Course News
Course News 14
Part 1:
Day 1: 3 hours interactive presentation & discussion,
Day 1&2: 4 hours self-paced exercises+learning-reinforcement quiz 1
Part 2:
Day 3: 3 hours interactive presentations & discussion
Day 3&4: 4 hours self-paced exercises+learning-reinforcement quiz 2
Part 3:
Day 4: 3 hours interactive presentation & discussion,
Day 4&5: 4 hours self-paced exercises+learning-reinforcement quiz 3
Part 4:
Day 5: 3 hours interactive presentations & discussion
Day 5&6: 4 hours self-paced exercises+learning-reinforcement quiz 4
Part 5:
Day 7: 3 hours interactive presentation & discussion,
Day 7&8: 4 hours self-paced exercises+learning-reinforcement quiz 5
Course News 13
The main reason is that as it is a E-Learning, self-paced course the client can adapt the time schedule (say, when staff also must work half days).
Two examples of the alternatives:
Machine Learning for Geophysical Applications (MLGA)Day 1: 3 hours interactive presentation & discussion
Day 1&2: 4 hours self-paced exercises+learning-reinforcement quiz 1
Part 2
Day 3: 3 hours interactive presentations & discussion
Day 3&4: 4 hours self-paced exercises+learning-reinforcement quiz 2
Part 1
Day 1: 3 hours interactive presentation & discussion,
Day 1&2: 4 hours self-paced exercises+learning-reinforcement quiz 1
Part 2
Day 3: 3 hours interactive presentations & discussion
Day 3&4: 4 hours self-paced exercises+learning-reinforcement quiz 2
Part 3
Day 4: 3 hours interactive presentation & discussion,
Day 4&5: 4 hours self-paced exercises+learning-reinforcement quiz 3
Part 4
Day 5: 3 hours interactive presentations & discussion
Day 5&6: 4 hours self-paced exercises+learning-reinforcement quiz 4
Part 5
Day 7: 3 hours interactive presentation & discussion,
Day 7&8: 4 hours self-paced exercises+learning-reinforcement quiz 5
Course News 12
The change in reflection strength with increasing angle of incidence (AVA) can provide important information with respect to the properties of reservoirs. Although formulations exist for strong contrasts across an interface, weak contrasts are more common in sedimentary sequences. This leads to formulations, through linearization, which provides insight in the influence of the elastic parameters across the interface on the AVA behaviour. These expressions can also be extended to include anisotropy of the media and in case of weak anisotropy the expressions still allow insight into the influence of the anisotropy contrasts across the interface on the AVA behaviour.
An interesting question is: Can we express the AVA behaviour in terms of the isotropic expressions using generalised elastic properties?
The answer is yes, we can with so-called pseudo-elastic properties. These are function of the true elastic properties modified by expressions involving the anisotropy parameters.
An example for VTI:
with
The use of pseudo-isotropic parameters is added to the Quantitative Reservoir Characterisation Blended Learning and F2F Course.
Course News 11
A new short course has been developed on the use of Machine Learning for prediction of facies along a borehole. Based on a reasonable number of interpreted wells various algorithms can be compared in classifying wellbore profiles of “new” unlabelled wells. In the course it will be shown that limited training data (1 well) results in poorer predictions than when more labelled data can be used for training. It also is found that using a Deep Neural Net results in the best classification. In addition, the use of semi-supervised learning, often needed in geoscience applications because of limited availability of labelled data, is discussed. The course is available in a Face-to-Face and an E-learning version.
Course News 10
I am happy to announce that I have been officially nominated as short course instructor with the EAGE.
Course News 9
To each course a multiple-choice quiz has been added. The aim is not only to provide a test of what has been learned but also as a way of enhancing the participants understanding of the subjects.
Course News 8
Based on Open Source software, a one-day (8hrs) Face-to-Face and a one-month duration (8hrs equivalent) E-learning course called “Introduction to Machine Learning for Geophysics” have been developed. Both courses contain power point presentations with references to publications together with computer-based exercises and Machine Learning related videos.
The exercises deal with a genuine geophysical issue, namely predicting lithology and pore fluids, including fluid saturations. The input features are Acoustic and Shear Impedances, Vp/Vs ratios and AVA Intercept and Gradients. Several exercises deal with pre-conditioning the datasets (balancing the input categories/instances, standardization & normalization of features, etc), the others apply several methods to classify the “instances” (Machine Learning terminology for cases): Bayes, Logistic, Multilayer Perceptron, Support Vector, Nearest Neighbour, AdaBoost, Trees, etc. This for supervised and unsupervised applications. Also, non-linear Regression is used to predict fluid saturations
Course News 7
July 24th 2019
Update on Machine Learning courses for Geophysics
Based on Open Source software I have developed a one-day (8hrs) Face-to-Face and a one-month duration (8hrs equivalent) E-learning course called “Introduction to Machine Learning
for Geophysics”. These courses are made up of power point presentations with references to publications together with computer-based exercises and Machine Learning related videos.
