DSDE: In Practice

This paper reports the observation of stealth asphaltenes, a potential flow-assurance issue, by means of experimental analysis.

This paper describes the suite of cloud-based digital twin tools that the operator has developed and is integrating into its operations, providing online, real-time calculation of scale risk and deployed barrier health to manage risk on a well-by-well basis.

This paper presents the processes of identifying production enhancement opportunities, as well as the methodology used to identify underperforming candidates and analyze well-integrity issues, in a brownfield offshore Malaysia.

This paper describes installation of autonomous inflow control valves in the Bretaña Norte field in Peru, enabling effective water control even though the trial well was placed in the flank, close to the oil/water contact.

This paper discusses the approach used to sectorize a mature giant carbonate reservoir located onshore Abu Dhabi for the purposes of reservoir management, offtake, and injection balancing.

This paper presents a novel modeling framework for predicting residual oil saturation in carbonate rocks. The proposed framework uses supervised machine learning models trained on data generated by pore-scale simulations and aims to supplement conventional coreflooding tests or serve as a tool for rapid residual oil saturation evaluation of a reservoir.

This paper describes the development of a comprehensive digital solution for well surveillance and field-production optimization for an offshore field consisting of four stacked reservoirs, each containing near-critical fluids.

This paper describes a workflow that has been established to calculate the value of reservoir surveillance in producing gas fields based on established value-of-information methods.

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.