A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real Time; Sensors; Vol. 23, iss. 14
| Parent link: | Sensors.— .— Basel: MDPI AG Vol. 23, iss. 14.— 2023.— Article number 6594, 28 p. |
|---|---|
| Altres autors: | , , , , |
| Sumari: | Title screen When drilling deep wells, it is important to regulate the formation pressure and prevent kicks. This is achieved by controlling the equivalent circulation density (ECD), which becomes crucial in high-pressure and high-temperature wells. ECD is particularly important in formations where the pore pressure and fracture pressure are close to each other (narrow windows). However, the current methods for measuring ECD using downhole sensors can be expensive and limited by operational constraints such as high pressure and temperature. Therefore, to overcome this challenge, two novel models named ECDeffc.m and MWeffc.m were developed to predict ECD and mud weight (MW) from surface-drilling parameters, including standpipe pressure, rate of penetration, drill string rotation, and mud properties. In addition, by utilizing an artificial neural network (ANN) and a support vector machine (SVM), ECD was estimated with a correlation coefficient of 0.9947 and an average absolute percentage error of 0.23%. Meanwhile, a decision tree (DT) was employed to estimate MW with a correlation coefficient of 0.9353 and an average absolute percentage error of 1.66%. The two novel models were compared with artificial intelligence (AI) techniques to evaluate the developed models. The results proved that the two novel models were more accurate with the value obtained from pressure-while-drilling (PWD) tools. These models can be utilized during well design and while drilling operations are in progress to evaluate and monitor the appropriate mud weight and equivalent circulation density to save time and money, by eliminating the need for expensive downhole equipment and commercial software Текстовый файл |
| Idioma: | anglès |
| Publicat: |
2023
|
| Matèries: | |
| Accés en línia: | http://earchive.tpu.ru/handle/11683/132511 https://doi.org/10.3390/s23146594 |
| Format: | xMaterials Electrònic Capítol de llibre |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=680225 |
MARC
| LEADER | 00000naa0a2200000 4500 | ||
|---|---|---|---|
| 001 | 680225 | ||
| 005 | 20250917143053.0 | ||
| 090 | |a 680225 | ||
| 100 | |a 20250515d2023 k||y0rusy50 ba | ||
| 101 | 0 | |a eng | |
| 102 | |a CH | ||
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i |b e | |
| 182 | 0 | |a b | |
| 183 | 0 | |a cr |2 RDAcarrier | |
| 200 | 1 | |a A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real Time |f Mohammed Al-Rubaii, Mohammed Al-Shargabi, Bayan Aldahlawi [et al.] | |
| 203 | |a Текст |b визуальный |c электронный | ||
| 283 | |a online_resource |2 RDAcarrier | ||
| 300 | |a Title screen | ||
| 320 | |a References: 73 tit | ||
| 330 | |a When drilling deep wells, it is important to regulate the formation pressure and prevent kicks. This is achieved by controlling the equivalent circulation density (ECD), which becomes crucial in high-pressure and high-temperature wells. ECD is particularly important in formations where the pore pressure and fracture pressure are close to each other (narrow windows). However, the current methods for measuring ECD using downhole sensors can be expensive and limited by operational constraints such as high pressure and temperature. Therefore, to overcome this challenge, two novel models named ECDeffc.m and MWeffc.m were developed to predict ECD and mud weight (MW) from surface-drilling parameters, including standpipe pressure, rate of penetration, drill string rotation, and mud properties. In addition, by utilizing an artificial neural network (ANN) and a support vector machine (SVM), ECD was estimated with a correlation coefficient of 0.9947 and an average absolute percentage error of 0.23%. Meanwhile, a decision tree (DT) was employed to estimate MW with a correlation coefficient of 0.9353 and an average absolute percentage error of 1.66%. The two novel models were compared with artificial intelligence (AI) techniques to evaluate the developed models. The results proved that the two novel models were more accurate with the value obtained from pressure-while-drilling (PWD) tools. These models can be utilized during well design and while drilling operations are in progress to evaluate and monitor the appropriate mud weight and equivalent circulation density to save time and money, by eliminating the need for expensive downhole equipment and commercial software | ||
| 336 | |a Текстовый файл | ||
| 461 | 1 | |t Sensors |c Basel |n MDPI AG | |
| 463 | 1 | |t Vol. 23, iss. 14 |v Article number 6594, 28 p. |d 2023 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a equivalent circulating density | |
| 610 | 1 | |a mud weight | |
| 610 | 1 | |a artificial intelligence | |
| 610 | 1 | |a drilling efficiency | |
| 610 | 1 | |a support vector machine | |
| 610 | 1 | |a artificial neural network | |
| 610 | 1 | |a decision tree | |
| 701 | 1 | |a Al-Rubaii |b M. |g Mohammed | |
| 701 | 1 | |a Al-Shargabi |b M. A. T. S. |c specialist in the field of petroleum engineering |c Engineer of Tomsk Polytechnic University |f 1993- |g Mokhammed Abdulsalam Takha Sallam |9 22768 | |
| 701 | 1 | |a Aldahlawi |b B. |g Bayan | |
| 701 | 1 | |a Al-Shehri |b D. |g Dhafer | |
| 701 | 1 | |a Minaev |b K. M. |c specialist in the field of oil and gas business |c associate Professor of Tomsk Polytechnic University, candidate of chemical Sciences |f 1982- |g Konstantin Madestovich |9 16672 | |
| 801 | 0 | |a RU |b 63413507 |c 20250515 | |
| 850 | |a 63413507 | ||
| 856 | 4 | |u http://earchive.tpu.ru/handle/11683/132511 |z http://earchive.tpu.ru/handle/11683/132511 | |
| 856 | 4 | |u https://doi.org/10.3390/s23146594 |z https://doi.org/10.3390/s23146594 | |
| 942 | |c CF | ||