The use of artificial Bayesian intelligence in practical well control Conference Paper uri icon


  • Many well control incidents have been analyzed, resulting in the optimum practices, as outlined in this paper. The objective of this paper is to propose a set of guidelines for the optimal well control operations, by integrating current best practices through a decision-making system based on Artificial Bayesian Intelligence. Best well control practices collected from data, models, and experts' opinions, are integrated into a Bayesian Network to simulate likely scenarios of its use that will honor efficient practices. When dictated by varying operation, kick details, and kick severity. The proposed decision-making model follows a causal and an uncertainty-based approach capable of simulating realistic conditions on the use of well control operations. For instance, by varying the operation, the system will show the kick indicators for that particular operation. Also in the same model as the user vary the operation, rig and crew capabilities, kick details (such as slim hole, deviated or horizontal well), the system will show the optimum practices for circulation method and shut in method. The model also shows optimum practices for blowout control by varying the un-controlled kick type (surface, subsurface or underground blowouts). Recommended practices after controlling the well are shown by the same operation that caused the well control incident and by varying the potential reason for the incident. Two well control experts' opinions were considered in building up the model in this paper. The advantage of the artificial Bayesian intelligence method is that it can be updated easily when dealing with different opinions. The outcome of this paper is user-friendly software, where you can easily find the specific subject of interest, and by the click of a button, get the related information you are seeking. Field cases will also be discussed to validate this work. Copyright 2012, Society of Petroleum Engineers.

author list (cited authors)

  • Al-Yami, A. S., & Schubert, J.

publication date

  • December 2012