{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T02:57:48Z","timestamp":1780455468175,"version":"3.54.1"},"reference-count":25,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2020,9,22]]},"abstract":"<jats:p>Drilling a high-pressure, high-temperature (HPHT) well involves many difficulties and challenges. One of the greatest difficulties is the loss of circulation. Almost 40% of the drilling cost is attributed to the drilling fluid, so the loss of the fluid considerably increases the total drilling cost. There are several approaches to avoid loss of return; one of these approaches is preventing the occurrence of the losses by identifying the lost circulation zones. Most of these approaches are difficult to apply due to some constraints in the field. The purpose of this work is to apply three artificial intelligence (AI) techniques, namely, functional networks (FN), artificial neural networks (ANN), and fuzzy logic (FL), to identify the lost circulation zones. Real-time surface drilling parameters of three wells were obtained using real-time drilling sensors. Well A was utilized for training and testing the three developed AI models, whereas Well B and Well C were utilized to validate them. High accuracy was achieved by the three AI models based on the root mean square error (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mtext>RMSE<\/mml:mtext><\/mml:math>), confusion matrix, and correlation coefficient (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mi>R<\/mml:mi><\/mml:math>). All the AI models identified the lost circulation zones in Well A with high accuracy where the <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mi>R<\/mml:mi><\/mml:math> is more than 0.98 and <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mtext>RMSE<\/mml:mtext><\/mml:math> is less than 0.09. ANN is the most accurate model with <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\"><mml:mi>R<\/mml:mi><mml:mo>=<\/mml:mo><mml:mn>0.99<\/mml:mn><\/mml:math> and <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M6\"><mml:mtext>RMSE<\/mml:mtext><mml:mo>=<\/mml:mo><mml:mn>0.05<\/mml:mn><\/mml:math>. An ANN was able to predict the lost circulation zones in the unseen Well B and Well C with <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M7\"><mml:mi>R<\/mml:mi><mml:mo>=<\/mml:mo><mml:mn>0.946<\/mml:mn><\/mml:math> and <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M8\"><mml:mtext>RMSE<\/mml:mtext><mml:mo>=<\/mml:mo><mml:mn>0.165<\/mml:mn><\/mml:math> and <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M9\"><mml:mi>R<\/mml:mi><mml:mo>=<\/mml:mo><mml:mn>0.952<\/mml:mn><\/mml:math> and <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M10\"><mml:mtext>RMSE<\/mml:mtext><mml:mo>=<\/mml:mo><mml:mn>0.155<\/mml:mn><\/mml:math>, respectively.<\/jats:p>","DOI":"10.1155\/2020\/8851065","type":"journal-article","created":{"date-parts":[[2020,9,23]],"date-time":"2020-09-23T00:20:14Z","timestamp":1600820414000},"page":"1-18","source":"Crossref","is-referenced-by-count":10,"title":["Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors"],"prefix":"10.1155","volume":"2020","author":[{"given":"Abdulmalek","family":"Ahmed","sequence":"first","affiliation":[{"name":"College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7209-3715","authenticated-orcid":true,"given":"Salaheldin","family":"Elkatatny","sequence":"additional","affiliation":[{"name":"College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulwahab","family":"Ali","sequence":"additional","affiliation":[{"name":"Center of Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmoud","family":"Abughaban","sequence":"additional","affiliation":[{"name":"Saudi Aramco, EXPEC ARC, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulazeez","family":"Abdulraheem","sequence":"additional","affiliation":[{"name":"College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"issue":"3","key":"6","first-page":"4","volume":"26","year":"2014","journal-title":"Oilfield 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