Characterization Of Hassi R'Mel Reservoir Rocks By An Unconventional Method Using Well Logs And Core Analysis Data

1980 ◽  
Author(s):  
Tahar Ghalem ◽  
Georges Blanc ◽  
Jean-Claude Sabathier ◽  
Marc Rocca
Keyword(s):  
Analysis Data ◽  
Well Logs ◽  
Core Analysis ◽  
Reservoir Rocks ◽  
Unconventional Method

Reservoir characterization of the Lower Abu Madi Formation using core analysis data: El-Wastani gas field, Egypt

2015 ◽  
Vol 110 ◽  
pp. 116-130 ◽  
Author(s):  
Mohamed A. Kassab ◽  
Mostafa A. Teama ◽  
Burns A. Cheadle ◽  
Ezz S. El-Din ◽  
Ibtehal F. Mohamed ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Analysis Data ◽  
Core Analysis ◽  
Gas Field

Petrophysical Facies Classification Based on Fuzzy Inference Networks

2013 ◽  
Vol 634-638 ◽  
pp. 4017-4021
Author(s):  
Jun Hui Pan ◽  
Hui Wang ◽  
Xiao Gang Yang
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Reasoning ◽  
Real Data ◽  
Analysis Data ◽  
Classification Model ◽  
Core Analysis ◽  
Development Zone ◽  
The Real ◽  
Facies Classification ◽  
Quantitative Indicators

Aiming at the petrophysical facies recognition, a novel identification method based on the weighted fuzzy reasoning networks is proposed in the paper. First, the types and indicators are obtained from core analysis data and the results given by experts, and then the standard patterning database of reservoir petrophysical facies is established. Secondly, by integrating expert experiences and quantitative indicators to reflect the change of petrophysical facies, the classification model of petrophysical facies based on the weighted fuzzy reasoning networks is designed. The preferable application results are presented by processing the real data from the Sabei development zone of Daqing oilfield.

Machine Learning for Multiple Petrophysical Properties Regression Based on Core Images and Well Logs in a Heterogenous Reservoir

2021 ◽  
Author(s):  
Tao Lin ◽  
Mokhles Mezghani ◽  
Chicheng Xu ◽  
Weichang Li
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Image Features ◽  
Well Logs ◽  
Model Complexity ◽  
Rock Properties ◽  
Core Analysis ◽  
Petrophysical Properties ◽  
Blind Test ◽  
Porosity And Permeability

Abstract Reservoir characterization requires accurate prediction of multiple petrophysical properties such as bulk density (or acoustic impedance), porosity, and permeability. However, it remains a big challenge in heterogeneous reservoirs due to significant diagenetic impacts including dissolution, dolomitization, cementation, and fracturing. Most well logs lack the resolution to obtain rock properties in detail in a heterogenous formation. Therefore, it is pertinent to integrate core images into the prediction workflow. This study presents a new approach to solve the problem of obtaining the high-resolution multiple petrophysical properties, by combining machine learning (ML) algorithms and computer vision (CV) techniques. The methodology can be used to automate the process of core data analysis with a minimum number of plugs, thus reducing human effort and cost and improving accuracy. The workflow consists of conditioning and extracting features from core images, correlating well logs and core analysis with those features to build ML models, and applying the models on new cores for petrophysical properties predictions. The core images are preprocessed and analyzed using color models and texture recognition, to extract image characteristics and core textures. The image features are then aggregated into a profile in depth, resampled and aligned with well logs and core analysis. The ML regression models, including classification and regression trees (CART) and deep neural network (DNN), are trained and validated from the filtered training samples of relevant features and target petrophysical properties. The models are then tested on a blind test dataset to evaluate the prediction performance, to predict target petrophysical properties of grain density, porosity and permeability. The profile of histograms of each target property are computed to analyze the data distribution. The feature vectors are extracted from CV analysis of core images and gamma ray logs. The importance of each feature is generated by CART model to individual target, which may be used to reduce model complexity of future model building. The model performances are evaluated and compared on each target. We achieved reasonably good correlation and accuracy on the models, for example, porosity R2=49.7% and RMSE=2.4 p.u., and logarithmic permeability R2=57.8% and RMSE=0.53. The field case demonstrates that inclusion of core image attributes can improve petrophysical regression in heterogenous reservoirs. It can be extended to a multi-well setting to generate vertical distribution of petrophysical properties which can be integrated into reservoir modeling and characterization. Machine leaning algorithms can help automate the workflow and be flexible to be adjusted to take various inputs for prediction.

