Testing page for app

This article describes a technology combining a compression unit with a flexible line to offer a flaring alternative for transferring hydrocarbons.

This paper describes a deep-learning image-processing model that uses videos captured by a specialized optical gas-imaging camera to detect natural gas leaks.

This paper explores the sustainability and safety aspects of laser perforation with a focus on factors such as water requirement, asset integrity, logistics, safety, and surface footprint.

This paper describes an intelligent completion system in the context of multiple wells that, by electrifying the process, replaces the conventional electrohydraulic systems that have been in use for decades.

This paper aims to provide insights to address the challenge of identifying the optimal point within the gas-processing lineup for recovering a high-purity CO₂ stream suitable for sequestration.

Recently, artificial intelligence (AI), deep learning (DL), and machine learning (ML) have taken natural gas processing and handling on a new trajectory, replacing complex simulation runs.

In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.

This work investigates the root cause of strong oil/water emulsion and if sludge formation is occurring within the reservoir using a robust integrated approach.

This study presents a production-optimization method that uses a deep-learning-based proxy model for the prediction of state variables and well outputs to solve nonlinearly constrained optimization with geological uncertainty.

In this work, a perturbed-chain statistical associating fluid theory equation of state has been developed to characterize heavy-oil-associated systems containing polar components and nonpolar components with respect to phase behavior and physical properties.