The authors apply a novel deep-learning algorithm called a transformer to build surrogate models for simulations of well performance. Transformer architecture initially was developed for natural-language processing problems. However, in recent years, researchers have adapted transformers for time-series forecasting.
Fotech has launched two next-generation Helios DAS systems at the International Pipeline Expo in Calgary, Canada, between 27 and 29 September 2022. The new Helios DAS TL4 (single-channel) and the Helios DAS TX4 (dual-channel) interrogators deliver lower false alarm rates and enhanced monitoring and incident detection. They incorporate new machine learning capabilities, which allows a faster, cost effective and more systematic deployment of solutions in long linear assets such, as pipelines and perimeters. Pedro Barbosa, Senior Product Manager at Fotech, says, "The new Helios DAS TL4 and Helios DAS TX4 interrogators take monitoring of pipelines, critical infrastructure and perimeters to the next level. The machine learning that is built into them means they deliver exceptional accuracy with a much-reduced false alarm rate. As a result, users have extremely high confidence in alarms, and don't waste precious time or resource investigating false alarms."
A new technical paper titled "A Thermal Machine Learning Solver For Chip Simulation" was published by researchers at Ansys. Abstract "Thermal analysis provides deeper insights into electronic chips' behavior under different temperature scenarios and enables faster design exploration. However, obtaining detailed and accurate thermal profile on chip is very time-consuming using FEM or CFD. Therefore, there is an urgent need for speeding up the on-chip thermal solution to address various system scenarios. In this paper, we propose a thermal machine-learning (ML) solver to speed-up thermal simulations of chips. The thermal ML-Solver is an extension of the recent novel approach, CoAEMLSim (Composable Autoencoder Machine Learning Simulator) with modifications to the solution algorithm to handle constant and distributed HTC. The proposed method is validated against commercial solvers, such as Ansys MAPDL, as well as a latest ML baseline, UNet, under different scenarios to demonstrate its enhanced accuracy, scalability, and generalizability."
The main objective of this work is to use machine-learning (ML) algorithms to develop a powerful model to predict well-integrity (WI) risk categories of gas-lifted wells. The model described in the complete paper can predict well-risk level and provide a unique method to convert associated failure risk of each element in the well envelope into tangible values. The predictive model, which predicts the risk status of wells and classifies their integrity level into five categories rather than three broad-range categories, as in qualitative risk classification. The five categories are Category 1, which is too risky Category 2, which is still too risky but less so than Category 1 Category 3, which is medium risk but can be elevated if additional barrier failures occur Category 4, which is low risk but features some impaired barriers Category 5, which is the lowest in risk The failure model, which identifies whether the well is considered to be in failure mode. In addition, the model can identify wells that require prompt mitigation.
As humans, we perceive the three-dimensional structure of the world around us with apparent ease. Think of how vivid the three-dimensional percept is when you look at a vase of flowers sitting on the table next to you. You can tell the shape and translucency of each petal through the subtle patterns of light and shading that play across its surface and effortlessly segment each flower from the background of the scene (Figure 1.1). Looking at a framed group por- trait, you can easily count (and name) all of the people in the picture and even guess at their emotions from their facial appearance. Perceptual psychologists have spent decades trying to understand how the visual system works and, even though they can devise optical illusions1 to tease apart some of its principles (Figure 1.3), a complete solution to this puzzle remains elusive (Marr 1982; Palmer 1999; Livingstone 2008).
Ikon Science, a global provider of knowledge management solutions designed to optimize subsurface discovery, announced the release of RokDoc Version 2022.4., an industry-leading geoprediction software. As global energy demand continues to grow and drilling activities increase to meet this challenge, subsurface teams in E&P companies are challenged to deliver key reservoir insights faster and more efficiently than before. To meet this challenge, the latest version of RokDoc introduces new functionality and automation of QC and knowledge generation workflows. The quantitative analysis workflow used to characterize reservoirs has been significantly improved by adding several user enhancements to streamline and speed-up results. Additionally, we are pleased to announce the release of the Rock Physics Machine Learning (RPML) tool, a technical collaboration with Australia's national science agency, CSIRO, as an addition to our already powerful Deep QI module.
