Jerome Schubert

Gas influxes are common during drilling operations. If not handled properly, in open loop systems gas can expand and unload the riser, however in closed loop systems, unless gas is depressurized, the top section of the riser will become over pressured. Riser unloading has not been accurately modeled due to uncertainties in gas expansion. This paper presents studies on gas expansion and unloading in a lab scale model at Texas A&M University. We have performed experiments in a scaled riser at the Dual Gradient Drilling laboratory. We ran experiments using water and air as the gas phase. We recorded the change in volume as gas expands and liquid height changes. We mitigated the disproportion of the atmospheric pressure and the scale of the model by using vacuum pumps. We also measured overflow by allowing liquid level to rise to determine the final gas volume. The experiments provide information on gas expansion and riser unloading. During the expansion process the top of the bubble travels rapidly building momentum, which carries a large volume of mud to the top of the riser model. This occurs through the rapid change in gas volume and the energy released as the pressure decreases. By allowing gas to expand in a controlled environment we measured the impact of gas expansion. This paper provides information on controlled and uncontrolled gas expansion, impact on riser unloading, and benefits of a closed system.

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.