Multi-task learning for virtual flow metering

• URL: http://arxiv.org/abs/2103.08713v1
• Date: Mon, 15 Mar 2021 20:52:40 GMT
• Title: Multi-task learning for virtual flow metering
• Authors: Anders T. Sandnes (1 and 2), Bjarne Grimstad (1 and 3), Odd Kolbj{\o}rnsen (2) ((1) Solution Seeker AS, (2) Department of Mathematics, University of Oslo, (3) Department of Engineering Cybernetics, Norwegian University of Science and Technology)
• Abstract summary: We propose a new multi-task learning architecture for data-driven VFM. Our findings show that MTL improves robustness over single task methods, without sacrificing performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Virtual flow metering (VFM) is a cost-effective and non-intrusive technology for inferring multi-phase flow rates in petroleum assets. Inferences about flow rates are fundamental to decision support systems which operators extensively rely on. Data-driven VFM, where mechanistic models are replaced with machine learning models, has recently gained attention due to its promise of lower maintenance costs. While excellent performance in small sample studies have been reported in the literature, there is still considerable doubt towards the robustness of data-driven VFM. In this paper we propose a new multi-task learning (MTL) architecture for data-driven VFM. Our method differs from previous methods in that it enables learning across oil and gas wells. We study the method by modeling 55 wells from four petroleum assets. Our findings show that MTL improves robustness over single task methods, without sacrificing performance. MTL yields a 25-50% error reduction on average for the assets where single task architectures are struggling.

Related papers

• A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Method-Level Code Smell Detection [11.9757082688031]
  Existing detection methods, relying on Codes or Machine Learning (ML) and Deep Learning (DL) techniques, often face limitations such as unsatisfactory performance. This study evaluates state-of-the-art PEFT methods on both small and large Language Models for detecting two types of method-level code smells: Complex Conditional and Complex Method. Results show that PEFT methods achieve comparable or better performance than full fine-tuning while consuming less GPU memory.
  arXiv  Detail & Related papers  (2024-12-18T12:48:36Z)
• Automatic Evaluation for Text-to-image Generation: Task-decomposed Framework, Distilled Training, and Meta-evaluation Benchmark [62.58869921806019]
  We propose a task decomposition evaluation framework based on GPT-4o to automatically construct a new training dataset. We design innovative training strategies to effectively distill GPT-4o's evaluation capabilities into a 7B open-source MLLM, MiniCPM-V-2.6. Experimental results demonstrate that our distilled open-source MLLM significantly outperforms the current state-of-the-art GPT-4o-base baseline.
  arXiv  Detail & Related papers  (2024-11-23T08:06:06Z)
• Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance [78.48606021719206]
  Mini-InternVL is a series of MLLMs with parameters ranging from 1B to 4B, which achieves 90% of the performance with only 5% of the parameters. We develop a unified adaptation framework for Mini-InternVL, which enables our models to transfer and outperform specialized models in downstream tasks.
  arXiv  Detail & Related papers  (2024-10-21T17:58:20Z)
• STLLM-DF: A Spatial-Temporal Large Language Model with Diffusion for Enhanced Multi-Mode Traffic System Forecasting [32.943673568195315]
  We propose the Spatial-Temporal Large Language Model (STLLM-DF) to improve multi-task transportation prediction. The DDPM's robust denoising capabilities enable it to recover underlying data patterns from noisy inputs. We show that STLLM-DF consistently outperforms existing models, achieving an average reduction of 2.40% in MAE, 4.50% in RMSE, and 1.51% in MAPE.
  arXiv  Detail & Related papers  (2024-09-08T15:29:27Z)
• Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines [0.0]
  This paper is aimed at using the newly developing field of physics informed machine learning (PIML) to develop models for predicting the remaining useful lifetime (RUL) aircraft engines. We consider the well-known benchmark NASA Commercial Modular Aero-Propulsion System Simulation System (C-MAPSS) data as the main data for this paper. C-MAPSS is a well-studied dataset with much existing work in the literature that address RUL prediction with classical and deep learning methods.
  arXiv  Detail & Related papers  (2024-06-21T19:55:34Z)
• MoE-LLaVA: Mixture of Experts for Large Vision-Language Models [49.32669226551026]
  We propose a simple yet effective training strategy MoE-Tuning for LVLMs. MoE-LLaVA, a MoE-based sparse LVLM architecture, uniquely activates only the top-k experts through routers. Experiments show the significant performance of MoE-LLaVA in a variety of visual understanding and object hallucination benchmarks.
  arXiv  Detail & Related papers  (2024-01-29T08:13:40Z)
• MinT: Boosting Generalization in Mathematical Reasoning via Multi-View Fine-Tuning [53.90744622542961]
  Reasoning in mathematical domains remains a significant challenge for small language models (LMs) We introduce a new method that exploits existing mathematical problem datasets with diverse annotation styles. Experimental results show that our strategy enables a LLaMA-7B model to outperform prior approaches.
  arXiv  Detail & Related papers  (2023-07-16T05:41:53Z)
• Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning [101.66860222415512]
  Multi-Task Diffusion Model (textscMTDiff) is a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis. For generative planning, we find textscMTDiff outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D.
  arXiv  Detail & Related papers  (2023-05-29T05:20:38Z)
• On gray-box modeling for virtual flow metering [0.0]
  A virtual flow meter (VFM) enables continuous prediction of flow rates in petroleum production systems. Gray-box modeling is an approach that combines mechanistic and data-driven modeling. This article investigates five different gray-box model types in an industrial case study on 10 petroleum wells.
  arXiv  Detail & Related papers  (2021-03-23T13:17:38Z)
• Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources [78.72922528736011]
  We propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box machine learning model. Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses. BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method.
  arXiv  Detail & Related papers  (2020-07-17T01:52:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.