The Application of Machine Learning Algorithm in Relative Permeability Upscaling for Oil-Water System

2021 ◽  
Author(s):  
Yanji Wang ◽  
Hangyu Li ◽  
Ji Tian ◽  
Ling Fan ◽  
Jianchun Xu
Keyword(s):  
Machine Learning ◽  
Relative Permeability ◽  
Learning Algorithm ◽  
Water System ◽  
Flow Simulation ◽  
Machine Learning Algorithm ◽  
Fine Scale ◽  
Two Phase ◽  
Permeability Upscaling ◽  
Similar Accuracy

Abstract Traditional two-phase relative permeability upscaling requires the fine-scale two-phase flow simulation over the target regions/blocks. It can be very computationally expensive especially for cases with multiple (hundreds of) geological realizations (as commonly used in subsurface uncertainty quantification or optimization). In this paper, we develop a machine learning assisted relative permeability upscaling procedure, in which the full numerical upscaling is performed for only a portion of the coarse blocks, while the upscaled functions for the rest of the coarse blocks are calculated by the machine learning algorithm. The upscaling procedure was tested for generic (left to right) flow problems using 2D models for scenarios involving multiple realizations. Numerical results have shown that the coarse-scale simulation results using the newly developed machine learning assisted upscaling procedure are of similar accuracy to the coarse results using full numerical upscaling. Because the fine-scale numerical simulation is only performed for a small fraction of the model, significant speedup is achieved.

A Novel Machine Learning Assisted Upscaling Workflow for Simulating the Waterflooding Process

2021 ◽  
Author(s):  
Yanji Wang ◽  
Hangyu Li ◽  
Jianchun Xu ◽  
Ling Fan ◽  
Xiaopu Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Flow Simulation ◽  
Machine Learning Algorithms ◽  
Scale Model ◽  
Two Phase ◽  
Flow Problems ◽  
Similar Accuracy

Abstract Conventional flow-based two-phase upscaling for simulating the waterflooding process requires the calculations of upscaled two-phase parameters for each coarse interface or block. The whole procedure can be greatly time-consuming especially for large-scale reservoir models. To address this problem, flow-based two-phase upscaling techniques are combined with machine learning algorithms, in which the flow-based two-phase upscaling is needed only for a small fraction of coarse interfaces (or blocks), while the upscaled two-phase parameters for the rest of the coarse interfaces (or blocks) are directly provided by the machine learning algorithms instead of performing upscaling computation on each coarse interfaces (or blocks). The new two-phase upscaling workflow was tested for generic (left to right) flow problems using a 2D large-scale model. We observed similar accuracy for results using the machine learning assisted workflow compared with the results using full flow-based upscaling. And significant speedup (nearly 70) is achieved. The workflow developed in this work is one of the pioneering work in combining machine learning algorithm with the time-consuming flow-based two-phase upscaling method. It is a valuable addition to the existing multiscale techniques for subsurface flow simulation.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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