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Applied Sciences
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Digital Technologies in the Petroleum Industry
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Digital Technologies in the Petroleum Industry

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (20 August 2021) | Viewed by 7362

Special Issue Editor

Prof. Dr. Kun Sang Lee

E-Mail Website
Guest Editor
Department of Earth Resources and Environmental Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: reservoir simulation; enhanced oil recovery; shale reservoir; CO2 storage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the digital revolution swept through many industries, it has disrupted established business models. To reduce costs markedly during the period of stabilized oil and gas prices, people in the E&P industry are now developing technologies to make a much more efficient and streamlined industry. There are several different use cases for digital technologies that address a wide spectrum of applications available today or nearing commercial application. The recent development of digital technologies during the past decade is beginning to change the future of the industry over the next decade.

The main objective of this Special Issue is to seek research papers to accelerate the development and application of digital technologies in various areas of the petroleum industry. Related technologies include but are not limited to the following subjects:

  • Artificial intelligence and automation: planning, forecasting, and improving safety in the oil and gas industry from surveying and continuous monitoring.
  • Big data and data analytics: analysis of 3D seismic surveys, drilling data, production data, or the monitoring of production facilities.
  • Internet of things and electronic monitoring: collection and analysis of real-time data on machinery, pipes, storage, transportation, and employee safety with IoT devices and sensors.
  • 3D virtual modelling: accurate, high-resolution 3D digital models for the monitoring and inspection of both inaccessible and/or dangerous oil and gas facilities.

Prof. Dr. Kun Sang Lee
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • digital technology
  • artificial intelligence
  • big data and data analytics
  • internet of things
  • virtual modelling
  • petroleum industry

Published Papers (4 papers)

