6 edition of Statistical Regression Line-Fitting in the Oil and Gas Industry found in the catalog.
January 2003 by Pennwell Books .
Written in English
|The Physical Object|
|Number of Pages||110|
Data: The New Oil • Oil and gas exploration and production activities generate large amounts of data from sensors, logistics, business operations and more • The rise of cost-effective data collection, storage and computing devices is giving an established industry a new boost • Producing value from big data is a challenge and an. The fact that traditional statistics-based technologies use data to accomplish their objectives does not mean that they are capable of doing the same things that are done by AI and ML. The first applications of AI- and ML-related technologies in the upstream oil and gas industry .
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Statistical Regression: Line-Fitting in the Oil Statistical Regression Line-Fitting in the Oil and Gas Industry book Gas Industry [Woodhouse, Richard] on *FREE* shipping on qualifying offers.
Statistical Regression: Line-Fitting in the Oil and Gas Industry. Statistical Regression Line-Fitting in the Oil and Gas Industry: A Descriptive Guide With Microsoft Excel Examples [Richard Woodhouse] on *FREE* shipping on qualifying offers.
Statistical Regression Line-Fitting in the Oil and Gas Industry: A Descriptive Guide Cited by: 2. Statistical Regression Line-Fitting in the Oil and Gas Industry: A Descriptive Guide With Microsoft Excel Examples January Stanford Libraries' official online search tool for books, media, journals, databases, government documents and more.
Statistical regression line-fitting in the oil & gas industry: a descriptive guide with Microsoft Excel examples in SearchWorks catalogAuthor: Woodhouse, Richard.
Statistical line-fitting methods are mathematically demanding and are often mis-applied. This book, available in both softcover and e-book formats, provides a practical guide to statistical line-fitting methods for the non-mathematician covering new methods such as geo-statistical.
Get this from a library. Statistical regression line-fitting in the oil & gas industry: a descriptive guide with Microsoft Excel examples.
[Richard Woodhouse]. The statistical fitting of trend lines to a data set. Many regression methods are available, including linear, iterative, multiple and polynomial.
If there is Statistical Regression Line-Fitting in the Oil and Gas Industry book 'good' fit to the data, then the variables are often assumed to be dependent. This research work focuses on Statistical Regression Line-Fitting in the Oil and Gas Industry book regression analysis and time series analysis of the Petroleum Product Sales in Masters Energy oil and Gas.
The application of multiple regression model were made to show the effect of environmental factors in the petroleum product sales.
In the regression analysis, the p-value shows howFile Size: KB. The objective of this paper is to develop models that assess and predict the condition of oil and gas pipelines based on several factors including corrosion. The regression analysis technique was used to develop the condition prediction models based on historical.
16 Regression and smoothing Least squares Ridge regression Simple and multiple linear regression Polynomial regression Generalized Linear Models (GLIM) Logistic regression for proportion data Poisson regression for count data Non-linear regression File Size: 1MB.
A Study on Prediction of Output in Oilfield Using Multiple Linear Regression Izni binti Mustafar divide the oil industry into three sectors which are upstream, midstream and downstream. Multiple linear regressions are one of the most widely used of all statistical methods.
Multiple regression analysisFile Size: KB. Abstract. We provide a systematic approach for analyzing oil and gas discovery data based on a successive sampling model for the discovery process. First, the size distribution of deposits is estimated nonparametrically.
Graphical goodness-of-fit procedures are then used to select an appropriate parametric form for the by: 7. Application of Statistical Analysis and Prediction to the The exploring object is oil Statistical Regression Line-Fitting in the Oil and Gas Industry book gas bearing basins decided the role of administrative statistical analysis and prediction in the production of oil industry: such as the statistical analysis for the increase of hydrocarbon reserves adapting to the.
In the logistic regression, w e use the g eneralized linear regression. The correlation b e- t wee n v ariables in data mining and whether the oil and gas will explo de only relay ing on the.
This study aims to develop and validate multivariate mathematical models in order to monitor in real time the quality processing of derivatives in an oil refinery.
Methods heavily based on statistical and artificial intelligence as multivariate or chemometric methods have been widely used in the oil industry (KIM; LEE, KIM, ).
Several articles have been written about applications of multivariate analysis to predict properties of oil derivatives Cited by: 1. at 10 times the cost of gas purchased under contract.
The large cost the LDC incurs for buying gas on the spot market is passed directly to the end customer. Accurate forecasting of air conditioning loads and of non-temperature dependent gas demand (commonly referred to as baseload gas) during warm weather conditions is equally Size: KB.
Oil and gas projects have special characteristics that need a different technique in project management. The development of any country depends on the development of the energy reserve through investing in oil and gas projects through onshore and offshore exploration, drilling, and increasing facility capacities.
Therefore, these projects need a sort of management match with their. The present research work outlines the main ideas behind statistical regression by a two-independent-variates and one-dependent-variate model based on the invariance of measures in probabilistic spaces.
The principle of probabilistic measure invariance, applied under the assumption that the model be isotonic, leads to a system of differential by: 3. Oil and gas exploration is arguably the riskiest of all commercial activities. As a result, the utilization of probability and statistics in the oil and gas industry is becoming widely accepted as a method to estimate oil and gas exploration prospect size.
Analysis typically utilizes the Monte Carlo simulation method and one of the commercial. This three-day intensive and interactive course uses the principles of financial analysis and the power of Excel to identify the important variables that can dramatically enhance the value of an organisation in the oil and gas industry.