Ray tracing, a next-generation lighting technology that creates hyper-realistic visuals, has been the hot topic in gaming graphics for a while. The most interesting aspect is that real-time ray tracing doesn't require spiffy new gaming engines or setups -- you can apply it to super-basic 3D titles like Minecraft or even the original Doom. As part of its RTX 4000 series announcement, Nvidia showed off an updated version of Valve's classic Portal, a first-person puzzler, with a ray tracing lighting mod and DLSS 3 applied. Nvidia isn't the first to apply ray tracing to Portal, but its new version of the game consistently applies the lighting technology to the entire game. This enhances the original title's bleak atmosphere and the super-clean testing facility and its dark, foreboding underbelly.
Portal 3 may never happen, but at least we've got a new way to experience the original teleporting puzzle shooter. Today during his GTC keynote, NVIDIA CEO Jensen Huang announced Portal with RTX, a mod that adds support for real-time ray tracing and DLSS 3. Judging from the the short trailer, it looks like the Portal we all know and love, except now the lighting around portals bleeds into their surroundings, and just about every surface is deliciously reflective. Similar to what we saw with Minecraft RTX, Portal's ray tracing mod adds a tremendous amount of depth to a very familiar game. And thanks to DLSS 3, the latest version of NVIDIA's super sampling technology, it also performs smoothly with plenty of RTX bells and whistles turned on. This footage likely came from the obscenely powerful RTX 4090, but it'll be interesting to see how well Portal with RTX performs on NVIDIA's older 2000-series cards.
Despite the widespread diffusion of renewable energy, oil and gas are among the highly valued commodities in the energy sector. However, commodity cycles, capital planning challenges, and increasing operational risk have propelled the oil and gas industry to make more intelligent and efficient decisions. In a 2018 Ernst & Young (EY) survey, Artificial Intelligence (AI)/Machine Learning (ML) didn't even rank in the top five technologies used by seven global oil and gas supermajors (Figure 1). Further, they feel that in the coming years, technologies like robotic process automation (RPA) (25%) and advanced analytics (25%), but not AI/ML, will have the most significant and positive effect on their businesses. AI/ML have enormous potential in the oil and gas industry, and by not considering it, leaders in the sector risk being blindsided. It can help reduce costs, add capacity and capability, speed decision-making, and improve quality while managing risk.
As autonomous systems and robots are applied to more real world situations, they must reason about uncertainty when planning actions. Mission success oftentimes cannot be guaranteed and the planner must reason about the probability of failure. Unfortunately, computing a trajectory that satisfies mission goals while constraining the probability of failure is difficult because of the need to reason about complex, multidimensional probability distributions. Recent methods have seen success using chance-constrained, model-based planning. However, the majority of these methods can only handle simple environment and agent models. We argue that there are two main drawbacks of current approaches to goal-directed motion planning under uncertainty. First, current methods suffer from an inability to deal with expressive environment models such as 3D non-convex obstacles. Second, most planners rely on considerable simplifications when computing trajectory risk including approximating the agent’s dynamics, geometry, and uncertainty. In this article, we apply hybrid search to the risk-bound, goal-directed planning problem. The hybrid search consists of a region planner and a trajectory planner. The region planner makes discrete choices by reasoning about geometric regions that the autonomous agent should visit in order to accomplish its mission. In formulating the region planner, we propose landmark regions that help produce obstacle-free paths. The region planner passes paths through the environment to a trajectory planner; the task of the trajectory planner is to optimize trajectories that respect the agent’s dynamics and the user’s desired risk of mission failure. We discuss three approaches to modeling trajectory risk: a CDF-based approach, a sampling-based collocation method, and an algorithm named Shooting Method Monte Carlo. These models allow computation of trajectory risk with more complex environments, agent dynamics, geometries, and models of uncertainty than past approaches. A variety of 2D and 3D test cases are presented including a linear case, a Dubins car model, and an underwater autonomous vehicle. The method is shown to outperform other methods in terms of speed and utility of the solution. Additionally, the models of trajectory risk are shown to better approximate risk in simulation.