Saudi Arabian Oil Company

Bashaer Abdullah Alhammad

Hasan Ali Al-Hashmy

Aziz U Rehman

Rashed Alnaimi

A system for determining a corrosion level of a pipeline includes a corrosion coupon received from the pipeline, a database, a first computer, a second computer, a neural network, and a third computer. The database includes existing data indicative of corrosion of a plurality of previously analyzed pipelines. The first computer receives corrosion data of the corrosion coupon and uploads the corrosion data to the database. The second computer uploads calculations performed on the corrosion data to the database. The neural network receives the corrosion data and the calculations from the database and outputs a corrosion level of the pipeline based on the corrosion data and the calculations. Further, the neural network is trained on the existing data from the plurality of previously analyzed pipelines so that the corrosion level is based on a combination of the corrosion data, the calculations, and the existing data. Finally, a third computer receives the corrosion level and generates a report of the corrosion level of the pipeline.

Digitalization and automation of corrosion coupon analysis with a predictive element

A system and method apply an input noisy ultrasonic test (UT) scan image to an input layer of a convolutional neural network, generate a feature map using a convolutional layer, pool the feature map using a pooling layer, apply the pooled feature map to a fully connected layer, generate a de-noised UT scan image, and output the de-noised UT scan image from an output layer.

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Jubail, Saudi Arabia

Bashaer Abdullah Alhammad