The objective of the book is to provide all the elements to evaluate the performance of production availability and reliability of a system, to integrate them and to manage them in its life cycle.
By the examples provided (case studies) the main target audience is that of the petroleum industries (where I spent most of my professional years). The oil and gas industry has lot of data from different sources. These data can give valuable information to decision-makers about uncertainty.
Statistical methods are valuable tools to estimate uncertainty and thereby give important decision support. The United States has been producing oil and gas since the 's and 's respectively.
Inthe U.S. produced million metric tons of oil and billion cubic meters of natural gas. This introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking.
It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. Keywords: Financial Performance, Multiple Regression Analysis Introduction The Oil and gas sectors play an active role in the political and economic scenario of the globe.
It caters 60% of the world‟s energy needs. World‟s primary energy demand is met by crude oil, coal, natural gas, nuclear energy andhydroelectricity contributing 38%, 29%,File Size: KB. While the oil and gas industry is frequently in the news, most often the stories are about fluctuations in the price of oil and gas.
Only when major disasters strike does the public focus on occupational injuries and illnesses in the oil and gas extraction industry. The Bureau of Labor Statistics (BLS) produces data that track the number of.
Journals & Books; Help As such, evaluations of the full-physics model are completely or partially replaced by evaluations of statistical regression surfaces (‘statistical proxy models’ or ‘reduced-order models’) that are constructed from prior evaluations of the full-physics model.
Some examples from oil and gas industry are Author: Mohammad S. Masnadi, Patrick R. Perrier, Jingfan Wang, Jeff Rutherford, Adam R. Brandt. Page 2 We used regression analysis to evaluate the relationship between domestic spot gasoline prices in the three U.S.
markets with: (1) crude oil benchmark prices for WTI, LLS, and Brent;File Size: KB. simultaneous fluid flow of oil and gas from the reservoir to the surface. This process is illustrated in Figure 1. A bubblepoint solution gas/oil ratio (R sob) is a key parameter in reservoir and in production engineering calculations.
The solution gas/oil ratio refers to the amount of gas dissolved in the oil at. With its integrative approach to system identification, regression and statistical theory, Statistics for Chemical and Process Engineers provides an excellent means of revision and self-study for chemical and process engineers working in experimental analysis and design in petrochemicals, ceramics, oil and gas, automotive and similar industries.
Despite Oil being one of the key drivers of the world economy, the recent fluctuations in oil prices has brought concerns about possible slowdowns in economic growth globally. To cushion their economies from these oil price volatility shocks, a number of developing countries have made structural reforms in their macroeconomic policies as far as domestic petroleum pricing system is : Anthony Makau.
Analytics Powers the Industrial Internet of Things for the Oil & Gas Industry. Keith Holdaway, of the Global Energy Practice at SAS, explains how IIoT data combined with analytics can fuel the decisions that keep oil and gas companies' assets and processes running at optimal capacity.
Calibration line-fitting The generally recommended method for obtaining a line-fit for porosity prediction is the " y -on- x " ordinary least-squares regression method.   The recommendation presumes that the calibration data set has accurate depth adjustments and is fully representative in all respects of the environment of the equation.
This paper discusses how oil and gas companies are using a new generation of AI-driven applications powered by computational-knowledge graphs and AI algorithms to create a digital knowledge layer for oil and gas wells that provides a timeline of significant well events.
01 February Addressing Challenges in Rig-Based Drilling Advisory. Other factors considered include the decline rate, the gas-oil ratio (GOR), the prices of oil and gas, as well as the rate of increase of the prices of oil and gas. The only input factor that contains an @RISK probability distribution function is reserves, but you could make the model more realistic by using distribution functions to describe.
Studies on residential regression analysis. Westergren et al. present results of regression models to estimate the heating energy consumption per unit of time (hour).
Two static models and one dynamic model were evaluated and compared. The first static model is a simple linear model of the form of by: Applied Statistical Modeling and Data Analytics for Petroleum Engineers and Geoscientists. This Course seeks to provide a practical guide to many of the classical and modern statistical techniques that have become, or are becoming, mainstream for oil and gas professionals.
The purpose of statistics is to project or infer, from limited samples, the character of a population. In most cases, particularly in oil and gas investigations, geological information is not derived from carefully designed sample schemes but, by design, represents anomalies.
This book is a valuable professional resource for engineers working in the global process industry and engineering companies, as well as students of engineering. It will be of great interest to those in the oil and gas, chemical, pulp and paper, water purification, pharmaceuticals and power generation industries, as well as for design engineers.
and less; wind and solar power are expanding fast. 1 The oil and gas industry, the traditional energy industry, is facing great challenges. On one hand, the U.S., the largest consumer of oil in the world and Canada’s key client, is seeking a diminished dependence on net oil. Alfonso's expertise encompasses all sectors of the pdf & gas industry including upstream, midstream and downstream, which led him to create and write this book.
Alfonso is an avid reader of Forms K and F, financial statements and other external company publications/5(15).• Brent crude oil prices are more important than WTI crude oil prices as a determinant of U.S.
gasoline prices in all four regions studied, including the Midwest. • The effect that a relaxation of current limitations on U.S. crude oil exports would have on Size: 1MB.Unconventional Ebook Geology is the first book of its kind to collectively identify, catalog, and assess the exploration ebook recovery potential of the Earth's unconventional hydrocarbons.
Advances in hydrocarbon technology and petroleum development systems have recently made the exploration of unconventional hydrocarbons—such as shale gas, tight sandstone oil and gas, heavy oil, tar sand 5/5(1).