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Oil&Gas meters processing with Artificial Intelligence and Computer Vision

Azati helped a Canadian customer to develop an AI-powered service for automatic data processing from meters that measured produced oil & gas resources using machine learning and computer vision technologies.


Petroleum products are the basic fuel for most types of transport, as well as raw materials for chemical production. Natural gas is one of the best types of fuel for domestic and industrial needs, polymers are made from gas, helium is released, which is used in the production of high-precision equipment and in the space industry.

The oil & gas industry plays a leading role in the economy and is closely linked to other industries. This is a complex system, including the raw materials extraction, the production of fuel purification and its further processing. An important role in this redistribution is given to modern specialized technologies.

A Canadian oil & gas customer service company turned to Azati to automate reading data from meters.


Today, any industrial complex of the oil and gas industry must be fully automated. So numerous controllers, meters and block modules are created. Production automation leads not only to decrease the influence of the human factor, but also to increase efficiency.

The customer turned to us to search and develop approaches to automate the data reading from graphs that are printed by equipment for accounting for extracted resources (meters).

The task included graphic information processing(recognition and reading of curves on the graph), printed data (stickers with printed text, graphics, such as barcodes), as well as handwritten data (dates, numbers, notes from equipment operators).


01. Challenge

The equipment prints data on round discs, which are scanned and sent to the system for subsequent reading and data processing. Thus it was necessary to find models and solutions that could work with such incoming data.

To overcome this challenge we have developed an algorithm that can unfold a round disk and convert the image into a rectangular shape to make it possible to trace and read the coordinates (x, y) of the fixed curves.

02. Challenge

The equipment prints several curves indicators that characterize the conditions for resource extraction on one graph. Each curve has its own color. For some of them, the background color could overlap with the line color, so it was necessary to find a solution that would allow to highlight the curves on the graph, bypassing this extraneous “noise”.

We have done work on training the neural network with test materials, which could select curves of different colors on the incoming graph with sufficient accuracy, considering all the features and potential interference.

03. Challenge

The customer has many partners around the world, who use different equipment. The problem arose while processing various data formats.

We have trained a neural network that, based on the characteristics studied, could conclude that the input data belonged to one or another client, and, depending on this, send it to the necessary data processor.

04. Challenge

Another problem was related to handwriting recognition because of the human factors: excitement, haste, absent-mindedness, etc.

The problem was solved by searching for data regions. From a few regions (bounding box), an attempt was made to recognize handwritten data (dates and numbers) using a trained neural network based on Google Tesseract.


We started the project with creating a successful pilot prototype for reading curves on a graph. This task turned out to be feasible, and we started development to recognize other aspects and details on the input data (graphs).

The Canadian side was involved in project management, prioritization of activities and coordination of the delivery schedule. And the client, in turn, decided on how closely the potential result meets the project’s goals and is suitable for marketing analysis.

Our responsibility included:

Activities coordination


Search for solutions and model implementation

Verification and support of product quality

Data recognition services were developed in parallel, iteratively, according to the schedule agreed with the client.


The project was done by a team of ML specialists.

The product is a set of services that receives a scan of the input data and the expected result on the output, recognized and calculated by the developed model based on artificial intelligence.

The services were integrated into the client’s infrastructure and launched in the cloud.

The developed services included the
following functionality:

  • 90% accuracy of barcodes processing
  • Above 80% accuracy of line processing
  • Processing of handwritten data (dates and numbers) varied greatly from the quality of the input data, from 30 to 70+%. Everything rested on the human factor, i.e. the accuracy of the data in the form fields, blots and corrections, handwriting features.





For now, we are still working on handwritten data processing enhancement.The customer is supposed to analyze and develop a strategy on how to get away from fixing marks manually by operators and transfer this step into the digital era (for example, fix the date and time through the operator’s mobile phone).

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