Testing page for app

The authors write that deployment of artificial-intelligence-based high-gas/oil ratio well-control technology enabled stabilization of well performance and maintenance of optimal production conditions.

The oil and gas industry is undergoing a significant shift with the advent of intelligent operations. This transformation is enabling upstream operations to move away from a reactive and manual mode of operation toward a more efficient, safe, and optimal state of operation.

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.

The paper describes the revalidation of a deepwater prospect that resulted in a no-drill decision.

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.