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Data-driven model for hydraulic fracturing design optimization: focus on building digital database and production forecast
[article]
2020
arXiv
pre-print
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system for advising DESC and production stimulation engineers on an optimized fracturing design. ...
Growing amount of hydraulic fracturing (HF) jobs in the recent two decades resulted in a significant amount of measured data available for development of predictive models via machine learning (ML). ...
The authors are particularly grateful to We would like to state it explicitly that the models presented in this work are solely based on the field data provided by JSC Gazprom neft and we are grateful ...
arXiv:1910.14499v3
fatcat:fxpg73zyanehnb3sf65njx65iq
Probabilistic Evaluation of Hydraulic Fracture Performance Using Ensemble Machine Learning
2022
Geofluids
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The evaluations provide a guideline for optimization of HF designs of wells that have not been hydraulically stimulated in the region. ...
We present a probabilistic evaluation approach that integrates ensemble machine learning with Monte Carlo simulation. ...
In recent years, the machine learning method has become a powerful tool for predicting the production performance. For instance, Nejad et al. ...
doi:10.1155/2022/1760065
fatcat:5ey4acf7evc5hmyx2ax53rwxna
A Novel Shale Gas Production Prediction Model Based on Machine Learning and Its Application in Optimization of Multistage Fractured Horizontal Wells
2021
Frontiers in Earth Science
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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. ...
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. ...
Support Vector Machine Regression SVM is based on the theory of small sample statistical learning proposed by Vapnik (2000) , which focuses on the statistical learning rule under small sample data. ...
doi:10.3389/feart.2021.726537
doaj:436c4d10959849d9925da046d24e69c4
fatcat:sp3drdt4rjh67fy6jyquow54km
Machine Learning-Based Prediction of Oil-Water Flow Dynamics in Carbonate Reservoirs
2022
Fluid Dynamics & Materials Processing
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We propose a machine learning-based capacity prediction method for carbonate rocks by analyzing the degree of correlation between various factors and three machine learning models: support vector machine ...
The error rate for these three models are 10%, 16%, and 33%, respectively (according to the analysis of 40 training wells and 10 test wells). ...
Machine Learning Model Preference Since the actual fracturing sample data set is small and not suitable for machine learning methods based on large data samples, the three algorithms used are support vector ...
doi:10.32604/fdmp.2022.020649
fatcat:w2idkd3g75gobcxwvvgi6okd6a
Forecasting Damage Mechanics by Deep Learning
2019
Computers Materials & Continua
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Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio, single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable ...
Relied on learning an amount of information from given data, the long short-term memory (LSTM) method and multi-layer neural networks (MNN) method are applied to predict solutions. ...
Acknowledgment: The author would like to thank European Commission H2020-MSCA-RISE BESTOFRAC project for research funding. ...
doi:10.32604/cmc.2019.08001
fatcat:x43kidqjp5bhrfngyrujwuwkxe
Probabilistic Prediction of Multi-Wells Production Based on Production Characteristics Analysis Using Key Factors in Shale Formations
2021
Energies
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These classified groups also identified the relationship between hydraulic fracturing design factors and productivity. ...
In addition, the limited data obtained from nearby existing multi-wells should be used to estimate the production of new wells. ...
production, and a method to present optimized hydraulic fracturing design factors for a single well was proposed. ...
doi:10.3390/en14175226
fatcat:svdw7wulrjgkxkimgji4saoney
Connectivity and Flowrate Estimation of Discrete Fracture Network Using Artificial Neural Network
2018
Journal of Soft Computing in Civil Engineering
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For this reason, in this paper using Artificial Neural Network, a tool is designed which precisely and accurately estimate hydraulic parameters of discrete fracture network. ...
On the other hand, excellent correlation of 0.98 exists between the predicted and actual value that proves the reliability of the designed artificial neural network. ...
Fig 8 shows the correlation between actual target data and computed data for all three groups of learning, evaluation and test data. ...
doi:10.22115/scce.2018.105018.1031
doaj:5820412a508243238c9ead2e4f6b8153
fatcat:lgyslk4z6beube2lzvnxgpxrem
Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks
2022
Advances in Geo-Energy Research
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Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks. ...
