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The authors of this paper propose a novel work flow for the problem of building intelligent data analytics in heavy-oil fields.

This paper discusses how machine learning by use of multiple linear regression and a neural network was used to optimize completions and well designs in the Duvernay shale.

This paper presents an analytics solution for identifying rod-pump failure capable of automated dynacard recognition at the wellhead that uses an ensemble of ML models.

As you read the examples in this section, you will see that a change is already under way in that the methods that are being used are increasingly not oil-and-gas-specific but instead follow patterns that are being used in other industries.

This paper presents the data collected by an ultrasound downhole scanner, demonstrating a novel method for diagnosing multilateral wells.

This paper describes challenges faced in a company’s first deepwater asset in Malaysia and the methods used to overcome these issues in the planning stage.

The oil industry is currently undergoing a technological transformation that will add value, improve processes, and reduce cost. Future drilling engineers will have knowledge of robotics, automation, and organizational efficiency, which is highly appealing for recruitment.

This paper discusses shale creep and other shale-deformation mechanisms and how an understanding of these can be used to activate shale that has not contacted the casing yet to form a well barrier.

Hydraulic isolation of wells drilled with nonaqueous fluids (NAFs) relies heavily on eliminating mud from the annuli before placing cement.

This paper demonstrates a new way to create gas-tight seals during well abandonment, overcoming the limitations of traditional methods and reducing the operator’s liability and potential environmental impact after decommissioning has been completed.