Our platform enables your team to execute a series of
workflows covering several disciplines for large volumes
of data—such as a facility maintenance initiative—all in
a single interface.
(Obviously.)
Powered by deep learning for computer vision
and natural language understanding, our machine learning
models are able to understand and extract context from
your unstructured data. This enables you to combine it
with your structured data to generate a comprehensive
view of your digital and physical assets.
You decide whether your project requires a particular module, a workflow capsule (several modules linked together), or an entire System of Intelligence. Stack capability as your goals and process require.
Deploying IIOT infrastructure,
RCM software, and other initiatives for your facilities?
You might be missing a critical element. When a
processed vibration signal predicts a compressor might fail,
you’ll need to know where that compressor is located,
what other equipment is connected to it,
and what spare parts were needed in the past.
That information provides the necessary context for
intelligent decision making, and it may be locked in
scanned engineering drawings, purchase orders, and maintenance reports.
The Facility Maintence SOI creates that essential context
through a package of natural language understanding and
computer vision technologies to classify engineering
drawings, retrieve instrument tag data from P&IDs,
fingerprint all of your documents, and more.
The first step for comprehensive maintenance planning is establishing the physical and operating context of your assets. Designed according to the ISO14224:2016 framework.
Site Register Module
Equipment Register Module
BOM Generation Module
Most long-standing O&G companies
have many shelved, bypassed and near field
opportunities hidden in document, log, seismic and
production data repositories. Uncovering them, however,
typically requires lengthy processing of old typewritten
documents, multidisciplinary analysis, root cause analysis,
and so forth. For each discovered opportunity, it’s
necessary to run a screen from then to now to check
if it was subsequently exploited. Attempting to do
all of this in a predominantly manual workflow may
require many tedious man-years of work.
Machine-based Exploitation (MBX) uses a mix of
machine learning technologies to systematically
work through this plethora of analytical steps in a
fraction of the time it would take a large team of
human experts. All uncovered opportunities are
classified as Fast, Intermediate or Slow Hydrocarbon
(FISH) depending on their accessibility. MBX allows
you to quickly add hundreds or thousands of barrels
to your production in a fraction of the time
required by conventional methods.