Course News 6
June 4th 2019
Machine Learning
Based on Open Source software I have developed an “Introduction to Machine Learning” together with computer-based exercises. The Introduction will be included in all my courses, but the exercises will be topic
specific. Apart from the Face-to-Face / Classroom courses I plan to develop very short E-learning courses (the equivalent of a one-day course) on the use of Machine Learning in Basic Geophysical Data Acquisition and Processing, Advanced Seismic
Acquisition/Processing/Interpretation, Quantitative Reservoir Characterization and Non-Seismic Data Acquisition and Processing.
Course News 5
Feb. 20th 2019
Course News 4
Dec. 24th 2018
In the courses the new approach of “Machine Learning” will be included. First in the form of lectures, followed later by exercises. Machine learning, also defined as Artificial Intelligence or Algorithms, relate to the use of statistical methods
to derive from large (learning) data sets relationships (“correlations”) between observations and subsurface properties, for example lithological facies or fluid saturations.
This is a clear break with the traditional approach in which
we ‘totally’ rely on understanding the physics of the subsurface processes (say visco-acoustic wave propagation) causing the observations or data (amplitude anomaly) at the surface.
In Machine Learning we mimic human “brain processes”
to learn from a very large number of labelled cases how to explain a new case. In analogy with when you have seen more cases, had a longer learning trajectory, you are better able to evaluate a new case. You might not fully understand why
events happen, but you have learnt how to deal with them, i.e. you can classify them say as beneficial or threatening.
Course News 3
Aug. 17th 2018
In the “Advanced Seismic Data Acquisition and Processing” Course two new topics have been introduced: 1) Blended acquisition and 2) Penta-source acquisition.In blended acquisition sources are fired closely in time such that the records do overlap. To separate the responses of the subsurface two methods can be applied.
One method is called “dithering” in which a “random” variation is applied to the auxiliary sources with the result that their responses are non-coherent and can be considered as random noise and be removed/suppressed in processing. As the “dithering” for each auxiliary source is known, it can be removed resulting in coherent records for each, leaving the other source records incoherent.
The other method uses “seismic apparition”. In this method a periodic variation, be it time delay or amplitude scaling, is applied to each source and each source response can be made to “appear” at the expense of the responses of the other sources this is often done in the KF domain.
The Penta-source (Polarcus), consists of 6 arrays (spaced 12.5 m apart), which are used in more than one source configuration resulting in 5 single sources. For operational reasons they are fired in the following sequence: (1,2), (3,4), (5,6), (2,3), (4,5). They can be used with a shot-point interval of 12.5 m and “dithered” over 1 s, or with a shot interval of 62.5 m and “dithered” over 5 ms, allowing deblending of the shot records. Note that the first part (1 or 5 s) of the record is “clean” and do not require deblending.
In both cases also the streamer spacing might vary, where an increasing separation from centre spread outwards is beneficial for a high-resolution shallow image.
Course News 2
In addition to the “Effective Media and Anisotropy” an extensive AVA modelling exercise has been implemented. It allows investigating the various aspects of AVA analysis, namely what rock and fluid properties can be derived from observing the
Amplitude changes with Angle of incidence on the interface between two rocks. It not only deals with density, P- and S-wave velocities, but also with anisotropy. From the anisotropy, not only average properties of sub-seismic-scale fine layering,
but maybe even more important fracture orientation and density can be derived. These are important in relation to where to drill production wells relative to injection wells (think of fractured carbonates).
In the exercise the AVA response
of different rock interfaces (shale, sand, salt, limestone, etc) can be modelled using Vertical Transvers Isotropy (VTI) for fine layering and Horizontal Transvers Isotropy (HTI) related to fractures. Although more than one fracture set do
occur in nature, the exercise is limited to one fracture set, based on the idea that most often it is the present stress regime that keeps fractures open in the minimum horizontal stress direction.
Course News 1
For the “Quantitative Reservoir Characterisation” (QRC) course a new module has been designed concerning Effective Media and Anisotropy. The Earth is often too complicated in terms of inhomogeneities to fully honour it in modelling wave propagation and inversion. The solution has been to replace the real earth by a so-called effective medium. This effective medium allows an “appropriate approximation” of the wave propagation and inversion. Although an approximation, it is still appropriate for solving the issue at hand.One main application is related to the concept of anisotropy. Anisotropy occurs when, for example, we deal with thin sequences of different rocks or in case of fractures. If these “inhomogeneities” are on a sub-seismic wave-length scale the inhomogeneous medium can be replaced by a homogeneous medium with “anisotropic" character. The anisotropy is then an appropriate replacement of the sub-seismic scale inhomogeneities and allows derivation of the rock properties using seismic observations. These anisotropic properties tell us that the wave propagation is not only a function of location (the commonly considered large scale inhomogeneity), but also at each location a function of the direction of propagation.
In the exercise, you will learn how effective media velocities are calculated. There are namely two ways and how in modelling these effective anisotropy parameters can be calculated. You also will derive anisotropy directly from seismic data.
(email: j.c.mondt@planet.nl).
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