Testing page for app

In this paper, the authors propose a least-squares Q migration (LSQM) method that combines the benefits of both LSM and Q prestack depth migration (QPSDM) to improve the amplitude fidelity and image resolution of seismic data.

Just as the unconventional revolution has transformed our industry during the past decade, it has also dominated the world of artificial lift. In fact, it can be difficult to find an artificial lift paper these days that does not address, in some way, artificial lift systems in unconventional wells.

Natural-gas wells suffer from liquid loading if the gas-flow rate is insufficient to carry liquids to the surface. Because of the technique’s many advantages, plunger lift has been used widely in gas wells for the removal of liquid columns and the rescue of dying gas wells from liquid loading.

This paper presents the results of a 3-year project aimed at mass field implementation of ultrahigh-speed (UHS) electric submersible pump (ESP) systems in western Siberia.

Production monitoring requires heightened degrees of precision and efficiency as operations are streamlined and projects are evaluated continuously. This month’s feature focuses on innovative technologies that have been implemented in environments ranging from the Gulf of Mexico to China.

The complete paper explains the steps taken to improve surveillance of beam pumps using dynamometer-card data and machine-learning techniques and reviews lessons learned from executing the operator’s first artificial intelligence project.

This paper describes a virtual metering tool that can monitor well performance and estimate production rates using real-time data and analytical models, integrating commercial software with an optimization algorithm that combines production and reservoir information.

This paper discusses the effectiveness of third-generation (Gen3) production-logging-tool (PLT) technology, which uses co-located digital sensors for simultaneous acquisition of flow data to provide the most accurate characterization of the flow condition at each depth surveyed.

This paper describes an accurate, three-step, machine-learning-based early warning system that has been used to monitor production and guide strategy in the Shengli field.

SPE Journal