Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Utilization of Artificial Intelligence by Students in Interdisciplinary Field of Biomedical Engineering
Shigehiro Hashimoto
(pages: 1-5)

Transdisciplinary Applications of Data Visualization and Data Mining Techniques as Represented for Human Diseases
Richard S. Segall
(pages: 6-15)

Beyond Status Quo: Why is Transdisciplinary Communication Instrumental in Innovation?
James Lipuma, Cristo Leon
(pages: 16-20)

How We Can Locate Validatable Foundations of Life Themes
Jeremy Horne
(pages: 21-32)

Bringing Discipline into Transdisciplinary Communications -The ISO 56000 Family of Innovation Standards-
Rick Fernandez, William Swart
(pages: 33-39)

To AI Is Human: How AI Tools with Their Imperfections Enhance Learning
Martin Cwiakala
(pages: 40-46)

Knowledge, Learning and Transdisciplinary Communication in the Evolution of the Contemporary World
Rita Micarelli, Giorgio Pizziolo
(pages: 47-52)

Human Complexity vs. Machine Linearity: Tug-of-War Between Two Realities Coexisting in Precarious Balance
Paolo Barile, Clara Bassano, Paolo Piciocchi
(pages: 53-62)

A Cybernetic Metric Approach to Course Preparation
Russell Jay Hendel
(pages: 63-70)

The Impact of Artificial Intelligence on Education
John Jenq
(pages: 71-76)

Bridging the Gap: Harnessing the Power of Machine Learning and Big Data for Media Research
Li-jing Arthur Chang
(pages: 77-84)

Image Processing, Computer Vision, Data Visualization, and Data Mining for Transdisciplinary Visual Communication: What Are the Differences and Which Should or Could You Use?
Richard S. Segall
(pages: 85-92)

Identification – The Essence of Education
Jeremy Horne
(pages: 93-99)

The Greek-Roman Theatre in the Mediterranean Area
Maria Rosaria D’acierno Canonici Cammino
(pages: 100-108)

Examination of AI and Conventional Teaching Approaches in Cultivating Critical Thinking Skills in High School Students
Luis Castillo
(pages: 109-112)

Thoughts, Labyrinths, and Torii
Maurício Vieira Kritz
(pages: 113-119)

Can Two Human Intelligences (HIs or Noes) and Two Artificial Intelligences (AIs) Get Involved in Interlinguistic Communication? – A Transdisciplinary Quest
Ekaterini Nikolarea
(pages: 120-128)


 

Abstracts

 


ABSTRACT


Modeling Tools for Drilling, Reservoir Navigation, and Formation Evaluation

Sushant Dutta, Fei Le, Alexandre Bespalov, Arcady Reiderman, Michael Rabinovich


The oil and gas industry routinely uses borehole tools for measuring or logging rock and fluid properties of geologic formations to locate hydrocarbons and maximize their production. Pore fluids in formations of interest are usually hydrocarbons or water. Resistivity logging is based on the fact that oil and gas have a substantially higher resistivity than water. The first resistivity log was acquired in 1927, and resistivity logging is still the foremost measurement used for drilling and evaluation. However, the acquisition and interpretation of resistivity logging data has grown in complexity over the years. Resistivity logging tools operate in a wide range of frequencies (from DC to GHz) and encounter extremely high (several orders of magnitude) conductivity contrast between the metal mandrel of the tool and the geologic formation. Typical challenges include arbitrary angles of tool inclination, full tensor electric and magnetic field measurements, and interpretation of complicated anisotropic formation properties. These challenges combine to form some of the most intractable computational electromagnetic problems in the world. Reliable, fast, and convenient numerical modeling of logging tool responses is critical for tool design, sensor optimization, virtual prototyping, and log data inversion. This spectrum of applications necessitates both depth and breadth of modeling software—from blazing fast one-dimensional (1-D) modeling codes to advanced threedimensional (3-D) modeling software, and from in-house developed codes to commercial modeling packages. In this paper, with the help of several examples, we demonstrate our approach for using different modeling software to address different drilling and evaluation applications. In one example, fast 1-D modeling provides proactive geosteering information from a deep-reading azimuthal propagation resistivity measurement. In the second example, a 3-D model with multiple vertical resistive fractures successfully explains the unusual curve separations of an array laterolog tool in a shale-gas formation. The third example uses two-dimensional (2-D) and 3-D modeling to prove the efficacy of a new borehole technology for reservoir monitoring.

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