scholarly journals Smart shale gas production performance analysis using machine learning applications

2021 ◽  
Author(s):  
Fahad I. Syed ◽  
Salem Alnaqbi ◽  
Temoor Muther ◽  
Amirmasoud K. Dahaghi ◽  
Shahin Negahban
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Shale Gas ◽  
Gas Production ◽  
Production Performance ◽  
Machine Learning Applications

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 The Evaluation and Sensitivity of Decline Curve Modelling

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2765
Author(s):  
Prinisha Manda ◽  
Diakanua Nkazi
Keyword(s):  
Shale Gas ◽  
Goodness Of Fit ◽  
Accuracy Assessment ◽  
Gas Production ◽  
Production Performance ◽  
Logistic Growth ◽  
Gas Reservoirs ◽  
Shale Gas Reservoirs ◽  
Decline Curve ◽  
Exponential Decline

The development of prediction tools for production performance and the lifespan of shale gas reservoirs has been a focus for petroleum engineers. Several decline curve models have been developed and compared with data from shale gas production. To accurately forecast the estimated ultimate recovery for shale gas reservoirs, consistent and accurate decline curve modelling is required. In this paper, the current decline curve models are evaluated using the goodness of fit as a measure of accuracy with field data. The evaluation found that there are advantages in using the current DCA models; however, they also have limitations associated with them that have to be addressed. Based on the accuracy assessment conducted on the different models, it appears that the Stretched Exponential Decline Model (SEDM) and Logistic Growth Model (LGM), followed by the Extended Exponential Decline Model (EEDM), the Power Law Exponential Model (PLE), the Doung’s Model, and lastly, the Arps Hyperbolic Decline Model, provide the best fit with production data.

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 AI/ML assisted shale gas production performance evaluation

2021 ◽  
Author(s):  
Fahad I. Syed ◽  
Temoor Muther ◽  
Amirmasoud K. Dahaghi ◽  
Shahin Negahban
Keyword(s):  
Performance Evaluation ◽  
Shale Gas ◽  
Continuous Process ◽  
The United States ◽  
Gas Production ◽  
Hydrocarbon Exploration ◽  
Production Performance ◽  
Support Vector ◽  
Data Set ◽  
And Performance

AbstractShale gas reservoirs are contributing a major role in overall hydrocarbon production, especially in the United States, and due to the intense development of such reservoirs, it is a must thing to learn the productive methods for modeling production and performance evaluation. Consequently, one of the most adopted techniques these days for the sake of production performance analysis is the utilization of artificial intelligence (AI) and machine learning (ML). Hydrocarbon exploration and production is a continuous process that brings a lot of data from sub-surface as well as from the surface facilities. Availability of such a huge data set that keeps on increasing over time enhances the computational capabilities and performance accuracy through AI and ML applications using a data-driven approach. The ML approach can be utilized through supervised and unsupervised methods in addition to artificial neural networks (ANN). Other ML approaches include random forest (RF), support vector machine (SVM), boosting technique, clustering methods, and artificial network-based architecture, etc. In this paper, a systematic literature review is presented focused on the AI and ML applications for the shale gas production performance evaluation and their modeling.

Key Factors in Shale Gas Modeling and Simulation

2014 ◽  
Vol 59 (4) ◽  
pp. 987-1004 ◽  
Author(s):  
Łukasz Klimkowski ◽  
Stanisław Nagy
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Production Performance ◽  
Hydraulic Fractures ◽  
Well Performance ◽  
Key Factors ◽  
Gas Well ◽  
Well Production ◽  
Multi Stage ◽  
The Impact

Abstract Multi-stage hydraulic fracturing is the method for unlocking shale gas resources and maximizing horizontal well performance. Modeling the effects of stimulation and fluid flow in a medium with extremely low permeability is significantly different from modeling conventional deposits. Due to the complexity of the subject, a significant number of parameters can affect the production performance. For a better understanding of the specifics of unconventional resources it is necessary to determine the effect of various parameters on the gas production process and identification of parameters of major importance. As a result, it may help in designing more effective way to provide gas resources from shale rocks. Within the framework of this study a sensitivity analysis of the numerical model of shale gas reservoir, built based on the latest solutions used in industrial reservoir simulators, was performed. The impact of different reservoir and hydraulic fractures parameters on a horizontal shale gas well production performance was assessed and key factors were determined.

Production-Performance Analysis of Composite Shale-Gas Reservoirs by the Boundary-Element Method

2019 ◽  
Vol 22 (01) ◽  
pp. 238-252 ◽  
Author(s):  
Minglu Wu ◽  
Mingcai Ding ◽  
Jun Yao ◽  
Chenfeng Li ◽  
Zhaoqin Huang ◽  
...  
Keyword(s):  
Boundary Element Method ◽  
Performance Analysis ◽  
Boundary Element ◽  
Shale Gas ◽  
Production Performance ◽  
Gas Reservoirs ◽  
Shale Gas Reservoirs ◽  
Element Method
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