📄

Paper

Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties

by Independent / Community 002672661e7976ee10ff6e81ebfa55606d4f7e09

Free2AITools Nexus Index

58.6

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 58

P: Popularity 35

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Rate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish the well as soon as possible to reduce the expenditures. So, knowing the rate of penetration in the area that is going to be drilled will help in speculation of the cost and that will lead to optimi...

Source →

Semantic Scholar 1 Citations

Paper Information Summary
Entity Passport
Registry ID	002672661e7976ee10ff6e81ebfa55606d4f7e09
License	ArXiv
Provider	semantic_scholar

📜

Cite this paper

Academic & Research Attribution

BibTeX

@misc{002672661e7976ee10ff6e81ebfa55606d4f7e09,
  author = {Unknown},
  title = {Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/002672661e7976ee10ff6e81ebfa55606d4f7e09}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties [Paper]. Free2AITools. https://api.semanticscholar.org/002672661e7976ee10ff6e81ebfa55606d4f7e09

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 58

Popularity (P) 35

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties: Authority (A:58), Popularity (P:35), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"Rate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish the well as soon as possible to reduce the expenditures. So, knowing the rate of penetration in the area that is going to be drilled will help in speculation of the cost and that will lead to optimi..."

❝ Cite Node

@article{Unknown2026Using,
  title={Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📈1CitationsSemantic Scholar

🏛️58AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️infrastructure opsField

📦Data Source: semantic_scholar

🔄 Updated daily

Source summary: Based on semantic_scholar metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: null
forks: null
citations: 1

Data indexed from public sources. Updated daily.