Processing systems/design
The new technology, recently highlighted at OTC, has received a statement of maturity from ABS.
War‑related infrastructure damage is beginning to influence global energy supply chains in ways that could reshape project development and capacity growth.
This paper describes the operator’s initiative to reduce greenhouse-gas emissions and recover additional hydrocarbon, monetizing it as sales gas, by integrating upstream and downstream gas facilities in a unified approach.
-
This paper describes development of an adjustable cone meter that can adapt to flow conditions automatically and provide a turndown of as much as a 54:1 under dry-gas conditions and as much as 20:1 under wet-gas conditions. -
This paper examines the implementation of a dual-frequency desalter from early engagement through technology selection, engineering design, and project execution, leading to a successful startup backed by operational history.
-
The paper describes a systems-engineering-guidance document currently being developed, and outlines the benefits associated with the use of formal systems-engineering processes when designing complex facilities.
-
The paper describes the components of an all-electric system, how it fits within a subsea application, and how it compares with traditional electrohydraulic systems.
-
SponsoredA new Coriolis sensor and innovative multi-frequency technology enables measurement of two-phase flow with entrained gas.
-
The acquisition expands Targa’s midstream natural gas processing footprint in the active Delaware Basin of New Mexico.
-
The deal also includes a second, lump-sum contract to expand sulfur-handling, storage, and loading facilities.
-
A system that could help turn stranded-gas fields into producing projects has moved closer to reality, aided by the growing focus on reductions in carbon emissions.
-
The authors describe an integrated subsurface characterization and monitoring approach for the construction of underground gas storage while ensuring long-term stability.
-
The machine-learning techniques applied aim to deliver a prediction model based on both simulation and real-time field data. The model tracks and monitors system key performance indicators.
