The objective is to demonstrate how DAS, integrated with AI-driven acoustic diagnostics, can be used to derive high-resolution inflow profiles along the wellbore without interrupting production or relying on conventional logging tools. The approach is applicable to both vertical and horizontal wells where zonal performance differentiation is critical for optimization. The AI-based platform developed in this paper overcomes four key limitations of traditional DAS analytics. It enables real-time processing of massive data volumes, enhances spatial resolution to eliminate blind zones along the wellbore, accurately distinguishes true reservoir inflow from internal fluid motion, and reliably differentiates gas flow from other high-frequency noise sources. A novel AI-based data compression technique is employed to significantly reduce data volume, facilitating secure encryption and efficient cloud transmission. Signal processing methods are applied to extract frequency-specific responses, suppress artifacts, and identify phase- consistent patterns indicative of fluid entry points. The approach has been validated through multiple field trials and benchmarked against known well operations. Case studies from recent DAS logging campaigns illustrate the ability of the system to generate inflow profiles that align with expected production behavior across a range of reservoir conditions. In wells producing emulsions without gas co-production, the method successfully identified misleading indications of gas entry points typically misinterpreted in traditional DAS analyses. Gas typically exhibits rapid and random vibration patterns like those observed in ESPs. The AI-based platform can recognize these characteristic patterns, allowing it to determine whether a high-frequency event corresponds to actual gas entry or another source. The system also demonstrated robustness in detecting evolving flow regimes and phase changes over time, supporting its potential for continuous monitoring and post-workover evaluation. Identifying such disturbances is crucial for locating water production zones and planning workovers to block them and enhance oil production, a task the AI-based platform accomplishes by detecting phase change intervals along the well. The operator plans to use upcoming 3D seismic data to compare with the zonal phase change results along the wells and to evaluate the production response to an intervention in one of the wells discussed in this paper. This work highlights a novel integration of DAS sensing with real-time cloud computing and AI-based signal interpretation, offering a non-intrusive assistive technology to traditional production logging. Unlike conventional production logging methods that provide only periodic snapshots, DAS enables continuous, remote insights into zonal production behavior. These insights can support improved decision-making in reservoir management, artificial lift optimization, well integrity assessment, and early detection of flo0w anomalies in thermal operations.
The laserLaser-powder bed fusion (L-PBF) processingProcessing environmentEnvironment poses significant challengesChallenges for the expansion of materials available for usage as a result of the drastic cooling rates native to this manufacturing method. First introduced in the castingCasting manufacturing ecosystem, the incorporation of small amounts of grain refining rare-earth metals, particularly scandiumScandium, into aluminum alloysAluminum alloys (Al alloysAl alloy) provided an avenue to resist the detrimental effects imposed by extreme thermal gradients, while also imparting a not insignificant increase in material strength. Through the analysis of solidificationSolidification behaviour, complementary element pairings, and owing to the supersaturation potential in these manufacturing conditions, a material designDesign strategyStrategy is outlined based upon observations made through experimentation and from the literature. Results show that an apparent extensive processingProcessing window exists for the material studied, with many opportunities for further improvementImprovement of print results available. Further work objectives are outlined for work involving the synthesis of new materials using scandiumScandium as a significant additiveAdditive, owing to its grain refining qualities.
THE TERM “SOLVENT” REFERS TO A SUBSTANCE, usually a liquid, which is able to dissolve or disperse a particular substance or ingredient. Solvents are used in numerous industrial, commercial, and consumer products. Specifications for solvents and chemical intermediates (a substance with a distinct molecular composition that is produced or consumed in a chemical process) invariably include volatility parameters such as initial and final boiling points as well as boiling point range. In this chapter, solvent volatility properties and how they relate to evaporation rate will be covered.
AS WE HAVE SEEN IN PREVIOUS CHAPTERS, THE measurement of distillation and vapor pressure characteristics are vital pieces of information for the classification and volatility property certification of petroleum products. While these measurements are typically conducted under standard laboratory conditions and practices, there also exists a need for measurement of these same parameters under the dynamic conditions encountered during the actual production process of these fuels and other products. This chapter provides a high level, non-technical overview to introduce readers to the subject matter. Measurements under these types of dynamic conditions are generally accomplished through the application of on-line analytical instrumentation systems. These systems are designed to tap, either directly or indirectly, into the process streams contained within a refineries production facility. These systems are generally capable of making continuous or periodic measurements of the distillation or vapor pressure characteristic during the actual dynamic production of the product. This provides for near continuous feedback of information about the volatility characteristics of a product directly to the process plant operators, such that the necessary adjustments to key process parameters can be made in order to have the final product meet the desired (or targeted) volatility properties. In modern day refineries, this process control function is typically carried out through automated control systems based on complex mathematical models of the manufacturing process, with the plant operators acting primarily in a supervisory role and to deal with unexpected disturbances. Last but not least, the on-line measurement system produced results can be used in providing continuous quality control and statistical analysis of the volatility properties of the monitored product stream. The design of these on-line measurement systems is non-trivial. For most applications, the design considerations begin with the process control requirement and objective. Since these systems are intended to operate continuously, unsupervised, and within the production facility, system hardware design must meet specific safety requirements and standards developed through ASTM, ISA, and other industry consortiums.
