Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Instrument Calibration01:12

Instrument Calibration

916
Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
916
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

112.1K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
112.1K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.7K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.7K
Random and Systematic Errors01:20

Random and Systematic Errors

15.6K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
15.6K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

5.1K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
5.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comparison of Allo-HSCT outcomes after CAR-T therapy versus chemotherapy in pediatric patients with relapsed/refractory B-ALL: a retrospective study.

The oncologist·2026
Same author

Molecular Subtype-Associated Response to Cyclophosphamide-Epirubicin-Cisplatin Regimen in Recurrent or Metastatic Adenoid Cystic Carcinoma: A Retrospective Single-Center Study.

Cancers·2026
Same author

Intravenous amivantamab after cetuximab failure in recurrent or metastatic head and neck squamous cell carcinoma: a single-centre retrospective real-world cohort study.

Oral oncology·2026
Same author

Radiomics: Current Applications and Future Directions.

MedComm·2026
Same author

Efficacy and safety of toripalimab in combination with cetuximab in patients with recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC): a phase 1b/2 study.

Signal transduction and targeted therapy·2026
Same author

Shifting dysmenorrhea management from treatment to prevention: the critical adolescent window.

Frontiers in public health·2026

Related Experiment Video

Updated: Mar 1, 2026

Stress Distribution During Cold Compression of Rocks and Mineral Aggregates Using Synchrotron-based X-Ray Diffraction
10:36

Stress Distribution During Cold Compression of Rocks and Mineral Aggregates Using Synchrotron-based X-Ray Diffraction

Published on: May 20, 2018

10.1K

Research on calibrating rock mechanical parameters with a statistical method.

Zhen Liu1,2, Ye Guo3, Shuheng Du2

  • 1The Key Laboratory of Orogenic Belts and Crustal Evolution (MOE), School of Earth and Space Sciences, Peking University, Beijing, China.

Plos One
|May 26, 2017
PubMed
Summary

This study calibrates rock mechanical models using statistical analysis of logging data. The new method enhances accuracy in oil and gas exploration, improving reservoir prediction.

More Related Videos

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples
10:12

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

Published on: June 19, 2018

9.6K
Atomic Force Microscopy Cantilever-Based Nanoindentation: Mechanical Property Measurements at the Nanoscale in Air and Fluid
08:58

Atomic Force Microscopy Cantilever-Based Nanoindentation: Mechanical Property Measurements at the Nanoscale in Air and Fluid

Published on: December 2, 2022

3.8K

Related Experiment Videos

Last Updated: Mar 1, 2026

Stress Distribution During Cold Compression of Rocks and Mineral Aggregates Using Synchrotron-based X-Ray Diffraction
10:36

Stress Distribution During Cold Compression of Rocks and Mineral Aggregates Using Synchrotron-based X-Ray Diffraction

Published on: May 20, 2018

10.1K
Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples
10:12

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

Published on: June 19, 2018

9.6K
Atomic Force Microscopy Cantilever-Based Nanoindentation: Mechanical Property Measurements at the Nanoscale in Air and Fluid
08:58

Atomic Force Microscopy Cantilever-Based Nanoindentation: Mechanical Property Measurements at the Nanoscale in Air and Fluid

Published on: December 2, 2022

3.8K

Area of Science:

  • Geosciences
  • Petroleum Engineering
  • Computational Modeling

Background:

  • Rock mechanics parameter modeling is crucial for oil and gas exploration.
  • Kriging interpolation of logging data aids reservoir prediction but faces challenges with limited samples and heterogeneity.
  • Existing methods may yield significant deviations in rock mechanics parameter estimations.

Purpose of the Study:

  • To address deviations in rock mechanics modeling caused by limited samples and high heterogeneity.
  • To develop and validate a novel approach for calibrating rock mechanical models using statistical analysis of logging data.
  • To improve the accuracy of rock mechanics parameter estimation in oilfield applications.

Main Methods:

  • Statistical analysis of well logging data.
  • Development of an automated module for calibrating rock mechanics parameters.
  • Application of the developed module to a specific oilfield area (Wangyao, Ansai oilfield).

Main Results:

  • A new approach for calibrating rock mechanical models was successfully developed.
  • An automated module for parameter calibration was created and implemented.
  • The application of the method in the Wangyao area demonstrated a significant improvement in modeling accuracy.

Conclusions:

  • The proposed statistical calibration method effectively enhances the accuracy of rock mechanics modeling.
  • The automated module provides a reliable tool for improving reservoir characterization and prediction.
  • This approach offers a valuable advancement for oil and gas exploration and development.