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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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End Point Prediction: Gran Plot01:07

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Genome Annotation and Assembly03:36

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Maxam-Gilbert Sequencing01:05

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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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Related Experiment Video

Updated: May 16, 2025

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
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Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

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Well log data generation and imputation using sequence based generative adversarial networks.

Abdulrahman Al-Fakih1, A Koeshidayatullah2, Tapan Mukerji3

  • 1College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum Minerals, 31261, Dhahran, Saudi Arabia. alja2014ser@gmail.com.

Scientific Reports
|March 31, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework using generative adversarial networks (GANs) for well log data imputation and synthetic data generation. The approach enhances data reliability for hydrocarbon exploration by accurately filling data gaps and creating realistic log data.

Keywords:
Generative adversarial networks modelsSequence GAN modelsSynthetic well log data generationTime series modelsWell log data imputation

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Area of Science:

  • Geosciences
  • Petroleum Engineering
  • Data Science

Background:

  • Well log data is crucial for hydrocarbon exploration but often contains gaps and inaccuracies.
  • These data deficiencies introduce uncertainties in reservoir evaluation.
  • Effective methods for synthetic data generation and missing data imputation are essential for reliable analysis.

Purpose of the Study:

  • To develop and evaluate a novel framework for well log data generation and imputation.
  • To address challenges posed by incomplete and inaccurate well log data.
  • To improve the integrity and utility of well log data in geosciences.

Main Methods:

  • Utilized sequence-based generative adversarial networks (GANs).
  • Integrated Time Series GAN (TSGAN) for synthetic data generation and Sequence GAN (SeqGAN) for data imputation.
  • Tested the framework on a North Sea, Netherlands dataset with normalized log measurements.

Main Results:

  • The imputation method demonstrated superior accuracy in filling data gaps compared to other deep learning models.
  • Achieved high R-squared values (up to 0.92) and low Mean Absolute Error (MAE) for imputation.
  • Synthetic data generation also yielded promising results with an R-squared of 0.92.

Conclusions:

  • The proposed GAN-based framework effectively generates synthetic well log data and imputes missing values.
  • This approach significantly enhances data completeness and reliability for reservoir evaluation.
  • The study sets a new benchmark for well log data integrity in geoscientific applications.