Cementing is an important factor in drilling and completion operations. Good cementing practices are required for a proper advancing in drilling and production operations. Successful cementing practices start with the design of effective cement slurries. However, to the best of the authors’ knowledge, there are no standard guidelines to help drilling engineers and scientists in the effective design of optimal cement slurries to be used in different well sections.
The objective of this paper is to propose a set of guidelines for the optimal design of cement slurries, by integrating current best practices through a decision-making system based on Artificial Bayesian Intelligence. Best cementing 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 designs when dictated by varying well objectives, well types, temperatures, pressures, and, drilling fluids.
The proposed decision-making model follows a causal and an uncertainty-based approach capable of simulating realistic conditions on the use of cement slurries during drilling and completion operations. For instance, well sections and drilling operations dictate the use of the proper cement design which may include the use of specific additives according to the particular modeling scenarios. These include operations on surface casing, top jobs, intermediate casings, cementing in weak formations, squeeze treatment, kickoff and isolation plugs, horizontal, and vertical completions, among others. Potential operational problems that can lead to cementing failures are also discussed. Different methods of investigation and recommendations are presented in detail.