Towards a More Accurate Gas-in-Place Model: Reconciling Gas Storage with Gas Production in the Marcellus Shale, Appalachian Basin, USA

2020 ◽  
Author(s):  
David Blood ◽  
Scott McCallum ◽  
Jalal Jalali ◽  
Ashley Douds ◽  
Merril Stypula
Keyword(s):  
Marcellus Shale ◽  
Appalachian Basin ◽  
Gas Storage ◽  
Gas Production

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.

Impact of the Cluster Spacing on Hydraulic Fracture Conductivity and Productivity of a Marcellus Shale Horizontal Well

2021 ◽  
Author(s):  
Mohamed El Sgher ◽  
Kashy Aminian ◽  
Ameri Samuel
Keyword(s):  
Hydraulic Fracture ◽  
Marcellus Shale ◽  
Technical Information ◽  
Gas Production ◽  
Production Performance ◽  
Valuable Insight ◽  
Fracture Conductivity ◽  
Fracture Properties ◽  
Gas Recovery ◽  
The Impact

Abstract The objective of this study was to investigate the impact of the hydraulic fracturing treatment design, including cluster spacing and fracturing fluid volume on the hydraulic fracture properties and consequently, the productivity of a horizontal Marcellus Shale well with multi-stage fractures. The availability of a significant amount of advanced technical information from the Marcellus Shale Energy and Environment Laboratory (MSEEL) provided an opportunity to perform an integrated analysis to gain valuable insight into optimizing fracturing treatment and the gas recovery from Marcellus shale. The available technical information from a horizontal well at MSEEL includes well logs, image logs (both vertical and lateral), diagnostic fracture injection test (DFIT), fracturing treatment data, microseismic recording during the fracturing treatment, production logging data, and production data. The analysis of core data, image logs, and DFIT provided the necessary data for accurate prediction of the hydraulic fracture properties and confirmed the presence and distribution of natural fractures (fissures) in the formation. Furthermore, the results of the microseismic interpretation were utilized to adjust the stress conditions in the adjacent layers. The predicted hydraulic fracture properties were then imported into a reservoir simulation model, developed based on the Marcellus Shale properties, to predict the production performance of the well. Marcellus Shale properties, including porosity, permeability, adsorption characteristics, were obtained from the measurements on the core plugs and the well log data. The Quanta Geo borehole image log from the lateral section of the well was utilized to estimate the fissure distribution s in the shale. The measured and published data were utilized to develop the geomechnical factors to account for the hydraulic fracture conductivity and the formation (matrix and fissure) permeability impairments caused by the reservoir pressure depletion during the production. Stress shadowing and the geomechanical factors were found to play major roles in production performance. Their inclusion in the reservoir model provided a close agreement with the actual production performance of the well. The impact of stress shadowing is significant for Marcellus shale because of the low in-situ stress contrast between the pay zone and the adjacent zones. Stress shadowing appears to have a significant impact on hydraulic fracture properties and as result on the production during the early stages. The geomechanical factors, caused by the net stress changes have a more significant impact on the production during later stages. The cumulative gas production was found to increase as the cluster spacing was decreased (larger number of clusters). At the same time, the stress shadowing caused by the closer cluster spacing resulted in a lower fracture conductivity which in turn diminished the increase in gas production. However, the total fracture volume has more of an impact than the fracture conductivity on gas recovery. The analysis provided valuable insight for optimizing the cluster spacing and the gas recovery from Marcellus shale.

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