Integrating geological attributes with a multiple linear regression of geophysical well logs to estimate the permeability of carbonate reservoirs in Campos basin - Southeastern Brazil

2016 ◽  
Author(s):  
Paula Almeida ◽  
Abel Carrasquilla
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Carbonate Reservoirs ◽  
Well Logs ◽  
Southeastern Brazil ◽  
Campos Basin ◽  
Geophysical Well Logs

scholarly journals PETROPHYSICAL CHARACTERIZATION OF ALBIAN CARBONATE RESERVOIR IN CAMPOS BASIN USING A MULTIVARIATE APPROACH WITH WELL LOGS AND LABORATORY MEASUREMENTS

2018 ◽  
Vol 36 (2) ◽  
pp. 123
Author(s):  
Antonio Abel Carrasquilla ◽  
Raphael Ribeiro Silva
Keyword(s):  
Cluster Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Water Saturation ◽  
Laboratory Data ◽  
Carbonate Reservoir ◽  
Well Logs ◽  
Flow Units ◽  
Rock Typing ◽  
Campos Basin

ABSTRACT. This study characterizes an Albian carbonate reservoir of Field B in the Campos Basin, based on geophysical well logs and laboratory petrophysical data. This permitted us to estimate the porosity, permeability and water saturation of this reservoir more reliably. In order to achieve this goal, the Cluster Analysis for Rock Typing module of the Interactive Petrophysics software was used to divide the well into electrofacies. For each of them, an equation was determined to find the porosity and the permeability, using the multiple linear regression technique, using as input the log data and as target the laboratory data. The obtained results were compared with different models proposed by other authors, with the best results being found with multiple linear regression. Water saturation, on the other hand, was estimated by Archie equation after identifying the cementation coefficient with the Pickett crossplot. Finally, the porosity and permeability data were again used to now identify three main flow units in the reservoir through the Winland graph. To verify the effectiveness of the adopted methodology, it was successfully applied in a blind test, defining poros-ity, permeability, water saturation and flow units in a well without laboratory data. Keywords: well logging, Field B, petrophysics, carbonate reservoir, Albian.RESUMO. Este estudo caracteriza um reservatório carbonático Albiano do Campo B na Bacia de Campos, a partir de dados de perfis de poço e de petrofísica de laboratório. Uma estimativa da porosidade, da permeabilidade e da saturação de água de forma mais confiável. Com ese objetivo, foi usado o módulo Cluster Analysis for Rock Typing do software Interactive Petrophysics para dividir o poço em eletrofácies. Para cada uma delas, foi determinada uma equação para a porosidade e a perme-abilidade, através da técnica de regressão linear múltipla, usando como entrada os dados de perfis de poço e como alvo os dados de laboratório. Esses resultados foram comparados com modelos propostos por outros autores, sendo os melhores aqueles obtidos com regressão linear múltipla. A saturação de água foi estimada com a Equação de Archie após identificar o coeficiente de cimenta-ção com o crossplot de Pickett. Finalmente, os dados de porosidade e permeabilidade foram usados para identificar três unidades de fluxo através do gráfico de Winland. Para verificar a eficácia da metodologia adotada, a mesma foi aplicada com sucesso num teste cego, definindo a porosidade, a permeabilidade, a saturação de água e as unidades de fluxo num poço sem dados de laboratório. Palavras-chave: perfis de poços, Campo B, petrofísica, reservatório carbonático, Albiano.   

scholarly journals Relationship between the iron status of pregnant women and their newborns

2007 ◽  
Vol 41 (3) ◽  
pp. 321-327 ◽  
Author(s):  
Adriana de A Paiva ◽  
Patrícia H C Rondó ◽  
Regina A Pagliusi ◽  
Maria do R D O Latorre ◽  
Maria A A Cardoso ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Iron Deficiency ◽  
Linear Regression ◽  
Nutritional Status ◽  
Pregnant Women ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Southeastern Brazil ◽  
Iron Deficient

OBJECTIVE: To determine the relationship between iron nutritional status of pregnant women and their newborns using a combination of hematological and biochemical parameters for the diagnosis of iron deficiency. METHODS: A cross-sectional study was conducted in Jundiaí, Southeastern Brazil, in 2000. Venous blood samples collected from 95 pregnant women and from their umbilical cord and used for the determination of complete blood count, serum iron, total iron-binding capacity, serum ferritin, zinc protoporphyrin, and transferrin saturation. Women were classified into three groups: anemic, iron deficient and non-iron deficient. Statistical analysis included the Tukey-HSD test, Pearson's correlation coefficient and multiple linear regression analysis. RESULTS: Among pregnant women, 19% were anemic (97.9% mildly anemic and 2.1% moderately anemic) and 30.5% were iron deficient. No significant difference was seen in mean values of any parameter studied between newborns in the three groups (p>0.05). Multiple linear regression analysis showed weak association between neonatal and maternal parameters. CONCLUSIONS: The iron nutritional status of pregnant women with iron deficiency or mild anemia does not seem to have a significant impact on the iron levels of their children.

