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Efficiency gains in inversion based interpretation through computer
- 1. Efficiency gains in inversion based interpretation through computer driven classification Dustin T. Dewett
- 2. Outline Part One: Background • SOM: A Brief Explaination • Inversion: Relevant Background (very brief) Part Two: SOM and Inversion • Concept of SOM Classification of Inversion • Real Data Example (brief) Part Three: Questions and Wrap-up 20170531 Efficiency Gains 2
- 3. SOM: Briefly Explained Suppose we have a table of data… • Let the columns be different attributes • Let the rows be what we want to classify • We can classify each object by the attributes that are ascribed to them. • The key parameters are the number of neurons, the maximum number of epochs, and the learning rate. 20170531 Efficiency Gains 3 1 2 3 4 5 OBJ1 5 3 3 7 9 OBJ2 6 3 6 8 6 OBJ3 9 2 6 9 4 OBJ4 1 4 9 6 4 OBJ5 1 2 4 6 2 OBJ6 2 8 4 5 6 OBJ7 3 5 5 3 1 OBJ8 4 8 1 2 2 OBJ9 5 6 1 2 6 Attributes
- 4. SOM: Briefly Explained • The system (SOM) will map the input attributes onto an output space, • Generate an initial state (e.g. pseudo random, true random, or predefined) • Then iterate the following: • Select training data at random • Compute the winning neuron • Update all neurons • Stop when input is classified or maximum epochs has been reached • In general, the neurons are interconnected in a defined topology. 20170531 Efficiency Gains 4
- 5. Graphical PCA Example 20170531 Efficiency Gains 5
- 6. Relevant Inversion Background 20170531 Efficiency Gains 6 Seismic data Well data Seismic Inversion Geophysical Properties (AI, SI, ρ) Rock Physics Model Geological Properties (fluid, lithology, etc.) Well data
- 7. Relative vs Absolute Inversion Relative • Poor fidelity over long time intervals (a function of the frequency content of the data) • Scaling of the output values is only proportional to the real values • Suffers more from tuning effects • Easy to compute • No initial model dependence Absolute • High fidelity over long time intervals • Correct (or more correct) output scaling • Suffers far less from tuning effects • Difficult to compute (both in computational time and knowledge) • Dependent on initial model 20170531 Efficiency Gains 7
- 8. A Note on Coloured Inversion Coloured Inversion • Tends to outperform other relative inversion methods (e.g. Runsum or Recursive Inversion) • Attempts spectrum flattening through wavelet estimation by smoothing the seismic spectrum • Matches the output seismic impedance spectrum to the well impedance spectrum • Quick and cheap, • Simple and Robust, • No background model, • No wavelet derivation • Well data are only used for spectral matching 20170531 Efficiency Gains 8
- 9. Outline Part One: Background • SOM: A Brief Explanation • Inversion: Relevant Background (very brief) Part Two: SOM and Inversion • Concept of SOM Classification of Inversion • Real Data Example (brief) Part Three: Questions and Wrap-up 20170531 Efficiency Gains 9
- 10. Goal: Empower a non-specialist to leverage inversion results in an effective way. 20170531 Efficiency Gains 10
- 11. Relative Seismic Inversion Results* 20170531 Efficiency Gains 11 AI GI *results courtesy of R. Meza • Given multi-volume results from seismic inversion, typically specialists classify the results based on their prior knowledge and experience combined with an understanding of the rock physics. • However, specialists may be limited in time and more so in supply. • Therefore, if a more unbiased and repeatable approach can be used to classify the inversion response, efficiency can be gained through specialist engagement at key times with specific areas of interest.
- 12. Relative Seismic Inversion Results* 20170531 Efficiency Gains 12 AI GI *results courtesy of R. Meza • Given multi-volume results from seismic inversion, typically specialists classify the results based on their prior knowledge and experience combined with an understanding of the rock physics. • However, specialists may be limited in time and more so in supply. • Therefore, if a more unbiased and repeatable approach can be used to classify the inversion response, efficiency can be gained through specialist engagement at key times with specific areas of interest.
