Oil & Gas

Optimizing oil production and health of equipment with big data.

Oil & Gas

Optimizing oil production and health of equipment with big data.

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INDUSTRY

Oil & Gas

  • The Oil & Gas industry has evolved in terms of instrumentation, process automation and collaboration- throwing up a lot of data from IoT enabled devices, sensors, weather and seismic monitoring. This data can be combined with data from market feeds, social media, customer information to provide fodder for new data insights. BryteFlow has the capability to ingest, replicate and prepare data from a huge variety of sources, making analytics-ready data available in real time for oil & gas companies.
  • Companies can log in data from oil wells defining operational set points for maximizing margins. They can thus receive alerts on deviations e.g. pump rate, fluid pressure etc. BryteFlow integrates with AI and ML software to ingest and prepare relevant data.
  • Data from equipment sensors and IoT enabled devices etc. can be used to maintain and repair equipment preventatively – increasing the availability and life of expensive equipment, failure of which can have a disastrous domino effect on operations. it also helps in maintaing safety in the field.
The role of data in better oil exploration

Oil and gas exploration activities generate a lot of data which can be instrumental in unearthing fresh oil deposits. Data from soil, weather and equipment can be collected analyzed to predict successful drilling outcomes and make informed, better decisions about drilling initiatives. BryteFlow replicates and prepares data drom diverse sources enabling the analysis of data for effective drilling operations.

Increasing oil output

Optimizing oil recovery by analyzing drilling, production and seismic data is key for oil & gas companies. This data opens a window for production engineers to enable them to understand when to make chandes to the oil reservoir and to oil lifting and extraction processes. Oil production can be predicted with big data techniques and if the forecast is not matching up to a predetermined output level then remediation measures for that oil well can be put in place. BryteFlow helps in ingesting data from seismic monitors, drilling eqipment and production figures to help oil & gas companies optimize output.

Preventative maintenance

Companies can use sensor and geological data to predict equipment failure which can save thousands of dollars by averting losses and increase availability and health of equipment. The data generated by various machines and equipment is replicated and prepared in real-time by BryteFlow for companies to use in predictive maintenance models.

Making reliable yield predictions for optimal lease bidding

Data from image files, seismic data and seisnic monitor data help to predict the yield of oil wells. This is useful for companies in determining how much to pay for multi-year leases for exploration and drilling rights. It helps in avoiding over-paying for leases and also to spot bargains at a discounted price. BryteFlow ingests and prepares this data for analytics to enable oil & gas companies to make informed decisions on leases.

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BryteFlow enabled this leading oil & gas company to empower a number of use cases with its data including monitoring and optimising of their oil rigs, collecting alarms and events from various sites etc.

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Getting data in real-time was made possible by BryteFlow for this oil and gas major.

The client was finding it challenging to access data and keeping a history of every transaction for dashboards was a challenge for the client. They tried several software, but there was a lot of manual coding involved to keep a consistent view with history of this data. Getting data optimised for dashboards on their S3 platform was also a major concern, as the dashboards were required to be real-time.

BryteFlow helped break down silos of SCADA data.

BryteFlow enabled the client to break down silos of SCADA data, permits data and other data silos and getting this data onto real-time dashboards and data science use cases. It also resulted in significant savings in costs with cheap storage and use of compute only when required, as orchestrated by the BryteFlow software. The data could be used for Predictive Maintenance and Condition based models as well, as being available for various use cases.

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