Underbalanced Drilling Expert System Development

Abstract One way to mitigate formation damage is to design and execute underbalanced drilling in all phases of operations such as drilling, tripping and completion. Field cases of underbalanced drilling failure showed high formation damage which motivated the need of expert systems in underbalanced drilling to achieve higher production rates. Many underbalanced drilling operations have been analyzed, resulting in the optimum practices, as outlined in this paper. To the best of the authors' knowledge, there are no systematic guidelines for underbalanced drilling. The objective of this paper is to propose a set of guidelines for the optimal underbalanced drilling operations, by integrating current best practices through a decision-making system based on Artificial Bayesian Intelligence. Optimum underbalanced drilling practices collected from data, models, 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 proposed decision-making model follows a causal and an uncertainty-based approach capable of simulating realistic conditions on the use of underbalanced drilling operations. For instance, by varying the type of UBD (flow, aerated, etc), operation and formation properties the system will show optimum tripping and connection procedure. The developed model also acknowledged UBD drilling techniques in different scenarios such as fractured formations, low permeability and high permeability formations. The model also shows optimum solutions to problems related to underbalance drilling such as well control, completion, drilling multiple reservoirs with different pressures, equipment associated with drilling. The advantage of the artificial Bayesian intelligence method is that it can be updated easily when dealing with different opinions.