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Khvostichenko et al., 2023 - Google Patents

Apples to Apples: Impartial Assessment of Drilling Technologies Through Big Data and Machine Learning

Khvostichenko et al., 2023

Document ID
2831892963218496243
Author
Khvostichenko D
Skoff G
Arevalo Y
Makarychev-Mikhailov S
Publication year
Publication venue
SPE/IADC Drilling Conference and Exhibition

External Links

Snippet

Ensuring a proper apple to apple comparison is a challenge in drilling performance evaluation. When assessing the effect of a particular drilling technology, such as bit, bottomhole assembly (BHA) or mud type, on the rate of penetration (ROP) or other drilling …
Continue reading at onepetro.org (other versions)

Classifications

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    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587—Details of specialised database models
    • G06F17/30595—Relational databases
    • G06F17/30598—Clustering or classification
    • G06F17/30601—Clustering or classification including cluster or class visualization or browsing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G—PHYSICS
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    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
    • G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
    • G—PHYSICS
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