To the best of the authors' knowledge, there are no standard guidelines to help in the effective design of completion practices. Many completions have been analyzed in this project, resulting in the best practices, as outlined in this paper.
The objective of this paper is to propose a set of guidelines for the optimal completion design, by integrating current best practices through a decision-making system based on Artificial Bayesian Intelligence. Best completion 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, rock and fluid properties.
The described decision-making model follows a causal and uncertainty-based approach capable of simulating realistic conditions on the use of completion operations. For instance, the use of water swelling packer dictates the use of organic acids instead of HCl acids. However, rock type and well geometry affect our selection of treatment fluids. Another example is selection of sand control method based on rock properties. The paper also outlines best operational practices in fracturing, sand control, perforation, treatment and completion fluids, multilateral junction level selection and lateral completion. The paper also discusses in details special well completions. Completion experts' opinions were considered in building the model in this paper.
The outcome of this paper is a user-friendly tool, where one 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.