scholarly journals Gamma-ray log derivative and volatility attributes assist facies characterization in clastic sedimentary sequences for formulaic and machine learning analysis

2022 ◽  
Vol 6 (1) ◽  
pp. 69-85
Author(s):  
David A. Wood
Keyword(s):  
Machine Learning ◽  
Gamma Ray ◽  
Sedimentary Sequences ◽  
Facies Characterization ◽  
Clastic Sedimentary ◽  
Gamma Ray Log ◽  
Learning Analysis

Automated gamma-ray log pattern alignment and depth matching by machine learning

2020 ◽  
Vol 8 (3) ◽  
pp. SL25-SL34
Author(s):  
Shirui Wang ◽  
Qiuyang Shen ◽  
Xuqing Wu ◽  
Jiefu Chen
Keyword(s):  
Machine Learning ◽  
Reservoir Characterization ◽  
Gamma Ray ◽  
Machine Learning Techniques ◽  
Matching Task ◽  
Learning Techniques ◽  
Single Well ◽  
Matching Strategy ◽  
Well Log Analysis ◽  
Gamma Ray Log

Depth matching of multiple logging curves is essential to any well evaluation or reservoir characterization. Depth matching can be applied to various measurements of a single well or multiple log curves from multiple wells within the same field. Because many drilling advisory projects have been launched to digitalize the well-log analysis, accurate depth matching becomes an important factor in improving well evaluation, production, and recovery. It is a challenge, though, to align the log curves from multiple wells due to the unpredictable structure of the geologic formations. We have conducted a study on the alignment of multiple gamma-ray well logs by using the state-of-the-art machine-learning techniques. Our objective is to automate the depth-matching task with minimum human intervention. We have developed a novel multitask learning approach by using a deep neural network to optimize the depth-matching strategy that correlates multiple gamma-ray logs in the same field. Our approach can be extended to other applications as well, such as automatic formation top labeling for an ongoing well given a reference well.

MACHINE LEARNING FOR PRODUCTIVITY PREDICTION IN HETEROGENEOUS CARBONATE GAS RESERVOIRS, CENTRAL SICHUAN BASIN, CHINA

2021 ◽  
Author(s):  
Zuoan Zhao ◽  
◽  
Dali Wang ◽  
Keyword(s):  
Machine Learning ◽  
Gamma Ray ◽  
Pore Space ◽  
Gas Production ◽  
Carbonate Reservoir ◽  
Production Rates ◽  
Gas Reservoirs ◽  
Data Set ◽  
Productivity Prediction ◽  
Gamma Ray Log

An approach of machine learning was used to evaluate and predict the production of the heterogeneous carbonate gas reservoirs in the horizontal development wells of the late Precambrian Dengying Formation. The present data set of machine learning consists of gamma ray log, laterolog, high-resolution electrical image logs, and production rate data. The previous data set acquired the conventional openhole logs, including gamma ray log, neutron-density log, sonic log, laterolog, and dipole acoustic log. The challenge in the previous data set was that the training process for machine learning was not convergent. It was most likely that the conventional log responses did not fully correspond to the productivity of the heterogenous carbonate gas reservoirs. Forty-one wells associated with the present data set were used to set up the training sample data set for the machine learning to the productivity prediction of the carbonate gas reservoirs. The data set construction includes log depth shift, calibrated image log creation, classification of reservoir types from core and carbonate reservoir heterogeneity variables extraction from image logs. Core observation and core laboratory analysis indicate that the pore space of the carbonate gas reservoirs mainly consists of vugs, caves, and fractures. However, the vugs and caves are selectively developed and randomly distributed both laterally and vertically. This represents a complex heterogeneous carbonate reservoir in which the vugs and caves are key contributor to the total pore space of the carbonate gas reservoir. The attributes of the vugs and caves can be extracted from the electrical image logs, including connectedness, surface proportion, size, and thickness of vug, and cave zones. Six horizontal development wells were used to validate the machine learning approach. The predicted gas production rates in the four wells separately were 700,000 m3/d, 2,000,000 m3/d, 800,000 m3/d, 300,000 m3/d, 1,100,000 m3/d and 1,180,000 m3/d, and the respective actual gas production rates are 1,019,790 m3/d, 1,820,000 m3/d, 800,000 m3/d, 396,000 m3/d , 1,700,000 m3/d, and 1,411,900 m3/d. The machine learning workflow and approach provided satisfactory results in the six horizontal wells. Subsequently, the electrical image logs have run in the standard logging program in the more than 50 horizontal development wells.

