modeling

This paper describes an approach to creating a digital, interconnected workspace that aligns sensor data with operational context to place the completions engineer back into a central role.

This paper demonstrates how the integration of multiphysics downhole imaging with machine-learning techniques provides a significant advance in perforation-erosion analysis.

This paper presents a workflow that leverages a multiagent conversational system to integrate data, analytics, and domain expertise for improved completion strategies.

Two examples from ONGC show how supervised AI-driven automation scaled well modeling across hundreds of offshore wells, saving more than 1,000 engineering hours.

This paper introduces a novel steam-sensitive flow-control device designed to restrict the production of steam and low-subcool liquids while allowing higher mobility of oil-phase fluids.

This study integrates laboratory testing with reservoir simulation to evaluate the effectiveness of autonomous inflow-control valves in managing late-life steam-assisted gravity-drainage production challenges.

In this study, the authors propose the use of a deep-learning reduced-order surrogate model that can lower computational costs significantly while still maintaining high accuracy for data assimilation or history-matching problems.

This paper demonstrates the effectiveness of integrating dynamic gas separation with existing gas-avoidance methods within the same electrical submersible pump string to address these issues.

This paper introduces an agentic artificial-intelligence framework designed for offshore production surveillance and intervention.

The objective of this study is to field test a non-nuclear multiphase flowmeter and assess its performance under challenging operating conditions.