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Applications of AI Tools in Petroleum Industry from Geosciences to...
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Applications of AI Tools in Petroleum Industry from Geosciences to Engineering

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Geological Oceanography".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 12747

Special Issue Editors

Dr. Mehdi Ostadhassan

E-Mail Website
Guest Editor
1. Department of Biomedical Engineering, University of North Dakota, Grand Forks, ND, USA
2. Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Institute of Unconventional Oil and Gas, Northeast Petroleum University, Daqing, China
Interests: materials characterization, petroleum system evaluation, organic geochemistry; force spectroscopy; analytical methods in rock characterization; application of 3D printing in geosciences; rock mechanics; ML/AI methods
Special Issues, Collections and Topics in MDPI journals
Dr. Hem Bahadur Motra

E-Mail Website
Guest Editor
Faculty of Mathematics and Natural Sciences, Department of Geosciences, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
Interests: engineering computing; uncertainty modeling in structural and geotechical engineering; quality evaluation of numerical; mathematical and experimental models/methods; reservoir characterization; geostatistics
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Liu

E-Mail Website
Guest Editor
Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Institute of Unconventional Oil and Gas, Northeast Petroleum University, Daqing, China
Interests: shale characterization; reservoir geomechanics; petroleum geochemistry; shale petrophysics

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has changed the direction of multiple industries in the past decade and has significantly impacted how they are operating these days toward more efficiency. In this regard, petroleum industry sought the potential applications of AI relatively late, but the amount of research in this area cannot be ignored. This special issue aims to showcase different aspects of petroleum engineering and geosciences where various AI tools have been utilized to provide us with more accurate results by avoiding complex numerical/analytical modeling. Editors, welcome original research papers, reviews, letters and communications that presents different applications of AI in reservoir fluid-rock interaction problems, improved field operations, production forecasting, reservoir characterization, stimulation and simulation methods. We are particularly interested in articles that have employed various and mainstream machine learning (ML) and AI techniques such as the deep learning, supervised and unsupervised learning, a variety of boosting methods where the algorithms are able to distinguish patterns or cluster the big data that is collected from the field/reservoir/play to resolve an issue with higher precision and independent from heavy mathematical manipulations. Ultimately, authors are further encouraged to consider submitting articles in a wide range of topics from exploration to production.

Dr. Mehdi Ostadhassan
Dr. Hem Bahadur Motra
Dr. Bo Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Machine Learning
  • Artificial Intelligence
  • Data Analytics
  • Optimization
  • Reservoir Modeling and Characterization
  • Supervised and Unsupervised Learning

Published Papers (3 papers)