NOVEL METHODOLOGY FOR AUTOMATION OF BAD WELL LOG DATA IDENTIFICATION AND REPAIR

2021 ◽  
Author(s):  
Ryan Banas ◽  
◽  
Andrew McDonald ◽  
Tegwyn Perkins ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Unsupervised Learning ◽  
Learning Algorithms ◽  
Well Logs ◽  
Quality Data ◽  
Well Log ◽  
Log Data ◽  
Petrophysical Analysis

Subsurface analysis-driven field development requires quality data as input into analysis, modelling, and planning. In the case of many conventional reservoirs, pay intervals are often well consolidated and maintain integrity under drilling and geological stresses providing an ideal logging environment. Consequently, editing well logs is often overlooked or dismissed entirely. Petrophysical analysis however is not always constrained to conventional pay intervals. When developing an unconventional reservoir, pay sections may be comprised of shales. The requirement for edited and quality checked logs becomes crucial to accurately assess storage volumes in place. Edited curves can also serve as inputs to engineering studies, geological and geophysical models, reservoir evaluation, and many machine learning models employed today. As an example, hydraulic fracturing model inputs may span over adjacent shale beds around a target reservoir, which are frequently washed out. These washed out sections may seriously impact logging measurements of interest, such as bulk density and acoustic compressional slowness, which are used to generate elastic properties and compute geomechanical curves. Two classifications of machine learning algorithms for identifying outliers and poor-quality data due to bad hole conditions are discussed: supervised and unsupervised learning. The first allows the expert to train a model from existing and categorized data, whereas unsupervised learning algorithms learn from a collection of unlabeled data. Each classification type has distinct advantages and disadvantages. Identifying outliers and conditioning well logs prior to a petrophysical analysis or machine learning model can be a time-consuming and laborious process, especially when large multi-well datasets are considered. In this study, a new supervised learning algorithm is presented that utilizes multiple-linear regression analysis to repair well log data in an iterative and automated routine. This technique allows outliers to be identified and repaired whilst improving the efficiency of the log data editing process without compromising accuracy. The algorithm uses sophisticated logic and curve predictions derived via multiple linear regression in order to systematically repair various well logs. A clear improvement in efficiency is observed when the algorithm is compared to other currently used methods. These include manual processing by a petrophysicist and unsupervised outlier detection methods. The algorithm can also be leveraged over multiple wells to produce more generalized predictions. Through a platform created to quickly identify and repair invalid log data, the results are controlled through input and supervision by the user. This methodology is not a direct replacement of an expert interpreter, but complementary by allowing the petrophysicist to leverage computing power, improve consistency, reduce error and improve turnaround time.

scholarly journals Factors associated with arterial stiffness in children aged 9-10 years

2015 ◽  
Vol 49 (0) ◽  
Author(s):  
Milena Santos Batista ◽  
José Geraldo Mill ◽  
Taisa Sabrina Silva Pereira ◽  
Carolina Dadalto Rocha Fernandes ◽  
Maria del Carmen Bisi Molina
Keyword(s):  
Blood Pressure ◽  
Linear Regression ◽  
Arterial Stiffness ◽  
Pulse Wave Velocity ◽  
Multiple Linear Regression ◽  
Wave Velocity ◽  
Pulse Wave ◽  
Southeastern Brazil ◽  
Convenience Sample ◽  
Factors Associated

OBJECTIVE To analyze the factors associated with stiffness of the great arteries in prepubertal children.METHODS This study with convenience sample of 231 schoolchildren aged 9-10 years enrolled in public and private schools in Vitória, ES, Southeastern Brazil, in 2010-2011. Anthropometric and hemodynamic data, blood pressure, and pulse wave velocity in the carotid-femoral segment were obtained. Data on current and previous health conditions were obtained by questionnaire and notes on the child’s health card. Multiple linear regression was applied to identify the partial and total contribution of the factors in determining the pulse wave velocity values.RESULTS Among the students, 50.2% were female and 55.4% were 10 years old. Among those classified in the last tertile of pulse wave velocity, 60.0% were overweight, with higher mean blood pressure, waist circumference, and waist-to-height ratio. Birth weight was not associated with pulse wave velocity. After multiple linear regression analysis, body mass index (BMI) and diastolic blood pressure remained in the model.CONCLUSIONS BMI was the most important factor in determining arterial stiffness in children aged 9-10 years.

Sign in / Sign up

Export Citation Format

Share Document