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Machine Learning Applications for Upstream Oil and Gas – Yogi Schulz


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These translations are done via Google Translate

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More and more oil and natural gas producers are investing in Artificial Intelligence/Machine Learning (AI/ML) applications with mixed results. There’s lots of enthusiasm and a fair bit of vendor hype. Nonetheless, adding AI/ML functionality to existing applications or building entirely new AI/ML applications can produce significant benefits.

“We have grown rapidly by developing effective AI/ML applications for oil and natural gas producers,” says Philippe Herve, SVP of Energy and Sustainability at SparkCognition. “We differentiate ourselves through our patented AI/ML software that accelerates development and automated technology that enables scaling.”

The prerequisites to AI/ML application success typically involve advancing the producer’s digital transformation with a focus on:

  1. Gathering more operational data.
  2. Increasing the timeliness of data.
  3. Managing that data for higher accuracy and completeness.
  4. Integrating data from many sources effectively.
  5. Improving data accessibility, typically through better data analytics.
  6. Consciously promoting multi-disciplinary work.

What affordable AI/ML oil and natural gas applications can produce an attractive net benefit?

Increase profitable production

Most oil and natural gas producers regularly see production enhancement and plant throughput improvement proposals that look good but don’t produce the promised net revenue increase after implementation.

Adding AI/ML functionality to production optimization applications can weed out proposals that won’t be profitable and improve recommendations that can be profitable. The benefits include:

  1. Reducing investment for profitable proposals.
  2. Avoiding engineering time in developing unprofitable proposals.

Reduce unscheduled downtime

Most oil and natural gas producers want to move beyond condition-based maintenance or fixing components once they break. They are frustrated by the variety and frequency of various outages. Too many production facilities operate materially below their nameplate capacity.

Adding AI/ML functionality to predictive maintenance applications can increase the number of days of advance notice of component failure. The benefits include:

  1. Largely eliminating the risk of waiting for parts or staff to complete a repair.
  2. Reducing the length of an outage for repairs. That shorter time decreases revenue lost and generally reduces the cost of the repair.

Reduce operating costs

Oil and natural gas producers are perpetually unhappy with their operating costs. With the benefit of hindsight, they see missed opportunities in operating cost statements.

Adding AI/ML functionality to analyze operating costs closer to real-time achieves the following benefits:

  1. Identifying opportunities to reorganize work to reduce driving time.
  2. Reducing trucking costs for oil, clean water and produced water disposal.
  3. Improving the utilization of equipment.
  4. Reducing consumption of materials.
  5. Reducing component inventories.

“SparkCognition delivers AI/ML solutions that deliver business value,” says Curt Richtermeyer, Executive Vice President of Global Sales. “Our approach reduces implementation risks and speeds deployments.”

GLJ
GLJ

Improve equipment utilization

Oil and natural gas producers contract with service companies for drilling and service rigs. Low equipment utilization leads to lower service company net income and higher producer costs.

Adding AI/ML functionality to better categorize and analyze non-productive time and invisible lost time with more consistency and granularity achieves the following benefits:

  1. Reducing travel for staff.
  2. Reducing mobilization time for heavy equipment and its crew.
  3. Improving work processes when rigs are in use.
  4. Improving the utilization of drilling and service rigs.

Improve safety

Oil and natural gas facilities are complex and dangerous with exposed rotary equipment, high pressure, high-temperature operations, large vehicles and hazardous chemicals. Producers invest significant resources to ensure their staff, suppliers, and neighbours are safe.

Adding AI/ML functionality to employee monitoring systems:

  1. Focuses supervisors on important alerts and reduces the distraction of false alerts.
  2. Reduces lost-time incidents.
  3. Reduces injuries and deaths.

“SparkCognition’s award-winning AI/ML solution for safety monitoring employs 120+ models to analyze real-time video for various infractions,” says Philippe Herve, SVP of Energy and Sustainability. “For example, the management console displays alerts for staff not wearing PPE or entering a restricted zone and can shut down process equipment if necessary.”

Reduce GHG emissions

Oil and natural gas producers are working to reduce their greenhouse gas (GHG) emissions by:

  1. Turning methane venting into CO2 flaring. That reduces GHG emissions by 95%.
  2. Capturing methane venting and flaring into sales of natural gas. That turns GHG emissions into revenue.
  3. Reducing fugitive GHG emissions from wellhead and processing equipment.
  4. Reducing their energy consumption.

Adding AI/ML functionality leads to better measurement, estimating and reporting to identify additional opportunities to reduce GHG emissions.

About Yogi Schulz

Yogi Schulz is an information technology consultant who works extensively in the petroleum industry to select and implement administrative, operations, and geotechnical systems. He writes regular articles about developments in the energy industry and technology.

You can contact Yogi Schulz through his LinkedIn profile at this link.



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