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Special Issue "Artificial Intelligence/Machine Learning Applications in the Oil and Gas Industry"

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

Deadline for manuscript submissions: 31 January 2023 | Viewed by 3170

Special Issue Editors

Dr. Ashkan Jahanbani Ghahfarokhi
E-Mail Website
Guest Editor
Department of Geoscience and Petroleum, Norwegian University of Science and Technology, S. P. Andersens veg 15b, 7031, Trondheim, Norway
Interests: reservoir modeling; enhanced oil recovery (EOR); CO2 EOR and storage; well test analysis
Dr. Alv-Arne Grimstad
E-Mail Website
Guest Editor
SINTEF Industry, Department of Petroleum, S.P. Andersens vei 15B, Trondheim, NO-7031, Norway
Interests: CO2 geological storage; CO2 enhanced oil recovery; reservoir modelling

Special Issue Information

Dear Colleagues,

We have the pleasure of inviting submissions to a Special Issue of Energies on the subject area of “Artificial Intelligence/Machine Learning Applications in the Oil and Gas Industry”.

While more efficient algorithms and high-performance computers can enhance the speed and accuracy of numerical models, they are incapable of transforming these models to new levels of computational footprint. Artificial intelligence and machine learning (AI&ML) have been used in petroleum engineering applications, with the possibility of combining the advantages of both traditional and intelligent modeling approaches to develop more powerful and faster computational tools. AI&ML capabilities perceive the relationship among relevant data and develop models based on the available measurements or simulated data. This characteristic makes the data-driven approach a viable modeling technology specifically for cases with complex physics. Promising results have been obtained in the application of data-driven techniques for resolving a wide variety of modeling problems such as history matching, well placement, production forecasting, injection strategies, CO2 storage and optimization and many more.

Dr. Ashkan Jahanbani Ghahfarokhi
Dr. Alv-Arne Grimstad
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. Energies 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 2200 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

  •  artificial intelligence
  •  machine learning
  •  data analytics
  •  petroleum engineering
  •  oil and gas industry
  •  CO2 utilization and storage
  •  reservoir engineering
  •  optimization
  •  digitalization
  •  real-time reservoir management

Published Papers (3 papers)

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Research

Article
Application of Digitalization in Real-Time Analysis of Drilling Dynamics Using Along-String Measurement (ASM) Data along Wired Pipes
Energies 2022, 15(23), 8930; https://doi.org/10.3390/en15238930 - 25 Nov 2022
Viewed by 393
Abstract
An automated drilling system requires a real-time evaluation of the drilling bit during drilling to optimize operation and determine when to stop drilling and switch bits. Furthermore, in the dynamic modeling of drill strings, it is necessary to take into account the interactions [...] Read more.
An automated drilling system requires a real-time evaluation of the drilling bit during drilling to optimize operation and determine when to stop drilling and switch bits. Furthermore, in the dynamic modeling of drill strings, it is necessary to take into account the interactions between drilling bits and rock. To address this challenge, a hybrid approach that combines physics-based models with data analytics has been developed to handle downhole drilling measurements in real time. First, experimental findings were used to formulate mathematical models of cutter–rock interaction in accordance with their geometrical characteristics, rock properties, and drilling parameters. Specifically, these models represent the normal and contact forces of polycrystalline diamond compact cutters (PDCs). Experimental data are analyzed utilizing deep learning, nonlinear regression, and genetic algorithms to fit nonlinear equations to data points. Following this, the recursive least square was implemented as a data analytic method to integrate real-time drilling data, drilling bit models, and mathematical models. Drilling data captured by the along-string measurement system (ASM) is implemented to estimate cutting and normal forces, torque, and specific energy at the bit. The unique aspect of this research is our approach in developing a detailed cutter–rock interaction model that takes all design and operation parameters into account. In addition, the applicability of the algorithm is demonstrated by real-time assessments of drilling dynamics, utilizing downhole digital data, that enable the prediction of drilling events and problems related to drilling bits. Full article
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Article
Data-Driven Proxy Models for Improving Advanced Well Completion Design under Uncertainty
Energies 2022, 15(20), 7484; https://doi.org/10.3390/en15207484 - 11 Oct 2022
Viewed by 468
Abstract
In order to improve the design of advanced wells, the performance of such wells needs to be carefully assessed by taking the reservoir uncertainties into account. This research aimed to develop data-driven proxy models for the simulation and assessment of oil recovery through [...] Read more.
In order to improve the design of advanced wells, the performance of such wells needs to be carefully assessed by taking the reservoir uncertainties into account. This research aimed to develop data-driven proxy models for the simulation and assessment of oil recovery through advanced wells under uncertainty. An artificial neural network (ANN) was employed to create accurate and computationally efficient proxy models as an alternative to physics-based integrated well–reservoir models created by the Eclipse® reservoir simulator. The simulation speed and accuracy of the data-driven proxy models compared to physic-driven models were then evaluated. The evaluation showed that while the developed proxy models are 350 times faster, they can predict the production of oil and unwanted fluids through advanced wells with a mean error of less than 1% and 4%, respectively. As a result, the data-driven proxy models can be considered an efficient tool for uncertainty analysis where several simulations need to be performed to cover all possible scenarios. In this study, the developed proxy models were applied for uncertainty quantification of oil recovery from advanced wells completed with different types of downhole flow control devices (FCDs). According to the obtained results, compared to other types of well completion design, advanced wells completed with autonomous inflow control valve (AICV) technology have the best performance in limiting the production of unwanted fluids and are able to reduce the associated risk by 91%. Full article
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Article
Artificial Neural Network-Based Caprock Structural Reliability Analysis for CO2 Injection Site—An Example from Northern North Sea
Energies 2022, 15(9), 3365; https://doi.org/10.3390/en15093365 - 05 May 2022
Cited by 1 | Viewed by 941
Abstract
In CO2 sequestration projects, assessing caprock structural stability is crucial to assure the success and reliability of the CO2 injection. However, since caprock experimental data are sparse, we applied a Monte Carlo (MC) algorithm to generate stochastic data from the given [...] Read more.
In CO2 sequestration projects, assessing caprock structural stability is crucial to assure the success and reliability of the CO2 injection. However, since caprock experimental data are sparse, we applied a Monte Carlo (MC) algorithm to generate stochastic data from the given mean and standard deviation values. The generated data sets were introduced to a neural network (NN), including four hidden layers for classification purposes. The model was then used to evaluate organic-rich Draupne caprock shale failure in the Alpha structure, northern North Sea. The train and test were carried out with 75% and 25% of the input data, respectively. Following that, validation is accomplished with unseen data, yielding promising classification scores. The results show that introducing larger input data sizes to the established NN provides better convergence conditions and higher classification scores. Although the NN can predicts the failure states with a classification score of 97%, the structural reliability was significantly low compare to the failure results estimated using other method. Moreover, this indicated that during evaluating the field-scale caprock failure, more experimental data is needed for a reliable result. However, this study depicts the advantage of machine learning algorithms in geological CO2 storage projects compared with similar finite elements methods in the aspect of short fitting time, high accuracy, and flexibility in processing different input data sizes with different scales. Full article
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