SOLUTION BRIEF

SambaNova
Dataflow-as-a-ServiceTM
for Oil, Gas, and Energy

Accelerate seismic analysis, increase
accuracy, and discover more resourcesin less time1

SAMBANOVA DATAFLOW-AS-A-SERVICET FOR OIL, GAS, AND ENERGY

Seismic analysis is the most common process currently
used to identify subsurface natural resources, such as oilor gas deposits. Successfully identifying these deposits
represent billions of dollars in potential profit for oil, gas,and energy producers.
Correctly predicting the location of these accumulations is bothan extremely time consuming and highly complex process. It
involves utilizing highly trained specialists who need to analyze
hundreds of km3 of detailed geospatial image data. The
accuracy of the predictions they make is critical. An incorrect
prediction represents billions in missed opportunities and a
massive, wasted capital investment of $100M or more.
In order to maximize the success of these predictions, oil and gasorganizations invest months in highly complex and timeconsuming imaging analysis that not only creates a massive
bottleneck in the full geoscience decision workflow, but typically
only achieves a 30% success rate in correctly predicting thelocation of these valuable deposits.

The Seismic Analysis Workflow

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SAMBANOVA DATAFLOW-AS-A-SERVICE FOR OIL, GAS, AND ENERGY

True resolution deep learning computer
vision can accelerate the seismic analysis
process and increase the accuracyof discovery predictions
Deep learning computer vision models have the potential togreatly improve and accelerate the analysis of seismic data bycapturing even higher dimensional information, such as
amplitude versus offset, as part of the patterns that experts needto identify. This enables organizations to reduce the seismic
analysis process from nine weeks to as little as one week.

There is limited availability of the specializedlabeled data which is necessary to train
seismic analysis computer vision models
Computer vision models have the potential to greatly improve andaccelerate seismic analysis, delivering significant value to the energy
industry.
However, training these models requires specialized data labeling ofup to 40% of the total volume of the massive seismic data sets which
can be hundreds of km3 in size.
This labeling process is highly complex, manual, and time consuming.
It requires significant effort from subject matter experts who have thetraining and skills necessary to recognize and label the complex 3Dfeatures that are critical to seismic analysis and prediction. However,these experts are almost always focused on analyzing seismic datafor predictions, not labeling data.
As a result, most organizations do not have the necessary
data needed to train effective computer vision models
that can improve the seismic analysis process.

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SAMBANOVA DATAFLOW-AS-A-SERVICE FOR OIL, GAS, AND ENERGYSambaNova significantly accelerates
training computer vision models
SambaNova utilizes a proprietary approach that greatly
accelerates the complex and time consuming model training
process by reducing the labeled data required to train a modelby 97.6%, while simultaneously resulting in more detailed 3D
features that can greatly improve the accuracy of the analysis.

Limitations of GPU-based AI infrastructure prevent
analyzing high resolution 3D images, impacting
accuracy
While 2D networks can be useful for seismic analysis, they can lead to artifacts andcomplicated post-processing issues that impact usability of the prediction.
Large 3D networks overcome these limitations by leveraging multi-scale, 3D
correlations to identify the most relevant features in the data to improve the qualityof the predictions. Many of these features are 3D themselves, so 3D data is critical
to identifying them.
However, the 3D image resolution that can be processed with GPUbased AI infrastructure is extremely limited, leading to tiling or lowering
the resolution of images, which both increases implementation
complexity and significantly impacts accuracy.

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SAMBANOVA DATAFLOW-AS-A-SERVICE FOR OIL, GAS, AND ENERGYSambaNova DataScale is uniquely capableof handling images of 5123 and beyondⓇThe SambaNova platform eliminates the limitations of GPU based
systems that cannot process true resolution images without
significant downsampling or tiling, which increases cost andreduces accuracy. The higher memory capacity of theSambaNova DataScale platform enables organizations toanalyze images at 5123 resolution and beyond, identifying
natural correlations and with higher quality of features, improving
downstream analysis and accuracy of results.
The higher quality of features from 5123 resolution images
enables seismic analysts to make more accurate predictions, inless time, resulting in billions of dollars of potential profit from thediscovery of additional resource deposits

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5123

Seismic Data Terms of Use: The New Zealand government collects seismic and well data and releases it to the public after a data
confidentiality period of a few years. The purpose of releasing these data to the public is to promote development of New Zealand’s
petroleum and mineral resources. These data can be used by students, academics, and industry provided publications and presentations
acknowledge New Zealand Petroleum and Minerals (NZPM) for providing data.
Label data source: AICrowd5

Next steps
If you would like to learn more
about how organizations arepreparing for the future,
visit SambaNova.ai

Learn more at SambaNova.ai
linkedin.com/company/sambanova@SambaNovaAI
info@sambanova.ai

Customers turn to SambaNova to quickly deploy state-of-the-art AI capabilities to meet the demands of the AI-enabled world. Our
purpose-built enterprise-scale AI platform is the technology backbone for the next generation of AI computing. We enable
customers to unlock the valuable business insights trapped in their data. Our flagship offering, Dataflow-as-a-ServiceTM, overcomesthe limitations of legacy technology to power the large complex foundation models that enable customers to discover new services
and revenue streams, and boost operational efficiency. Headquartered in Palo Alto, California, SambaNova Systems was foundedin 2017 by industry luminaries, and hardware and software design experts from Sun/Oracle and Stanford University. Investors include
SoftBank Vision Fund 2, funds and accounts managed by BlackRock, Intel Capital, GV, Walden International, Temasek, GIC, Redline
Capital, Atlantic Bridge Ventures, Celesta, and several others.
Copyright © 2022 SambaNova Systems, Inc. All rights reserved.

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