scholarly journals Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type

2021 ◽  
Vol 14 (3) ◽  
pp. 101016 ◽  
Author(s):  
Jim Abraham ◽  
Amy B. Heimberger ◽  
John Marshall ◽  
Elisabeth Heath ◽  
Joseph Drabick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tumor Type ◽  
Learning Analysis

scholarly journals Model-independent calibrations of gamma-ray bursts using machine learning

2021 ◽  
Vol 503 (3) ◽  
pp. 4581-4600
Author(s):  
Orlando Luongo ◽  
Marco Muccino
Keyword(s):  
Machine Learning ◽  
Dark Energy ◽  
Gamma Ray ◽  
Gamma Ray Bursts ◽  
Bayesian Regression ◽  
Machine Learning Techniques ◽  
Acoustic Oscillations ◽  
Chi Square ◽  
Model Independent ◽  
Chi Square Analysis

ABSTRACT We alleviate the circularity problem, whereby gamma-ray bursts are not perfect distance indicators, by means of a new model-independent technique based on Bézier polynomials. We use the well consolidate Amati and Combo correlations. We consider improved calibrated catalogues of mock data from differential Hubble rate points. To get our mock data, we use those machine learning scenarios that well adapt to gamma-ray bursts, discussing in detail how we handle small amounts of data from our machine learning techniques. We explore only three machine learning treatments, i.e. linear regression, neural network, and random forest, emphasizing quantitative statistical motivations behind these choices. Our calibration strategy consists in taking Hubble’s data, creating the mock compilation using machine learning and calibrating the aforementioned correlations through Bézier polynomials with a standard chi-square analysis first and then by means of a hierarchical Bayesian regression procedure. The corresponding catalogues, built up from the two correlations, have been used to constrain dark energy scenarios. We thus employ Markov chain Monte Carlo numerical analyses based on the most recent Pantheon supernova data, baryonic acoustic oscillations, and our gamma-ray burst data. We test the standard ΛCDM model and the Chevallier–Polarski–Linder parametrization. We discuss the recent H0 tension in view of our results. Moreover, we highlight a further severe tension over Ωm and we conclude that a slight evolving dark energy model is possible.

scholarly journals Technical Note: A Guide to Annotation of Neurosurgical Intraoperative Video for Machine Learning Analysis and Computer Vision

2021 ◽  
Author(s):  
Dhiraj J. Pangal ◽  
Guillaume Kugener ◽  
Shane Shahrestani ◽  
Frank Attenello ◽  
Gabriel Zada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Technical Note ◽  
Learning Analysis

scholarly journals Dynamic prediction of energy and power usage cost using linear regression-machine learning analysis

2021 ◽  
Vol 1921 ◽  
pp. 012067
Author(s):  
Rani Fathima Kamal Basha ◽  
M.L Bharathi ◽  
Kanagaraj Venusamy
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Dynamic Prediction ◽  
Usage Cost ◽  
Learning Analysis

Machine learning analysis of multispectral imaging and clinical risk factors to predict amputation wound healing

2021 ◽  
Author(s):  
John J. Squiers ◽  
Jeffrey E. Thatcher ◽  
David Bastawros ◽  
Andrew J. Applewhite ◽  
Ronald D. Baxter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Wound Healing ◽  
Multispectral Imaging ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Learning Analysis
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