4. Spectral Decomposition AASPI AASPI 2019 Workplan Kurt J. Marfurt Marcilio Matos Bo Zhang Sumit Verma Jie Qi OU AASPI Team

Tech transfer and user help to folks in Houston and OKC Laoshan Jie Qi Laoren With a new baby coming last August, Jie Qi decided it was best to forgo any job offers and stay in Norman with his wife and new daughter. In October, his spouse finished her PhD in Petroleum Engineering and is now working in Houston. Jie remains a postdoc for now, but will spend 5-days in Norman and 9-days in Houston with the objective of better transferring our technology, identifying technology headaches and shortcomings, defining new research objectives, and providing sponsor support. If you are in Houston or Norman, Jie can come out for the price of a good lunch. For other applications, we ask that you cover his travel expenses. Postdoc Jie Qi will spend every other week in Houston Invite him to make a presentation, discuss future needs, or to provide one-on-one help

Stop traveling Documentation boy!

Original SEP-type format Minimize intermediate files and format conversion Original SEP-type format Petrel format (completed 2018) Other formats for 2019? AASPI data Compute attributes ZGY data Compute attributes Shell data Compute attributes G-Insights data Compute attributes Kingdom data Compute attributes Conventional trace-by-trace formats Data and headers are well-defined Alternative data formats have padded traces Alternative data formats retain dead trace and mute header keys to honor “no permit” zones Brick formats Need an i/o library (ideally one that runs on Windows and Linux) Are always padded traces Need to search for dead traces and mute zones.

Extend our graphical capability Allow examination of the SOM or GTM attribute vectors To better build the link from seismic attributes to different facies, we now see the attribute responses of four key facies. All attribute are normalized using z-score. As expected, similar facies (colors) on the SOM map have similar attribute responses. We see the sample vector from the sand-filled channel deposits, has a similar response to that of facies 3, which is the sandy overbank complexes. The inter-channel overbank complex and the mud-filled sinuous channel complex are in similar facies (blue to purple colors). We can also see the difference in seismic amplitudes of the multistoried channel (more chaotic) and older sand filled channel (more flat and higher amplitude).

Predict pore pressure using multi-attribute classification Prediction of pore pressure in the Sichuan Basin using Eaton’s equation Shale Porosity Shale Slowness (DT) Density Porosity Pore pressure gradient Overburden gradient (PSI/ft) Select pure shale in a well to analyze the relationship between porosity and overburden pressure. use well log data (p-wave velocity and density) to compute porosity based on local experimental equation. Compute pore pressure gradient based on Eaton’s equation for the well log. Then we can compute the pore pressure gradient to all shale that marked by geobodies using slowness (1/velocity) and density from seismic prestack inversion. (Courtesy Jie Qi, OU)

Predict pore pressure using multi-attribute classification PPG 1.15 1.00 Select pure shale in a well to analyze the relationship between porosity and overburden pressure. use well log data (p-wave velocity and density) to compute porosity based on local experimental equation. Compute pore pressure gradient based on Eaton equation for the well log. Then we can compute pore pressure gradient to all shale that marked by geobodies using inversed velocity and density from seismic prestack inversion. Figure shows pore pressure gradient of a target horizon. (Courtesy Jie Qi, OU)

Predict pore pressure using multi-attribute classification (use prestack inversion to map geobodies, predict velocity and density) Then we can compute pore pressure gradient to all shale that marked by geobodies using inversed velocity and density from seismic prestack inversion. We use seismic amplitude and attributes classification methods to estimate shale geobodies as we have done for salt, mtd, volcanic sed, and other facies. (Courtesy Jie Qi, OU)

I demand a unified machine learning interface! som3d gtm3d gmm3d kmeans3d pca3d psvm3d pnn3d rf3d

Modify projection and machine learning algorithms to read in ascii-format data from spread sheets Define facies based on geologic parameters measured at wells and correlate to A√(k and engineering parameters (cf Amanda Thomsen et al. URTec/GSOC presentation) Thickness, P-wave impedance Maturation Vsh VOM Statistical measures of attributes about wells correlated to well head initial pressures (cf. Gabriel Machado AASPI poster) GLCM energy k1 curvature -60ᵒ azimuthal fault density.

