TRAINING COURSE ON GEOSTATISTICS USING SOFTWARE FOR GEOSPATIAL ANALYSIS

Course Date Location Course fee: Registrations
Start Date: 16/12/2019 End Date: 20/12/2019 Nairobi $ 1,000 Register to attend
Start Date: 13/01/2020 End Date: 17/01/2020 Nairobi $ 1,000 Register to attend
Start Date: 03/02/2020 End Date: 07/02/2020 Nairobi $ 1,000 Register to attend
Start Date: 02/03/2020 End Date: 06/03/2020 Nairobi $ 1,000 Register to attend
Start Date: 06/04/2020 End Date: 10/04/2020 Nairobi $ 1,000 Register to attend
Start Date: 04/05/2020 End Date: 08/05/2020 Nairobi $ 1,000 Register to attend
Start Date: 01/06/2020 End Date: 05/06/2020 Nairobi $ 1,000 Register to attend

INTRODUCTION

This training course is an ideal presentation of using statistics methods in spatial data analysis. It presents the concepts used in geoscience with special emphasis on oil and gas exploration. The use of omnipresent Excel for the geospatial analysis for the initial applied research in geology and exploration. Advanced concepts of using free R software for geospatial analysis trough examples is also presented, with explaining of statistical methods used and the packages.  

This training course is designed to help professionals in data analysis, geologists and oil and gas professionals to remove the limitations of using an off-the-shelf software, which is quite helpful but it limits the ability of the professional using it to apply its knowledge and extend the models used, as the readymade software applies pre-designed algorithms and ‘’forces’’ the data into distributions applicable for the models. This training course will allow the professionals to understand how they can use free or low-cost software to extend the capabilities of commercial software and enable them to use their own ingenuity without limitations.   

COURSE OBJECTIVES

By the end of the course, participants should be able to:

  • Learn the concepts and methods of geostatistics
  • Understand the capabilities of Excel and R programming language
  • Acquire the knowledge of available R packages for spatial data analysis
  • Import, analyze and interpret results from spatial data
  • Perform Monte Carlo simulation, clustering analysis and other advanced techniques

DURATION

5 Days

WHO SHOULD ATTEND

This training course is designed for all professionals working in the field of data analysis, oil and gas exploration, geology and reservoir modelling and will greatly benefit:

  • Data Scientists
  • Data Analysts
  • Geologists
  • Petroleum engineers
  • Reservoir engineers
  • Other professionals involved in spatial analysis and oil and gas exploration

COURSE CONTENT

Day One: Geostatistics - Concepts and Introduction to Software

  • Basics of Geostatistics
  • Geostatistical reservoir modelling
  • Short introduction to Excel
  • Short introduction to R and R studio
  • Exercise: importing well log data into excel and creating GR vs Depth plot 
  • Exercise: importing well log data into R and initial analysis

Day Two: Spatial Data Analysis

  • Spatial Data Sampling
  • Spatial Resolution Gap
  • Spatial Weight Matrices
  • Basis of data analysis: statistical measures, correlation and autocorrelation
  • Exercise: determining correlation and autocorrelation in well log data using Excel
  • Exercise: Plotting Spatial connectivity

Day Three: Steps in Geostatistics - The Variogram and Kriging

  • Variogram and Modelling
  • Sampling for the Variogram
  • Nested Sampling
  • Geostatistical Prediction: Kriging
  • Exercise: Performing ANOVA in Excel, Kriging Example in Excel
  • Exercise: Variogram and Kriging in R studio

Day Four: Big Data Analytics and its Relation to Oil and Gas

  • Big Data Concepts
  • Clustering analysis
  • Spatial Variance and Covariance
  • Data distributions
  • Exercise: Variance and covariance calculation in Excel
  • Exercise: Clustering analysis in R studio

Day Five: Advanced Topics in Spatial Statistics

  • Bayesian Theory and Spatial Data
  • Monte Carlo Analysis
  • Markov Chains
  • Exercise: Monte Carlo Simulation for Oil and Gas reserves simulation in Excel
  • Exercise: Monte Carlo Simulation in R
  • Fuzzy Logic, Machine Learning and generative algorithms and the future of prediction

GENERAL NOTES

·         This course is delivered by our seasoned trainers who have vast experience as expert professionals in the respective fields of practice. The course is taught through a mix of practical activities, theory, group works and case studies.

·         Training manuals and additional reference materials are provided to the participants.

·         Upon successful completion of this course, participants will be issued with a certificate.

·         We can also do this as tailor-made course to meet organization-wide needs. Contact us to find out more: training@data-afriqueconsultancy.org

·         The training will be conducted at DATA-AFRIQUE TRAINING CENTRE, NAIROBI KENYA.

·         The training fee covers tuition fees, training materials, lunch and training venue. Accommodation and airport transfer are arranged for our participants upon request.

·         Payment should be sent to our bank account before start of training and proof of payment sent to: training@data-afriqueconsultancy.org