Reservoir Using Reservoir Simulation

2018 ◽  
Vol 35 ◽  
pp. 01002
Author(s):  
Jerzy Stopa ◽  
Rafał Wiśniowski ◽  
Paweł Wojnarowski ◽  
Damian Janiga ◽  
Krzysztof Skrzypaszek
Keyword(s):  
Shale Gas ◽  
Reservoir Simulation ◽  
Integrated Approach ◽  
Gas Production ◽  
Full Scale ◽  
Production Optimization ◽  
Unconventional Reservoir ◽  
Gas Field ◽  
Technical Factor ◽  
Drilling Project

Accumulation and flow mechanisms in unconventional reservoir are different compared to conventional. This requires a special approach of field management with drilling and stimulation treatments as major factor for further production. Integrated approach of unconventional reservoir production optimization assumes coupling drilling project with full scale reservoir simulation for determine best well placement, well length, fracturing treatment design and mid-length distance between wells. Full scale reservoir simulation model emulate a part of polish shale – gas field. The aim of this paper is to establish influence of technical factor for gas production from shale gas field. Due to low reservoir permeability, stimulation treatment should be direct towards maximizing the hydraulic contact. On the basis of production scenarios, 15 stages hydraulic fracturing allows boost gas production over 1.5 times compared to 8 stages. Due to the possible interference of the wells, it is necessary to determine the distance between the horizontal parts of the wells trajectories. In order to determine the distance between the wells allowing to maximize recovery factor of resources in the stimulated zone, a numerical algorithm based on a dynamic model was developed and implemented. Numerical testing and comparative study show that the most favourable arrangement assumes a minimum allowable distance between the wells. This is related to the volume ratio of the drainage zone to the total volume of the stimulated zone.

Devonian Shale Gas Production: Mechanisms and Simple Models

1991 ◽  
Vol 43 (04) ◽  
pp. 476-482 ◽  
Author(s):  
Eric S. Carlson ◽  
James C. Mercer
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Devonian Shale ◽  
Production Mechanisms ◽  
Simple Models

Coupling of fracture model with reservoir simulation to simulate shale gas production with complex fractures and nanopores

2020 ◽  
Vol 193 ◽  
pp. 107422 ◽  
Author(s):  
Xincheng Wan ◽  
Vamegh Rasouli ◽  
Branko Damjanac ◽  
Wei Yu ◽  
Hongbing Xie ◽  
...  
Keyword(s):  
Shale Gas ◽  
Reservoir Simulation ◽  
Gas Production ◽  
Fracture Model ◽  
Complex Fractures

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.

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