scholarly journals Development of shale gas production prediction models based on machine learning using early data

2022 ◽  
Vol 8 ◽  
pp. 1229-1237
Author(s):  
Wente Niu ◽  
Jialiang Lu ◽  
Yuping Sun
Keyword(s):  
Machine Learning ◽  
Shale Gas ◽  
Prediction Models ◽  
Gas Production ◽  
Early Data ◽  
Gas Production Prediction ◽  
Production Prediction

scholarly journals A Novel Shale Gas Production Prediction Model Based on Machine Learning and Its Application in Optimization of Multistage Fractured Horizontal Wells

2021 ◽  
Vol 9 ◽  
Author(s):  
Huijun Wang ◽  
Lu Qiao ◽  
Shuangfang Lu ◽  
Fangwen Chen ◽  
Zhixiong Fang ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Simulation Model ◽  
Shale Gas ◽  
Horizontal Well ◽  
Gas Production ◽  
Production Model ◽  
Support Vector ◽  
Gas Production Prediction ◽  
Half Length ◽  
Production Prediction

Shale gas production prediction and horizontal well parameter optimization are significant for shale gas development. However, conventional reservoir numerical simulation requires extensive resources in terms of labor, time, and computations, and so the optimization problem still remains a challenge. Therefore, we propose, for the first time, a new gas production prediction methodology based on Gaussian Process Regression (GPR) and Convolution Neural Network (CNN) to complement the numerical simulation model and achieve rapid optimization. Specifically, through sensitivity analysis, porosity, permeability, fracture half-length, and horizontal well length were selected as influencing factors. Second, the n-factorial experimental design was applied to design the initial experiment and the dataset was constructed by combining the simulation results with the case parameters. Subsequently, the gas production model was built by GPR, CNN, and SVM based on the dataset. Finally, the optimal model was combined with the optimization algorithm to maximize the Net Present Value (NPV) and obtain the optimal fracture half-length and horizontal well length. Experimental results demonstrated the GPR model had prominent modeling capabilities compared with CNN and Support Vector Machine (SVM) and achieved the satisfactory prediction performance. The fracture half-length and well length optimized by the GPR model and reservoir numerical simulation model converged to almost the same values. Compared with the field reference case, the optimized NPV increased by US$ 7.43 million. Additionally, the time required to optimize the GPR model was 1/720 of that of numerical simulation. This work enriches the knowledge of shale gas development technology and lays the foundation for realizing the scale-benefit development for shale gas, so as to realize the integration of geological engineering.

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 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 Development of Shale Gas Prediction Models for Long-Term Production and Economics Based on Early Production Data in Barnett Reservoir

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 424 ◽  
Author(s):  
Viet Nguyen-Le ◽  
Hyundon Shin ◽  
Edward Little
Keyword(s):  
Shale Gas ◽  
Net Present Value ◽  
Prediction Models ◽  
Input Parameter ◽  
Gas Production ◽  
Barnett Shale ◽  
Production Data ◽  
Production History ◽  
Early Production

This study examined the relationship between the early production data and the long-term performance of shale gas wells, including the estimated ultimate recovery (EUR) and economics. The investigated early production data are peak gas production rate, 3-, 6-, 12-, 18-, and 24-month cumulative gas production (CGP). Based on production data analysis of 485 reservoir simulation datasets, CGP at 12 months (CGP_12m) was selected as a key input parameter to predict a long-term shale gas well’s performance in terms of the EUR and net present value (NPV) for a given well. The developed prediction models were then validated using the field production data from 164 wells which have more than 10 years of production history in Barnett Shale, USA. The validation results showed strong correlations between the predicted data and field data. This suggests that the proposed models can predict the shale gas production and economics reliably in Barnett shale area. Only a short history of production (one year) can be used to estimate the EUR and NPV of various production periods for a gas well. Moreover, the proposed prediction models are consistently applied for young wells with short production histories and lack of reservoir and hydraulic fracturing data.

scholarly journals Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos

2017 ◽  
Vol 157 ◽  
pp. 1148-1159 ◽  
Author(s):  
Thomas A. McCourt ◽  
Suzanne Hurter ◽  
Brodie Lawson ◽  
Fengde Zhou ◽  
Bevan Thompson ◽  
...  
Keyword(s):  
Coal Seam ◽  
Uncertainty Quantification ◽  
Polynomial Chaos ◽  
Gas Production ◽  
Coal Seam Gas ◽  
Gas Production Prediction ◽  
Production Prediction

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
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