Traditional shallow learning vs. deep learning Seismic Interpretation Decision making Oil No Oil Shallow Learning using Attributes Oil No Oil We will be needed! Deep Learning using Raw Images We will be replaced! Geoscientists have been using neural networks for over 20 years. Kurt Marfurt has used this image in talks to professional societies on his DISC tour in 2018. One vision of the future is that deep learning software will tell us where to drill for oil in addition to what news to read on Google, what music to buy from the Apple Store, and what movie to watch on Netflix. The interpreters chair is now empty. My vision is a little less ambitious, with the computer taking over monotonous, repetitive tasks. The example of driving a car to work, leaving home at the same time, taking the same route, and parking in the same parking lot is a repetitive task. Repetitive tasks for today’s seismic interpreter include picking another horizon, picking another fault, tying one more well. If you work for a seismic processing company and need to define the top and base of salt for subsequent depth migration, picking the extent of the salt is a repetitive process. No interpreter will miss such processes. For these simple “classifications” (the green horizon or not the green horizon) the interpreters job is one of quality controlling the result – accepting, rejecting, or modifying the “classification.” With the computer conducting these repetitive tasks, the interpreter now has the time to evaluate alternative hypotheses of reservoir quality, preferred drilling locations, and optimized completion strategies. (modified from Seif, 2018)

Continued attribute assessment using GMM and PNN Cumulative distances using GMM n 1 2 3 4 5 6 7 Attributes  3 1,6 2,5,7  3,4,6,8 2,3,6,7,8 2,3,6,7,8,9 1,2,5,6,7,8,9 Distance  1.93 2.12 1.59  1.27 2.38 2.98 2.76 𝑂 𝑛 𝐵𝑒𝑠𝑡 𝑐𝑜𝑚𝑏≡𝑎𝑟𝑔( max 𝑛=1,2,..𝑁 ( 𝑂 𝑛 )) “Best” attributes: 2. Energy deviation 3. Covariance of dip and energy 6. Spectral bandwidth 7. Spectral roughness 8. Reflector convergence 9. Coherence In this example, you can see the average distance of the best n=2 attribute combinations are attributes 1,6 resulting in O2=2.12. In contrast, the best n=4 attribute combinations are attributes 3,4,6,8 resulting in O4=1.27. The best n=5 attribute combinations are attributes 2,3,6,7,8 resulting in O5=2.38. The highest distance is for n=6 and attributes 2,3,6,7,8,9 with O6=2.98. We will use these six attributes to separate our three facies.

Short term software interface plans Demo by Rafael tomorrow! Display 102-104 labeled images User Interface Scripts (Python 3.7) This image shows our short term plan to provide a software interface to AASPI sponsors. TensorFlow was developed by the Google Brain team and later released under the Apache 2.0 open-source license on November 9, 2015. Nvidia and perhaps other hardware vendors provide optimized versions of this software. In terms of complexity, it is somewhat like R, Matlab, and other programs – easy for a grad student to pick up (because they have the time and inclination), but perhaps too demanding for the working professional geologist or geophysicist. We’ll start by building a graphical user interface (or GUI) in C++ using the FoxToolkit software we use for other AASPI applications. The GUI will “control” most of the subsequent processes, including display of the results using python scripts. If we are able to find industry or private foundation funding, we will build a more general, stand-alone program with increased features that may be able to access and augment a data base either on an OU server, in the cloud, or with the AAPG and SEG professional societies, open to all universities and research institutions at first, and once established, to the public at large. Inception V-3 CNN model Classified images

Collaboration with other universities

4. Spectral Decomposition AASPI 2019 Workplan Suggestions from the floor?