This study explores the use of autoencoder models with convolutional neural networks to present a framework and prototype for early and accurate kick detection during offshore oilwell drilling.

Operators aren’t rushing to drill, even as the closure of the Strait of Hormuz drives oil prices up.

This paper establishes that the use of a dual-gradient fluid column during the running of large casing in an extreme-reach deepwater well is an effective method to overcome drag and enable the casing to reach total depth.

This paper presents the first global application of autonomous drilling in deepwater and the journey to reach optimal drilling parameters, integrating proprietary tools from the project’s business partners.

This paper examines the effects of cement voids and microannuli on the collapse resistance of pipe/cement/pipe systems with void angles ranging from 0º to 70º.

This paper describes the evolution of the operator’s initial PWC (perforate, wash, and cement) abandonment projects performed in deepwater Brazil.

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.

Drilling experts recently shared candid views on what will be required for their segment of the upstream business to move to the next stage of development.