Drilling
Industry forecasts point to increasing upstream expenditure and production, even as geopolitical volatility and smaller discoveries challenge project economics.
Industry fundamentals continue to strengthen as rising utilization and scarce replacement capacity support higher rig valuations.
This paper aims to establish a set of best practices for generating particle-size-distribution data from core samples.
-
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 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 describes the evolution of the operator’s initial PWC (perforate, wash, and cement) abandonment projects performed in deepwater Brazil.
-
The authors reach conclusions that the industry should define a standard testing method to improve swelling performance, including validation of repeatability, to complement existing guidelines.
-
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 year, we observed a notable increase in activity centered on physical technologies, including cementing tools, zonal isolation barriers, and cement evaluation systems.
-
This paper presents the development of an advanced simulation tool aimed at providing a better understanding of the complex fluid-displacement phenomena present in well-cementing processes.
-
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.
-
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.
