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Energies
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Applications of Artificial Intelligence Techniques in the Petroleum Engineering
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Applications of Artificial Intelligence Techniques in the Petroleum Engineering

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H: Geo-Energy".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 12226

Special Issue Editors

Dr. Baehyun Min

E-Mail Website
Guest Editor
Department of Climate and Energy Systems Engineering, Ewha Womans University, Seoul, Korea
Interests: petroleum; reservoir; optimization; artificial intelligence; machine learning
Dr. Hoonyoung Jeong

E-Mail Website
Guest Editor
Department of Energy Resources Engineering, Seoul National University, Seoul, Korea
Interests: thermo-hydro-mechanical-chemical simulation; reservoir modeling; optimization; machine learning

Special Issue Information

Dear Colleagues,

During the last decade, artificial intelligence has gained importance as an efficient data analysis technique in the petroleum industry. The E&P business moves forward toward the era of digital transformation for realizing a sustainable energy society. In particular, deep learning is considered crucial for maximizing carbon energy utilization and minimizing carbon emissions to cope with climate change.

With this Special Issue, we aim to collect original research or review articles on applications of artificial intelligence techniques in petroleum geology, petrophysics, and petroleum engineering. Articles on related topics will also be considered for publication.

Dr. Baehyun Min
Dr. Hoonyoung Jeong
Guest Editors

Manuscript Submission Information

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Keywords

  • petroleum geology
  • petrophysics
  • petroleum engineering
  • optimization
  • artificial intelligence
  • data analytics
  • cloud solutions

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Published Papers (3 papers)

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Research

16 pages, 1182 KiB  
Article
A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan
by Timur Merembayev, Darkhan Kurmangaliyev, Bakhbergen Bekbauov and Yerlan Amanbek
Energies 2021, 14(7), 1896; https://doi.org/10.3390/en14071896 - 29 Mar 2021
Cited by 52 | Viewed by 4743
Abstract
Defining distinctive areas of the physical properties of rocks plays an important role in reservoir evaluation and hydrocarbon production as core data are challenging to obtain from all wells. In this work, we study the evaluation of lithofacies values using the machine learning [...] Read more.
Defining distinctive areas of the physical properties of rocks plays an important role in reservoir evaluation and hydrocarbon production as core data are challenging to obtain from all wells. In this work, we study the evaluation of lithofacies values using the machine learning algorithms in the determination of classification from various well log data of Kazakhstan and Norway. We also use the wavelet-transformed data in machine learning algorithms to identify geological properties from the well log data. Numerical results are presented for the multiple oil and gas reservoir data which contain more than 90 released wells from Norway and 10 wells from the Kazakhstan field. We have compared the the machine learning algorithms including KNN, Decision Tree, Random Forest, XGBoost, and LightGBM. The evaluation of the model score is conducted by using metrics such as accuracy, Hamming loss, and penalty matrix. In addition, the influence of the dataset features on the prediction is investigated using the machine learning algorithms. The result of research shows that the Random Forest model has the best score among considered algorithms. In addition, the results are consistent with outcome of the SHapley Additive exPlanations (SHAP) framework. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Techniques in the Petroleum Engineering)
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20 pages, 55529 KiB  
Article
Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning
by Sungil Kim, Byungjoon Yoon, Jung-Tek Lim and Myungsun Kim
Energies 2021, 14(5), 1499; https://doi.org/10.3390/en14051499 - 9 Mar 2021
Cited by 14 | Viewed by 3709
Abstract
It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the application of machine learning for signal–noise classification of microseismic data [...] Read more.
It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the application of machine learning for signal–noise classification of microseismic data from Pohang, South Korea. For the first time, unique microseismic data were obtained from the monitoring system of the borehole station PHBS8 located in Yongcheon-ri, Pohang region, while hydraulic stimulation was being conducted. The collected data were properly preprocessed and utilized as training and test data for supervised and unsupervised learning methods: random forest, convolutional neural network, and K-medoids clustering with fast Fourier transform. The supervised learning methods showed 100% and 97.4% of accuracy for the training and test data, respectively. The unsupervised method showed 97.0% accuracy. Consequently, the results from machine learning validated that automation based on the proposed supervised and unsupervised learning applications can classify the acquired microseismic data in real time. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Techniques in the Petroleum Engineering)
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Graphical abstract

19 pages, 4991 KiB  
Article
Adaptive Surrogate Estimation with Spatial Features Using a Deep Convolutional Autoencoder for CO2 Geological Sequestration
by Suryeom Jo, Changhyup Park, Dong-Woo Ryu and Seongin Ahn
Energies 2021, 14(2), 413; https://doi.org/10.3390/en14020413 - 13 Jan 2021
Cited by 11 | Viewed by 2467
Abstract
This paper develops a reliable deep-learning framework to extract latent features from spatial properties and investigates adaptive surrogate estimation to sequester CO2 into heterogeneous deep saline aquifers. Our deep-learning architecture includes a deep convolutional autoencoder (DCAE) and a fully-convolutional network to not [...] Read more.
This paper develops a reliable deep-learning framework to extract latent features from spatial properties and investigates adaptive surrogate estimation to sequester CO2 into heterogeneous deep saline aquifers. Our deep-learning architecture includes a deep convolutional autoencoder (DCAE) and a fully-convolutional network to not only reduce computational costs but also to extract dimensionality-reduced features to conserve spatial characteristics. The workflow integrates two different spatial properties within a single convolutional system, and it also achieves accurate reconstruction performance. This approach significantly reduces the number of parameters to 4.3% of the original number required, e.g., the number of three-dimensional spatial properties needed decreases from 44,460 to 1920. The successful dimensionality reduction is accomplished by the DCAE system regarding all inputs as image channels from the initial stage of learning using the fully-convolutional network instead of fully-connected layers. The DCAE reconstructs spatial parameters such as permeability and porosity while conserving their statistical values, i.e., their mean and standard deviation, achieving R-squared values of over 0.972 with a mean absolute percentage error of their mean values of less than 1.79%. The adaptive surrogate model using the latent features extracted by DCAE, well operations, and modeling parameters is able to accurately estimate CO2 sequestration performances. The model shows R-squared values of over 0.892 for testing data not used in training and validation. The DCAE-based surrogate estimation exploits the reliable integration of various spatial data within the fully-convolutional network and allows us to evaluate flow behavior occurring in a subsurface domain. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Techniques in the Petroleum Engineering)
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