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This is a Remote Instructor Led Series (RILS) training. The remote classroom delivery is a modality that takes advantage of the instructor led training content, while allowing the same content to be delivered remotely. All training sessions will be delivered online with no face-to-face classroom attendance.
This course is intended for the user with fundamental Petrel modeling skills. The course covers basic geostatistics, data preparation, data analysis, facies and petrophysical modeling. You will learn different ways to create property models and how to condition models to existing models and secondary data. This course guides the user through concepts, algorithms and software functionalities in property modeling.
The first part of the course focuses on the usage of basic geostatistical tools, through data analysis. Also pre-modeling processes concerning well data preparation will be covered. Also here we will look into the first step of the property modeling workflow: upscale well logs to create single property values at well location for each cell. This will create hard data that will be used to populate the 3D grid with either deterministic or stochastic algortihms.
The second half of the course focuses on facies and petrophysical modeling workflows using stochastic methods as well as covering the usage of Kriging for continuous properties. Implementing Data analysis results and using secondary data to constrain the result will also be shown.
The Petrel Property Modeling - RILS course will be delivered over a total of twenty-four (24) Hours; i.e. in five (5) sessions lasting 4.5 to 5 Hours each.Each participantwill need the following for an optimal experience of the course:
Session 1 : Modules 1, 2 & 3
This session first introduces the Petrel reservoir modeling workflow - in particular, the Petrel Property modeling workflow - and presents the differences between Deterministic and Stochastic methods and the difference between Kriging and simulation. Then, the participants will learn how to prepare inputs for Property modeling through the different methodologies for generating lighological/sedimentological facies and petrophysical logs and through the Scale-up well logs process.Day 2
Session 2 : Modules 4, 5, 6 & 7
This second session covers geostatistical analysis fundamentals forming the basis of the property modeling tools and methods in Petrel. In addition to variogram analysis, the session will also provide an overview of the facies modeling process in Petrel: how to build a realistic facies model, perform a statistical discrete data analysis for preparing inputs, and quality control the results.
Session 3 : Modules 8, 9, 10 & 11
During this session, the participants will first learn about all the facies modeling techniques available in Petrel. Then, they will discover the basic concepts specific to each technique and apply the Sequential Indicator Simulation, Facies Object Modeling and Truncated Gaussian Simulation methods.Day 4
Session 4 : Modules 12, 13, 14 & 15
In addition to presenting the different ways of creating and applying trends in Facies Modeling algorithms, this session will focus on petropohysical modeling: the preparation of the input data needed, the overview of deterministic and stochastic approaches, the key features, strengths and issues with the Kriging algorithm.
Session 5 : Modules 16 & 17
In this final session, the participants will learn about the stochastic petrophysical modeling algorithms (Sequential Gaussian Simulation & Gaussian Random Function Simulation). They will also learn how to use various methods to identify the spatial variation of each petrophysical parameter and its correlations and how to use secondary information by applying Collocated Co-kriging, Bivariate distribution, and Local varying mean in the Petrophysical modeling process.
Development and exploration geologists, geophysicists, geochemists, petrophysicists, petroleum engineers, managers, reservoir engineers and technical personnel with prior experience in Petrel.
Petrel Fundamentals course or equivalent Petrel experience.
General knowledge of petroleum geology.
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