- 13. Workflow • Begin with inversion products, • Classify the results with a computer based classification algorithm, • Scan the results for anomalous classifications (neurons), • Focus the specialist’s engagement on anomalous areas. 20170531 Efficiency Gains 13
- 14. Begin with Inversion Products 20170531 Efficiency Gains 14 AI GI *results courtesy of R. Meza
- 15. Look for anomalies 20170531 Efficiency Gains 15
- 16. But what are these classes really? 20170531 Efficiency Gains 16 AI GI (Modified from Whitcombe and Fletcher, 2001)
- 17. But what are these classes really? 20170531 Efficiency Gains 17 AI GI (Modified from Whitcombe and Fletcher, 2001) It’s not quite this easy.
- 18. Maximum attribute 1 and minimum attribute 2 But what are these classes really? 20170531 Efficiency Gains 18 Minimum attribute 1 and maximum attribute 2 Maximum attribute 1 and maximum attribute 2 Minimum attribute 1 and minimum attribute 2 Data located about the middle point on both attributes
- 19. 20170531 Efficiency Gains 19 Most anomalous class (of 16). Determined after a scan of the data one class at a time. Using more classes can allow for finer distinctions between different data points.
- 20. 20170531 Efficiency Gains 20 Five classes (of 64) that show the most anomalous events in the data.
- 23. Summary By leveraging a self-organized map and inversion products together, it is possible • to more rapidly localize interpretation in a dataset while leveraging a higher degree of geologic understanding typically found in prospect interpreters (who commonly lack in-depth QI knowledge). • to make potentially substantial gains in both efficiency and interpretation quality • to increase the engagement of non-specialists. But always remember to: • engage a specialist to fully understand the physics behind the SOM classified anomalies. 20170531 Efficiency Gains 23
- 24. Acknowledgements R. Meza for providing a suitable relative inversion data set. M. Florez for feedback and critique. F. Hilterman for encouraging publication. BHP Billiton for encouraging the work and providing needed resources for completion. 20170531 Efficiency Gains 24
- 25. References Whitcombe D. and John G. Fletcher (2001) The AIGI crossplot as an aid to AVO analysis and calibration. SEG Technical Program Expanded Abstracts 2001: pp. 219-222. Connolly, P.A., 1999, Elastic impedance: The Leading Edge, 18, 438-452, doi: 10.1190/1.1438307. Kohonen, T., 1989, Self-organization and associative memory, 3rd Edition, Springer, New York. 20170531 Efficiency Gains 25
- 26. Questions?
Editor's Notes
- Good afternoon, my name is Dustin Dewett, and I am both a geophysicist with BHP Billiton and a Ph.D. student at the University of Oklahoma. Today, I will be specifically talking about using relative inversion products and self-organizing maps together. However, the general principle applies to both model based and relative inversion as well as other methods of computer based classification.
- Briefly, I will begin with an explanation of what a SOM is and how it works. Then, I will briefly touch on some seismic inversion background that is specific to the products that I used in this work. In part two, I will go over the concept of using a SOM together with inversion products and how it can increase efficiency. The efficiency that I am specifically referring to is related to idle time when waiting on a specialist or increased quality of interpretation through better engagement of a general interpreter. As I go through this section there will be a real data example where this technique was applied.
- If we are given an arbitrary set of data, where each data object is a function of several data attributes, then we can apply a classification scheme to those data. As the number of attributes becomes larger, it becomes significantly more efficient to leverage computer based schemed to automate this process. With respect to a SOM, there are several key parameters that affect the result: the number of neurons (or how many classes you will end up with), the maximum number of epochs (or how many iterations the SOM will run), and the learning rate (or the distance that a neuron can move for a given epoch).
- In a simple picture, we begin with input data and a set of starting parameters (discussed is in last slide). We also will need a starting topology and how to seed the neuron (to determine how the neurons are placed). In this example, I have a linear topology with pseudo random seed values. I then randomly choose a data point from the input set and move the closest neuron toward that point. Notice that the neurons are linked together. After a number of iterations, a solution will converge and individual neuron movement will be small. So how neurons are placed, where they are placed, and the key parameters discussed previously are important to the final result.
- Often SOMs are compared to PCA (principle component analysis). Where PCA is linear, however, SOMs are very non-linear. Here are two movies where the input data are arranged in letters placed horizontally.
- Here is the basic idea behind inversion. There are several points where a specialist would need to be engaged.