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Research

16 pages, 69240 KiB  
Article
Multi-Objective Optimization of CO2 Sequestration in Heterogeneous Saline Aquifers under Geological Uncertainty
by Changhyup Park, Jaehwan Oh, Suryeom Jo, Ilsik Jang and Kun Sang Lee
Appl. Sci. 2021, 11(20), 9759; https://0-doi-org.brum.beds.ac.uk/10.3390/app11209759 - 19 Oct 2021
Cited by 2 | Viewed by 1928
Abstract
This paper presents a Pareto-based multi-objective optimization for operating CO2 sequestration with a multi-well system under geological uncertainty; the optimal well allocation, i.e., the optimal allocation of CO2 rates at injection wells, is obtained when there is minimum operation pressure as [...] Read more.
This paper presents a Pareto-based multi-objective optimization for operating CO2 sequestration with a multi-well system under geological uncertainty; the optimal well allocation, i.e., the optimal allocation of CO2 rates at injection wells, is obtained when there is minimum operation pressure as well as maximum sequestration efficiency. The distance-based generalized sensitivity analysis evaluates the influence of geological uncertainty on the amount of CO2 sequestration through four injection wells at 3D heterogeneous saline aquifers. The spatial properties significantly influencing the trapping volume, in descending order of influence, are mean sandstone porosity, mean sandstone permeability, shale volume ratio, and the Dykstra–Parsons coefficient of permeability. This confirms the importance of storable capacity and heterogeneity in quantitatively analyzing the trapping mechanisms. Multi-objective optimization involves the use of two aquifer models relevant to heterogeneity; one is highly heterogeneous and the other is less so. The optimal well allocations converge to non-dominated solutions and result in a large injection through one specific well, which generates the wide spread of a highly mobile CO2 plume. As the aquifer becomes heterogeneous with a large shale volume and a high Dykstra–Parsons coefficient, the trapping performances of the combined structural and residual sequestration plateau relatively early. The results discuss the effects of spatial heterogeneity on achieving CO2 geological storage, and they provide an operation strategy including multi-objective optimization. Full article
(This article belongs to the Special Issue Digital Technologies in the Petroleum Industry)
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18 pages, 7393 KiB  
Article
Quantitative Interpretation of TOC in Complicated Lithology Based on Well Log Data: A Case of Majiagou Formation in the Eastern Ordos Basin, China
by Shuiqing Hu, Haowei Zhang, Rongji Zhang, Lingxuan Jin and Yuming Liu
Appl. Sci. 2021, 11(18), 8724; https://0-doi-org.brum.beds.ac.uk/10.3390/app11188724 - 18 Sep 2021
Cited by 3 | Viewed by 1821
Abstract
Source rock evaluation plays a key role in studies of hydrocarbon accumulation and resource potential. Total organic carbon (TOC) is the basis of source rock evaluation and it is a key parameter that influences petroleum resource assessment. The Majiagou formation in the eastern [...] Read more.
Source rock evaluation plays a key role in studies of hydrocarbon accumulation and resource potential. Total organic carbon (TOC) is the basis of source rock evaluation and it is a key parameter that influences petroleum resource assessment. The Majiagou formation in the eastern Ordos Basin has complicated lithology and low abundance of organic matters. There are different opinions over the existence of scale source rocks. Due to inadequate laboratory data of TOC in the Ordos Basin, it is difficult to accurately describe source rocks in the region; thus, log interpretation of TOC is needed. In this study, the neural network model in the artificial intelligence (AI) field was introduced into the TOC logging interpretation. Compared with traditional ΔlogR methods, sample optimization, logging correlation analysis and comparative optimization of computational methods were carried out successively by using measured TOC data and logging data. Results show that the neural network model has good prediction effect in complicated lithologic regions and it can identify variations of TOC in continuous strata accurately regardless of the quick lithologic changes. Full article
(This article belongs to the Special Issue Digital Technologies in the Petroleum Industry)
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11 pages, 1279 KiB  
Article
Research on Prediction of Movable Fluid Percentage in Unconventional Reservoir Based on Deep Learning
by Jiuxin Wang, Yutian Luo, Zhengming Yang, Xinli Zhao and Zhongkun Niu
Appl. Sci. 2021, 11(8), 3589; https://0-doi-org.brum.beds.ac.uk/10.3390/app11083589 - 16 Apr 2021
Viewed by 1191
Abstract
In order to improve the measurement speed and prediction accuracy of unconventional reservoir parameters, the deep neural network (DNN) is used to predict movable fluid percentage of unconventional reservoirs. The Adam optimizer is used in the DNN model to ensure the stability and [...] Read more.
In order to improve the measurement speed and prediction accuracy of unconventional reservoir parameters, the deep neural network (DNN) is used to predict movable fluid percentage of unconventional reservoirs. The Adam optimizer is used in the DNN model to ensure the stability and accuracy of the model in the gradient descent process, and the prediction effect is compared with the back propagation neural network (BPNN), K-nearest neighbor (KNN), and support vector regression model (SVR). During network training, L2 regularization is used to avoid over-fitting and improve the generalization ability of the model. Taking nuclear magnetic resonance (NMR) T2 spectrum data of laboratory unconventional core as input features, the influence of model hyperparameters on the prediction accuracy of reservoir movable fluids is also experimentally analyzed. Experimental results show that, compared with BPNN, KNN, and SVR, the deep neural network model has a better prediction effect on movable fluid percentage of unconventional reservoirs; when the model depth is five layers, the prediction accuracy of movable fluid percentage reaches the highest value, the predicted value of the DNN model is in high agreement with the laboratory measured value. Therefore, the movable fluid percentage prediction model of unconventional oil reservoirs based on the deep neural network model can provide certain guidance for the intelligent development of the laboratory’s reservoir parameter measurement. Full article
(This article belongs to the Special Issue Digital Technologies in the Petroleum Industry)
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13 pages, 4763 KiB  
Article
Depth-Extrapolation-Based True-Amplitude Full-Wave-Equation Migration from Topography
by Hao Liu and Xuewei Liu
Appl. Sci. 2021, 11(7), 3010; https://0-doi-org.brum.beds.ac.uk/10.3390/app11073010 - 27 Mar 2021
Cited by 2 | Viewed by 1373
Abstract
The lack of an initial condition is one of the major challenges in full-wave-equation depth extrapolation. This initial condition is the vertical partial derivative of the surface wavefield and cannot be provided by the conventional seismic acquisition system. The traditional solution is to [...] Read more.
The lack of an initial condition is one of the major challenges in full-wave-equation depth extrapolation. This initial condition is the vertical partial derivative of the surface wavefield and cannot be provided by the conventional seismic acquisition system. The traditional solution is to use the wavefield value of the surface to calculate the vertical partial derivative by assuming that the surface velocity is constant. However, for seismic exploration on land, the surface velocity is often not uniform. To solve this problem, we propose a new method for calculating the vertical partial derivative from the surface wavefield without making any assumptions about the surface conditions. Based on the calculated derivative, we implemented a depth-extrapolation-based full-wave-equation migration from topography using the direct downward continuation. We tested the imaging performance of our proposed method with several experiments. The results of the Marmousi model experiment show that our proposed method is superior to the conventional reverse time migration (RTM) algorithm in terms of imaging accuracy and amplitude-preserving performance at medium and deep depths. In the Canadian Foothills model experiment, we proved that our method can still accurately image complex structures and maintain amplitude under topographic scenario. Full article
(This article belongs to the Special Issue Digital Technologies in the Petroleum Industry)
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