Using Bayesian Network to Develop Drilling Expert Systems
| dc.contributor | Schubert, Jerome J. | |
| dc.creator | Alyami, Abdullah | |
| dc.date.accessioned | 2014-11-03T19:49:13Z | |
| dc.date.accessioned | 2017-04-07T20:00:42Z | |
| dc.date.available | 2014-11-03T19:49:13Z | |
| dc.date.available | 2017-04-07T20:00:42Z | |
| dc.date.created | 2012-08 | |
| dc.date.issued | 2012-10-19 | |
| dc.description.abstract | Long years of experience in the field and sometimes in the lab are required to develop consultants. Texas A&M University recently has established a new method to develop a drilling expert system that can be used as a training tool for young engineers or as a consultation system in various drilling engineering concepts such as drilling fluids, cementing, completion, well control, and underbalanced drilling practices. This method is done by proposing a set of guidelines for the optimal drilling operations in different focus areas, by integrating current best practices through a decision-making system based on Artificial Bayesian Intelligence. Optimum practices collected from literature review and experts' opinions, are integrated into a Bayesian Network BN to simulate likely scenarios of its use that will honor efficient practices when dictated by varying certain parameters. The advantage of the Artificial Bayesian Intelligence method is that it can be updated easily when dealing with different opinions. To the best of our knowledge, this study is the first to show a flexible systematic method to design drilling expert systems. We used these best practices to build decision trees that allow the user to take an elementary data set and end up with a decision that honors the best practices. | |
| dc.identifier.uri | http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11454 | |
| dc.language.iso | en_US | |
| dc.subject | Drilling Expert System | |
| dc.subject | bayesian network | |
| dc.title | Using Bayesian Network to Develop Drilling Expert Systems | |
| dc.type | Thesis |