| dc.contributor.author | Jung Chan Choi | |
| dc.contributor.author | Zhongqiang Liu | |
| dc.contributor.author | Suzanne Lacasse | |
| dc.contributor.author | Elin Skurtveit | |
| dc.contributor.other | Norwegian Geotechnical Institute, 3930 Oslo, Norway | |
| dc.contributor.other | Norwegian Geotechnical Institute, 3930 Oslo, Norway | |
| dc.contributor.other | Norwegian Geotechnical Institute, 3930 Oslo, Norway | |
| dc.contributor.other | Norwegian Geotechnical Institute, 3930 Oslo, Norway | |
| dc.date.accessioned | 2025-10-09T05:20:21Z | |
| dc.date.available | 2025-10-09T05:20:21Z | |
| dc.date.issued | 01-04-2021 | |
| dc.identifier.uri | https://www.mdpi.com/2076-3263/11/4/181 | |
| dc.identifier.uri | http://digilib.fisipol.ugm.ac.id/repo/handle/15717717/40973 | |
| dc.description.abstract | Leak-off pressure (LOP) is a key parameter to determine the allowable weight of drilling mud in a well and the in situ horizontal stress. The LOP test is run in situ and is frequently used by the petroleum industry. If the well pressure exceeds the LOP, wellbore instability may occur, with hydraulic fracturing and large mud losses in the formation. A reliable prediction of LOP is required to ensure safe and economical drilling operations. The prediction of LOP is challenging because it is affected by the usually complex earlier geological loading history, and the values of LOP and their measurements can vary significantly geospatially. This paper investigates the ability of machine learning algorithms to predict leak-off pressure on the basis of geospatial information of LOP measurements. About 3000 LOP test data were collected from 1800 exploration wells offshore Norway. Three machine learning algorithms (the deep neural network (DNN), random forest (RF), and support vector machine (SVM) algorithms) optimized by three hyperparameter search methods (the grid search, randomized search and Bayesian search) were compared with multivariate regression analysis. The Bayesian search algorithm needed fewer iterations than the grid search algorithms to find an optimal combination of hyperparameters. The three machine learning algorithms showed better performance than the multivariate linear regression when the features of the geospatial inputs were properly scaled. The RF algorithm gave the most promising results regardless of data scaling. If the data were not scaled, the DNN and SVM algorithms, even with optimized parameters, did not provide significantly improved test scores compared to the multivariate regression analysis. The analyses also showed that when the number of data points in a geographical setting is much smaller than that of other geographical areas, the prediction accuracy reduces significantly. | |
| dc.language.iso | EN | |
| dc.publisher | MDPI AG | |
| dc.subject.lcc | Geology | |
| dc.title | Leak-Off Pressure Using Weakly Correlated Geospatial Information and Machine Learning Algorithms | |
| dc.type | Article | |
| dc.description.keywords | leak-off pressure | |
| dc.description.keywords | machine learning | |
| dc.description.keywords | hyperparameter optimization | |
| dc.description.keywords | geospatial information | |
| dc.description.doi | 10.3390/geosciences11040181 | |
| dc.title.journal | Geosciences | |
| dc.identifier.e-issn | 2076-3263 | |
| dc.identifier.oai | oai:doaj.org/journal:992c737860dd48e89158224cb7e5c8b0 | |
| dc.journal.info | Volume 11, Issue 4 | |
