DSDE: In Practice

This paper proposes a method to radically revise the well-path-generation process to reach the vision of planning a well in one day with high quality, relying on the collection of experience-based constraints from each discipline to generate possible alternatives.

This paper describes an approach that combines rock typing and machine-learning neural-network techniques to predict the permeability of heterogeneous carbonate formations accurately.

This study describes the performance of machine-learning models generated by the self-organizing-map technique to predict electrical rock properties in the Saman field in northern Colombia.

Geo Oceans has achieved class sign off for the world’s first entirely robotic FPSO cargo oil tank special survey.

The authors of this paper review the advantages of machine learning in complex compositional reservoir simulations to determine fluid properties such as critical temperature and saturation pressure.

This study describes a technique combining borehole acoustic reflection imaging with production logging in a horizontal well experiencing high water cut with the aim of enabling a mitigation plan.

Operators tell an audience at the Unconventional Resources Technology Conference how a hybrid expandable liner system and machine-learning-based analysis improve the bottom line.

This paper presents efforts to reduce greenhouse-gas emissions and increase energy efficiency through the use of a real-time monitoring tool on exploration and production operated assets.

An agent-based model is developed and used to predict COVID-19 spread within living quarters of an offshore oil and gas platform.

This study introduces a detailed model to capture the physics and chemistry of acid flow in complex horizontal wells completed in carbonate formations.