Directional/complex wells

In this paper, a case study is described in which a software solution enabled prescriptive optimization of well delivery using a physics-informed machine-learning approach for predictive identification and characterization of well-construction risks.
SPE conference authors offer a trio of papers that blend field practice, simulation optimization, and machine-learning techniques to more-efficiently pursue the goal of longer, highly deviated wells that only grows in importance to the industry with every passing year.
This paper describes the integration of iterative torque/drag/buckling and hydraulic simulations for multiple tapered string combinations, the results of which guided the selection of a string configuration that deemed planned well total depths feasible.

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