Geostatistics - Software Application for Geospatial Analysis
Course Info
Code IND02-104
Duration 5 Days
Format Online
Course Summary
When it comes to industries where it is necessary to have advanced knowledge of geology, such as oil and gas, it is crucial to conduct geostatistical analysis to build confidence in the area and allow for accurate planning of business functions.
Geospatial analysis is the process of gathering data using a variety of methods and analysing it to gain an in-depth understanding of a geographical location. Spatial analysis is commonly used in alignment with models such as Monte Carlo to predict data over a wide-spread area without having to physically sample each individual location. These processes combined allow for an organisation to accurately review the environment and proceed with their next steps.
There can be many limitations faced when conducting spatial analysis, but the type of software used is no longer one of these. There are many free and low-cost software options that are extremely effective to use. Excel and RStudio are the most popular programs for data analysis. It is essential for professionals to be highly competent in using these software, as they will allow for the most freedom when it comes to inputting data, and provide the most accurate results.
- To understand the importance of geostatistics.
- To describe the use of geoscience in relation to oil and gas.
- To assess the concepts and methods of geostatistics.
- To utilise coding software such as RStudio to compile and analyse data.
- To explain the capabilities and limitations of excel and RStudio.
- To acquire available R packages for spatial data analysis.
- To identify the benefits of conducting spatial data analysis.
- To accurately import, analyse and interpret results from spatial data.
- To compile various sources of data with varying degrees of uncertainty.
- To understand advanced concepts techniques, including Monte Carloe simulation and clustering analysis.
This course is designed for anyone with the responsibility of analysing geostatistical data. It would be most beneficial for:
- Data Analysts
- Data Scientists
- Geologists
- Software Engineers
- Petroleum Engineers
- Geospatial Analysts
- Geographic Information Systems (GIS) Officers
- Nature Recovery Advisors
This course uses a variety of adult learning styles to aid full understanding and comprehension. Participants will be provided with datasets of collected samples to later analyse using various models and methods.
They will be supplied with all the necessary equipment and software to carry out the given learning exercises. Combined with presentations, practical demonstrations and activities, participants will have ample opportunities to develop a well-rounded understanding of the concepts of geostatistics. They will also be able to utilise the provided data to conduct their own analysis using Excel and Rstudio to develop the related practical skills.
Course Content & Outline
- Defining geostatistics.
- The vitality of understanding geostatistics in different industries such as oil and gas.
- The four types of geostatistical reservoir modelling.
- Reviewing the available data analysis programs.
- The advantages and disadvantages of using coding software, such as Excel and RStudio.
- The concept of spatial data.
- How spatial data can provide in-depth knowledge of locations.
- Collecting spatial data samples.
- Minimising spatial resolution gaps.
- Using spatial weight matrices to quantify the spatial relationships within the sampled data.
- The principles of data analysis - statistical measures, correlation and autocorrelation.
- Creating variograms to describe the relationship of spatial data.
- Collecting samples for the variogram model.
- Understanding the concept of nested sampling.
- The benefits of using nested sampling.
- Describing kriging.
- Utilising kriging to predict the values of unsampled locations.
- The concept of big data.
- Using clustering analysis to explore occurring groups within datasets.
- Conducting clustering analysis using RStudio.
- Understanding the difference between variance and covariance in relation to spatial data.
- Calculating the variance and covariance.
- The ways data can be distributed.
- Understanding the Bayesian theory in relation to data collection.
- The concept and principles of the Monte Carlo simulation.
- The advantages of using the Monte Carlo model for outcome prediction.
- Utilising the Markov chain and Monte Carlo model simultaneously to build confidence in results.
- Advanced machine learning and generative algorithms for the future of statistical prediction.
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