Quantitative Reservoir Characterization

Trainer(s): Jaap Mondt
Duration: 5 days

Business context

Although a F2F course, use will be made of Moodle for the extra options it provides: a News Forum and a Discussion forum, plus extra files, and videos of interest for you to download. Therefore, check these fora regularly during the course.

What kinds of Geophysical Data could be used?
Various kinds of geophysical data are available. They are usually separated into Seismic and MultiPhysics data. Seismic is, without any doubt, the main method used in the oil and gas industry. But MultiPhysics data (gravity, magnetics, electrical, electromagnetics, spectral, etc.) is the main source of ---- information in shallow subsurface applications (engineering, mapping pollution, archaeology, etc.) and at the early basin reconnaissance stage. However, seismic has its limitations and therefore also multi-physics methods are used successfully as complementary tools in subsurface evaluation. In combination with seismic data, they can significantly reduce the uncertainty of subsurface models as they measure different physical properties of the subsurface.

Seismic data and Processing
From seismic we not only need to obtain the structure that could contain hydrocarbons, but also the rock properties so we can decide on whether we are dealing with reservoir rocks (sandstone, carbonates, even shales), sealing rocks (shales, salt) or source rocks (shales, coals). To know what type of rock is present is important, but also what its porosity is and whether it is fractured, as that is important for permeability (How easy do the hydrocarbons flow through the rocks). To obtain accurate information on the rock properties we need to consider two-way elastic wave propagation. This is, for example, done in so-called Reverse Time Migration (RTM) used for "true" amplitude imaging. Considering elastic propagation, which includes mode conversion, is also needed when we analyze the (pre-stack) amplitude variation with offset (AVO) or more accurately defined as amplitude variation with angle of incidence (AVA).

Quantitative Reservoir Characterization
Clearly most information will be obtained from seismic data. From quantitative analysis of pre-stack seismic data, elastic properties of the reservoir will be derived. But these need to be translated in to rock properties relevant for exploitation, that is porosity and fluid saturations. That means that a rock-physics model need to be chosen. For clastic reservoirs that is relatively easy, for carbonate reservoirs it is much more non-unique. Machine Learning, which is part of Artificial Intelligence is applied more and more in all domains of the geosciences, including reservoir characterization. Therefore, I have included applications for classification and clustering of seismic reservoirs, using open-source software like Weka, Keras and TensorFlow.

The Course
The above items will be dealt with in the course; by presentations and discussions, watching videos and by making many practical exercises. Also, each day contains a quiz which is meant to reinforce the learning. The quiz consists of multiple-choice questions. For each question, all answers can be tried. The idea is that if the answer is not correct, one can go back to the course material to find out what the right answer should be. So, it is not an exam.

Email: j.c.mondt@planet.nl
Website: www.breakawaylearning.n

Who should attend

Geologists, geophysicists, petroleum and reservoir engineers, involved in exploration and development of hydrocarbon fields. That means not only those involved in the production side but also geoscientists designing the acquisition of seismicandnon-seismic data needed for Quantitative Interpretation.

Course content

Quantitative Reservoir Characterisation Teamwork will consist of:
Summary/Presentation of learning points previous day

Day 1
09:00-09:30Biography, Program, Moodle, 52 Things, How a Geophysicist, Teams
09:30-10:30 Geophysical Methods, Seismic Acq & Proc, Workflow, Seismic for QI
10:30-11:00 Exercise: Resolution I (paper)
11:15-13:00Rock Physics
13:00-14:00 Lunch
14:00-14:30 Videos: Rock Physics (26:30)
14:30-15:00 Effective Media
15:15-16:00 Seismic Resolution: Point-Spread or Resolution Functions
16:00-17:00 Exercise: (Resolution) Resolution II (computer)
17:00-17:30Team a: Preparation Summary of day 1

Day 2
09:00-09:30Team a: Summary of day 1
09:30-10:00 Structural & Stratigraphic Interpretation, Tuning: Simmons & Backus
10:00-11:00 Exercise: (Tuning) Tuning Wedge, Tuning AVA (computer)
11:15-13:00 Exercise: Effective Media (paper)
13:00-14:00 Lunch
14:00-14:30 Videos: EAGE Gassmann Fluid Replacement, EAGE AVO
14:30-15:30 Effective Media, Anisotropy, AVA
15:45-17:00 Exercise: AVA (computer)
17:00-17:30Team b: Preparation Summary of day 2

Day 3
09:00-09:30Team b: Summary of day 2
09:30-10:00 EI, EEI, EPI, Lambda-Mu-Rho
09:30-11:30 Exercises: AVA (ΔRPP, ΔRSS) (computer)
10:30-11:00 Inhomogeneity & Anisotropy
11:00-11:15 Refreshments
11:15-12:00 ML Tutorial I
12:00-13:00 Exercise: AVA Rps (computer)
13:00-14:00 Lunch
14:00-15:00 Videos: EAGE Wave Equation AVO, Activation Functions
15:00-15:30 Exercise: V-NMO-azi
15:45-16:15 Exercis: AVA VTI HTI (computer)
16:15-17:00 Exercise: AVA HTI Ortho (computer)
17:00-17:30Team c: Preparation Summary of day 3

Day 4
09:00-09:30Team c: Summary of day 3
09:30-10:15 AVAz Fractures, Machine learning,
10:15-11:00 Exercise: (Fractures) AVA lith1,lith2 (computer)
11:15-12:00 ML AVO Tutorial II, Inversion
12:00-13:00 Exercise: ML Classification (computer)
13:00-14:00 Lunch
14:00-14:30 Videos: Classification, Inversion vs Machine learning I, Clustering
14:30-15:30 Exercise: ML AVA Tutorial II (computer)
15:45-16:00 SOM
16:00-16:30 Exercise: ML Clustering (computer)
16:30-17:00 Supervised, Unsupervised and Semi-Supervised learning
17:00-17:30Team d: Preparation Summary of day 4

Day 5
09:00-09:30Team d: Summary of day 4
09:30-10:00 Videos: Hydrocarbon Indicators, Gassmann
10:00-11:00 Exercise: Gassmann Fluid Replacement (computer)
11:15-11:45 ML Keras, TensorFlow
11:45-13:00 Exercise: ML Regression (computer)
13:00-14:00 Lunch
14:00-14:15 Video: You ain't seen nothing yet.
14:15-15:00 Exercise: Google Colab I
15:00-15:15 Refreshments
15:15-15:30 Gassmann subsalt rock
15:30-16:00 Exercise: Google Colab II
16:00-16:30Course evaluation


Learning, methods and tools

At the end of the course participants will have a solid foundation in Seismic Quantitative Interpretation. The aim of the course is to provide a solid conceptual understanding without going into mathematical detail. It uses a mixture of lectures, practical exercises and direct (workshop-like) participant involvement, complemented with case histories. Use of laptops for exercises and WIFI internet access in the classroom is mandatory.

The course can be customized to meet specific needs of participants.