Implement Deep Learning Solutions for Oil and Gas Industry

Customer Background

Our client is a highly reputable oil and gas consulting firm headquartered in the United States. Their extensive portfolio of services includes exploration and production consulting, reservoir engineering, drilling optimization, and economic analysis. With the constantly evolving landscape of the energy industry, the client urgently requires up-to-date knowledge and insights to stay competitive.

  • Industry

    Oil and gas

  • Technologies / Platforms / Frameworks

    Python, YOLO V3, OCR, MS SQL, Azure, Amazon SageMaker


Our client faced several challenges that hindered their data accuracy and efficiency. They had to dedicate significant time and resources to review critical documents to identify inaccuracies. This process was time-consuming, tedious and prone to human error. If errors went unnoticed, it could result in inaccurate data being circulated, which could lead to costly mistakes and potential legal liabilities.

The client suffered financial losses and reputational damage due to inaccurate data, leading to missed opportunities and potential legal liabilities. Urgent action was needed to prevent further losses and ensure long-term success.


With the collaborative and consultative approach of our data scientists, we worked closely with the client to understand their unique business needs. After thoroughly discussing their pain points, we offered them deep learning solutions.

Data Preprocessing and feature extraction

SageMaker was utilized to preprocess and clean the data, ensuring its readiness for analysis. It provided tools for data transformation, feature engineering and dimensionality reduction, preparing the data for the YOLO algorithm. By utilizing SageMaker’s capabilities, the data was appropriately formatted and optimized for the subsequent computer vision tasks.

Model training and optimization

We leveraged SageMaker to train and optimize the YOLO algorithm. It provided a managed environment for training the computer vision model at scale, using the client’s labeled data. Our data engineers trained the YOLO model SageMaker’s distributed computing resources.

Deep learning solution for automated error detection

Softweb Solutions implemented an automated error detection system using the YOLO V3 algorithm. It is a state-of-the-art object detection system that uses deep neural networks to identify objects in images and videos. This provided the client with an automated system that detects and highlights errors in their documents.

The implementation of this error detection system provided our client with a highly efficient and time-saving solution. By simply adding the details of the document into a user-friendly form, the algorithm takes over and meticulously scans the entire document for errors. Any identified errors are promptly highlighted, enabling the client to swiftly identify and rectify them. This not only ensures the integrity and accuracy of the data but also reduces the risk of circulating erroneous information.


Softweb Solutions’ team of professionals demonstrated their ability to understand our specific business requirements and tailor solutions accordingly. They offered a comprehensive solution that included leveraging computer vision technology. We were pleasantly surprised by their attention to detail, professionalism and timely communication, surpassing our expectations. We achieved improved workflow efficiency and error detection through the implementation of Softweb Solutions’ customized computer vision services.


  • Improved data accuracy: The YOLO algorithm allows the client to detect errors with a high level of accuracy. This helps the client maintain a high level of quality in their work and avoid costly mistakes.
  • Increased efficiency: By automating the error detection process, our client can complete reviews more quickly and efficiently, reducing the turnaround time.
  • Cost savings: By eliminating manual error detection, the client reduced their labor costs and improve profitability.
  • 100%

    data accountability

  • 82%

    improved collaboration

  • 40%

    enhanced productivity

Architecture diagram


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