Machine learning database The data of this study come from the fractured horizontal wells in well block Ma18 of the Mahu oil field. ...
Liu et al. (2021) designed a shale gas well EUR prediction algorithm on the basis of deep learning in light of geological data, fracturing stimulation data, production data, and EUR calculation results ...
doi:10.46690/ager.2022.02.04
fatcat:lbwhm5fgjvgfzag5szqwjhahp4
Data-driven model for hydraulic fracturing design optimization. Part II: Inverse problem
[article]
2021
arXiv
pre-print
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The model is developed based on an extended digital field data base of reservoir, well and fracturing design parameters. ...
A recommendation system containing all the above methods is designed to advise a production stimulation engineer on an optimized fracturing design. ...
We would like to state it explicitly that the models presented in this work are solely based on the field data provided by JSC Gazpromneft and we are grateful for the permission to publish. ...
arXiv:2108.00751v1
fatcat:i537ksfwz5gkvmnxmap7xnezee
Coupling numerical simulation and machine learning to model shale gas production at different time resolutions
2015
Journal of Natural Gas Science and Engineering
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Reservoir simulation is the most robust tool for simulating gas production from the desorption controlled and hydraulically fractured shale reservoir. ...
Instead of using pre-defined functional forms that are more frequently used to develop response surfaces, a series of machine learning algorithms that conform to the system theory are used. ...
(ISI) and Schlumberger for providing the ISMA and Eclipse/ Petrel software packages. ...
doi:10.1016/j.jngse.2015.04.018
fatcat:ybr356l6xjdqjbrhetvirxcyrm
Productivity Prediction of Fractured Horizontal Well in Shale Gas Reservoirs with Machine Learning Algorithms
2021
Applied Sciences
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Predicting shale gas production under different geological and fracturing conditions in the fractured shale gas reservoirs is the foundation of optimizing the fracturing parameters, which is crucial to ...
The research results can provide a fast and effective mean for shale gas productivity prediction. ...
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app112412064
fatcat:ns3zz2eglrdfji5hjmyjpwskjq
Hybrid Data-driven Framework for Shale Gas Production Performance Analysis via Game Theory, Machine Learning and Optimization Approaches
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2112.04243v1 fatcat:iczhkjhvevfyfez4pt3vym44mu
A multi-model-fused stacked model is trained for production forecast, on the basis of which derivative-free optimization algorithms are introduced to optimize the development plan. ...
A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential, designing field development plan, and making investment decisions. ...
We apply PSO, DE, and BO to optimize five engineering parameters of the hydraulic fracturing design: stimulated length, stage count, fracturing fluid intensity, proppant intensity, and angle to the minimum ...
arXiv:2112.04243v2
fatcat:bve6ds7fr5bhzk3s362hhfdhry
Detection and characterization of microseismic events from fiber-optic DAS data using deep learning
[article]
2022
arXiv
pre-print
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We design, train, and deploy a deep learning model to detect microseismic events in DAS data automatically. ...
We optimize the deep learning model's network architecture together with its training hyperparameters by Bayesian optimization. ...
Acknowledgements We thank Chevron Technical Centre for making the data available for this research and giving permission to publish this study. ...
arXiv:2203.07217v1
fatcat:2qoddtmkknhwzmjf3rldl5f4i4
Advancement of Hydraulic Fracture Diagnostics in Unconventional Formations
2021
Geofluids
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Hence, the applications of machine learning in fracture diagnostics and DFIT analysis were discussed. ...
Multistage hydraulic fracturing is a technique to extract hydrocarbon from tight and unconventional reservoirs. ...
Machine Learning (ML) Applications. ...
doi:10.1155/2021/4223858
fatcat:4tmtsy32bzcqhj23cvi2kwpohm
Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs
2021
Geofluids
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This paper presents a thorough analysis of the feasibility of machine learning in multiple fractured shale reservoirs. ...
Therefore, in this paper, two methods are developed to bridge this gap by using the machine learning technique to forecast well production performance in unconventional reservoirs, especially on the EOR ...
Therefore, to design an optimized ANN system, feature selection was implemented to control the quality of data by removing inefficient features in input data. ...
doi:10.1155/2021/3169456
fatcat:3s52peqcjvhmhpxk2rss4eashi
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