Matches in SemOpenAlex for { <https://semopenalex.org/work/W2895978636> ?p ?o ?g. }
- W2895978636 endingPage "1578" @default.
- W2895978636 startingPage "1569" @default.
- W2895978636 abstract "The equivalent circulation density (ECD) is a very important parameter in drilling high-pressure high-temperature and deepwater wells. ECD is a key parameter during drilling through formations where the margin between the pore pressure and the fracture pressure (FP) is narrow. In these critical formations, the ECD is used to control the formation pressure and prevent kicks. Recent approaches in oilfields to determine ECD depend mainly on using expensive downhole sensors for providing real-time values of ECD. Most of these tools have operational limitations such as high pressure and high temperature which may prevent using these tools in downhole conditions. The objective of this paper is to develop a new approach for predicting ECD using artificial intelligence (AI) techniques from surface drilling parameters [mud weight, drill pipe pressure, and rate of penetration (ROP)]. 2376 data points were used to develop the AI models. The data were collected during the drilling of an 8.5″ vertical hole section. Two AI models were used to estimate the ECD: artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). An empirical correlation for ECD was derived from the optimized ANN model by extracting the weights and biases. The developed ANN and ANFIS models were able to calculate ECD with a correlation coefficient (R) of 0.99 and average absolute percentage error of 0.22% for ANN and ANFIS models, respectively. The developed empirical correlation for the ANN model can be used during well design to choose a correct mud weight to safely drill the well based on the expected drilling parameters. It will also minimize the drilling problems related to ECD such as losses or gains especially in critical situations where the margin between the pore and fracture pressure is very narrow. In addition, using this technique will save cost and time by reducing the need for expensive, complicated downhole tools." @default.
- W2895978636 created "2018-10-26" @default.
- W2895978636 creator A5046127560 @default.
- W2895978636 creator A5046818830 @default.
- W2895978636 creator A5049649985 @default.
- W2895978636 creator A5053834729 @default.
- W2895978636 creator A5061769780 @default.
- W2895978636 creator A5087794785 @default.
- W2895978636 date "2018-10-23" @default.
- W2895978636 modified "2023-10-13" @default.
- W2895978636 title "New approach to evaluate the equivalent circulating density (ECD) using artificial intelligence techniques" @default.
- W2895978636 cites W1834844660 @default.
- W2895978636 cites W1971046155 @default.
- W2895978636 cites W1973491918 @default.
- W2895978636 cites W1978508948 @default.
- W2895978636 cites W1980880956 @default.
- W2895978636 cites W1984760612 @default.
- W2895978636 cites W1996467797 @default.
- W2895978636 cites W2005771380 @default.
- W2895978636 cites W2012867977 @default.
- W2895978636 cites W2012883561 @default.
- W2895978636 cites W2019207321 @default.
- W2895978636 cites W2023534587 @default.
- W2895978636 cites W2036045374 @default.
- W2895978636 cites W2039700271 @default.
- W2895978636 cites W2040411177 @default.
- W2895978636 cites W2044165336 @default.
- W2895978636 cites W2051551431 @default.
- W2895978636 cites W2061636817 @default.
- W2895978636 cites W2067351301 @default.
- W2895978636 cites W2077009979 @default.
- W2895978636 cites W2078686865 @default.
- W2895978636 cites W2084403904 @default.
- W2895978636 cites W2085937536 @default.
- W2895978636 cites W2101927907 @default.
- W2895978636 cites W2116280993 @default.
- W2895978636 cites W2133360658 @default.
- W2895978636 cites W2136922672 @default.
- W2895978636 cites W2138845222 @default.
- W2895978636 cites W2152820512 @default.
- W2895978636 cites W2226390861 @default.
- W2895978636 cites W2244110959 @default.
- W2895978636 cites W2251270867 @default.
- W2895978636 cites W2292790393 @default.
- W2895978636 cites W2312579057 @default.
- W2895978636 cites W2331967395 @default.
- W2895978636 cites W2338823636 @default.
- W2895978636 cites W2490152460 @default.
- W2895978636 cites W2505772285 @default.
- W2895978636 cites W2507060692 @default.
- W2895978636 cites W2508316915 @default.
- W2895978636 cites W2551473233 @default.
- W2895978636 cites W2580773761 @default.
- W2895978636 cites W2580884505 @default.
- W2895978636 cites W2616765092 @default.
- W2895978636 cites W2619365378 @default.
- W2895978636 cites W2756943920 @default.
- W2895978636 cites W2786052368 @default.
- W2895978636 cites W2787249892 @default.
- W2895978636 doi "https://doi.org/10.1007/s13202-018-0572-y" @default.
- W2895978636 hasPublicationYear "2018" @default.
- W2895978636 type Work @default.
- W2895978636 sameAs 2895978636 @default.
- W2895978636 citedByCount "28" @default.
- W2895978636 countsByYear W28959786362019 @default.
- W2895978636 countsByYear W28959786362020 @default.
- W2895978636 countsByYear W28959786362021 @default.
- W2895978636 countsByYear W28959786362022 @default.
- W2895978636 countsByYear W28959786362023 @default.
- W2895978636 crossrefType "journal-article" @default.
- W2895978636 hasAuthorship W2895978636A5046127560 @default.
- W2895978636 hasAuthorship W2895978636A5046818830 @default.
- W2895978636 hasAuthorship W2895978636A5049649985 @default.
- W2895978636 hasAuthorship W2895978636A5053834729 @default.
- W2895978636 hasAuthorship W2895978636A5061769780 @default.
- W2895978636 hasAuthorship W2895978636A5087794785 @default.
- W2895978636 hasBestOaLocation W28959786361 @default.
- W2895978636 hasConcept C119857082 @default.
- W2895978636 hasConcept C127313418 @default.
- W2895978636 hasConcept C127413603 @default.
- W2895978636 hasConcept C128990827 @default.
- W2895978636 hasConcept C152068911 @default.
- W2895978636 hasConcept C154945302 @default.
- W2895978636 hasConcept C173736775 @default.
- W2895978636 hasConcept C186108316 @default.
- W2895978636 hasConcept C187320778 @default.
- W2895978636 hasConcept C195975749 @default.
- W2895978636 hasConcept C25197100 @default.
- W2895978636 hasConcept C2776088076 @default.
- W2895978636 hasConcept C2776497017 @default.
- W2895978636 hasConcept C2780092901 @default.
- W2895978636 hasConcept C41008148 @default.
- W2895978636 hasConcept C50644808 @default.
- W2895978636 hasConcept C58166 @default.
- W2895978636 hasConcept C78519656 @default.
- W2895978636 hasConcept C78762247 @default.
- W2895978636 hasConcept C80019733 @default.
- W2895978636 hasConceptScore W2895978636C119857082 @default.