scholarly journals The synthesis and characterization of some new diazoamino derivatives

2010 ◽  
Vol 16 (1) ◽  
pp. 89-95
Author(s):  
Mihaela Mocanu
Keyword(s):  
1H Nmr ◽  
Bioactive Compounds ◽  
Elemental Analysis ◽  
Analysis Data ◽  
Biological Properties ◽  
Synthesis And Characterization ◽  
Elemental Analysis Data ◽  
Spectral Measurements ◽  
Inflammatory Processes

The sulfonamidic moiety is much encountered in structures of bioactive compounds. In the present paper the studies on the sulfonamidated aryloxyalkylcarboxylic acids are extended by their attaching on certain substrata able to confer some special biological properties to the final products, such as anti-tumor and antioxidant actions useful in treating inflammatory processes, ulcer, convulsions and diabetes, as well as a herbicidal action. The stepwise syntheses of the sulfonamidated aryloxyalkylcarboxylic acid derivatives and their characterization by elemental analysis data and IR, 1H-NMR and UV-Vis spectral measurements are described. The newly obtained compounds could show potential pharmaceutical and herbicide properties.

Phanerozoic magmatism in the Parnaíba Basin: characterization of igneous bodies (well logs and 2D seismic sections), geometry, distribution and sill emplacement patterns

2018 ◽  
Vol 472 (1) ◽  
pp. 321-340 ◽  
Author(s):  
I. Trosdtorf ◽  
J. M. Morais Neto ◽  
S. F. Santos ◽  
C. V. Portela Filho ◽  
T. A. Dall Oglio ◽  
...  
Keyword(s):  
Well Logs ◽  
Seismic Sections ◽  
Parnaíba Basin

Whole-core analysis for effective characterization of inter-well permeability from a horizontal well

2008 ◽  
Author(s):  
Abdulla H. Bu Ali ◽  
Mehdi M. Honarpour ◽  
Syed M. Tariq and Nizar F. Djabbarah
Keyword(s):  
Horizontal Well ◽  
Core Analysis

scholarly journals Assessing Lysosomal Disorders in the NGS Era: Identification of Novel Rare Variants

2020 ◽  
Vol 21 (17) ◽  
pp. 6355
Author(s):  
Marisa Encarnação ◽  
Maria Francisca Coutinho ◽  
Lisbeth Silva ◽  
Diogo Ribeiro ◽  
Souad Ouesleti ◽  
...  
Keyword(s):  
Rare Variants ◽  
Health Care Systems ◽  
Genetic Disorders ◽  
Case Series ◽  
In Silico Analysis ◽  
Analysis Data ◽  
Lysosomal Storage ◽  
Pathogenic Variants ◽  
Number Of Patients

Lysosomal storage diseases (LSDs) are a heterogeneous group of genetic disorders with variable degrees of severity and a broad phenotypic spectrum, which may overlap with a number of other conditions. While individually rare, as a group LSDs affect a significant number of patients, placing an important burden on affected individuals and their families but also on national health care systems worldwide. Here, we present our results on the use of an in-house customized next-generation sequencing (NGS) panel of genes related to lysosome function as a first-line molecular test for the diagnosis of LSDs. Ultimately, our goal is to provide a fast and effective tool to screen for virtually all LSDs in a single run, thus contributing to decrease the diagnostic odyssey, accelerating the time to diagnosis. Our study enrolled a group of 23 patients with variable degrees of clinical and/or biochemical suspicion of LSD. Briefly, NGS analysis data workflow, followed by segregation analysis allowed the characterization of approximately 41% of the analyzed patients and the identification of 10 different pathogenic variants, underlying nine LSDs. Importantly, four of those variants were novel, and, when applicable, their effect over protein structure was evaluated through in silico analysis. One of the novel pathogenic variants was identified in the GM2A gene, which is associated with an ultra-rare (or misdiagnosed) LSD, the AB variant of GM2 Gangliosidosis. Overall, this case series highlights not only the major advantages of NGS-based diagnostic approaches but also, to some extent, its limitations ultimately promoting a reflection on the role of targeted panels as a primary tool for the prompt characterization of LSD patients.

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