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Research

18 pages, 2151 KiB  
Article
Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach
by Qasem Al-Tashi, Emelia Akashah Patah Akhir, Said Jadid Abdulkadir, Seyedali Mirjalili, Tareq M. Shami, Hitham Alhusssian, Alawi Alqushaibi, Ayed Alwadain, Abdullateef O. Balogun and Nasser Al-Zidi
J. Mar. Sci. Eng. 2021, 9(8), 888; https://doi.org/10.3390/jmse9080888 - 18 Aug 2021
Cited by 7 | Viewed by 2641
Abstract
The accurate classification of reservoir recovery factor is dampened by irregularities such as noisy and high-dimensional features associated with the reservoir measurements or characterization. These irregularities, especially a larger number of features, make it difficult to perform accurate classification of reservoir recovery factor, [...] Read more.
The accurate classification of reservoir recovery factor is dampened by irregularities such as noisy and high-dimensional features associated with the reservoir measurements or characterization. These irregularities, especially a larger number of features, make it difficult to perform accurate classification of reservoir recovery factor, as the generated reservoir features are usually heterogeneous. Consequently, it is imperative to select relevant reservoir features while preserving or amplifying reservoir recovery accuracy. This phenomenon can be treated as a multi-objective optimization problem, since there are two conflicting objectives: minimizing the number of measurements and preserving high recovery classification accuracy. In this study, wrapper-based multi-objective feature selection approaches are proposed to estimate the set of Pareto optimal solutions that represents the optimum trade-off between these two objectives. Specifically, three multi-objective optimization algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Particle Swarm Optimization (MOPSO)—are investigated in selecting relevant features from the reservoir dataset. To the best of our knowledge, this is the first time multi-objective optimization has been used for reservoir recovery factor classification. The Artificial Neural Network (ANN) classification algorithm is used to evaluate the selected reservoir features. Findings from the experimental results show that the proposed MOGWO-ANN outperforms the other two approaches (MOPSO and NSGA-II) in terms of producing non-dominated solutions with a small subset of features and reduced classification error rate. Full article
(This article belongs to the Special Issue Applications of AI Tools in Petroleum Industry from Geosciences to Engineering)
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23 pages, 75292 KiB  
Article
Prediction of Water Saturation from Well Log Data by Machine Learning Algorithms: Boosting and Super Learner
by Fahimeh Hadavimoghaddam, Mehdi Ostadhassan, Mohammad Ali Sadri, Tatiana Bondarenko, Igor Chebyshev and Amir Semnani
J. Mar. Sci. Eng. 2021, 9(6), 666; https://doi.org/10.3390/jmse9060666 - 16 Jun 2021
Cited by 17 | Viewed by 5681
Abstract
Intelligent predictive methods have the power to reliably estimate water saturation (Sw) compared to conventional experimental methods commonly performed by petrphysicists. However, due to nonlinearity and uncertainty in the data set, the prediction might not be accurate. There exist new machine [...] Read more.
Intelligent predictive methods have the power to reliably estimate water saturation (Sw) compared to conventional experimental methods commonly performed by petrphysicists. However, due to nonlinearity and uncertainty in the data set, the prediction might not be accurate. There exist new machine learning (ML) algorithms such as gradient boosting techniques that have shown significant success in other disciplines yet have not been examined for Sw prediction or other reservoir or rock properties in the petroleum industry. To bridge the literature gap, in this study, for the first time, a total of five ML code programs that belong to the family of Super Learner along with boosting algorithms: XGBoost, LightGBM, CatBoost, AdaBoost, are developed to predict water saturation without relying on the resistivity log data. This is important since conventional methods of water saturation prediction that rely on resistivity log can become problematic in particular formations such as shale or tight carbonates. Thus, to do so, two datasets were constructed by collecting several types of well logs (Gamma, density, neutron, sonic, PEF, and without PEF) to evaluate the robustness and accuracy of the models by comparing the results with laboratory-measured data. It was found that Super Learner and XGBoost produced the highest accurate output (R2: 0.999 and 0.993, respectively), and with considerable distance, Catboost and LightGBM were ranked third and fourth, respectively. Ultimately, both XGBoost and Super Learner produced negligible errors but the latest is considered as the best amongst all. Full article
(This article belongs to the Special Issue Applications of AI Tools in Petroleum Industry from Geosciences to Engineering)
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13 pages, 5862 KiB  
Article
Fault Detection Based on Fully Convolutional Networks (FCN)
by Jizhong Wu, Bo Liu, Hao Zhang, Shumei He and Qianqian Yang
J. Mar. Sci. Eng. 2021, 9(3), 259; https://doi.org/10.3390/jmse9030259 - 01 Mar 2021
Cited by 23 | Viewed by 3037
Abstract
It is of great significance to detect faults correctly in continental sandstone reservoirs in the east of China to understand the distribution of remaining structural reservoirs for more efficient development operation. However, the majority of the faults is characterized by small displacements and [...] Read more.
It is of great significance to detect faults correctly in continental sandstone reservoirs in the east of China to understand the distribution of remaining structural reservoirs for more efficient development operation. However, the majority of the faults is characterized by small displacements and unclear components, which makes it hard to recognize them in seismic data via traditional methods. We consider fault detection as an end-to-end binary image-segmentation problem of labeling a 3D seismic image with ones as faults and zeros elsewhere. Thus, we developed a fully convolutional network (FCN) based method to fault segmentation and used the synthetic seismic data to generate an accurate and sufficient training data set. The architecture of FCN is a modified version of the VGGNet (A convolutional neural network was named by Visual Geometry Group). Transforming fully connected layers into convolution layers enables a classification net to create a heatmap. Adding the deconvolution layers produces an efficient network for end-to-end dense learning. Herein, we took advantage of the fact that a fault binary image is highly biased with mostly zeros but only very limited ones on the faults. A balanced crossentropy loss function was defined to adjust the imbalance for optimizing the parameters of our FCN model. Ultimately, the FCN model was applied on real field data to propose that our FCN model can outperform conventional methods in fault predictions from seismic images in a more accurate and efficient manner. Full article
(This article belongs to the Special Issue Applications of AI Tools in Petroleum Industry from Geosciences to Engineering)
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