The worldwide demand for oil has been rising rapidly for many decades, being the first
indicator of economic development. Oil is extracted from underneath reservoirs found below …

Anomalies in oil-producing wells can have detrimental financial implications, leading to
production disruptions and increased maintenance costs. Machine learning techniques offer …

Different clusters of abnormal activities often arise within same temporal domain of drilling
operations. This contrasts with employing simplified scenarios, such as anomaly detection …

The standard torque and drag (T&D) modeling programs have been extensively used in the
oil and gas industry to predict and monitor the T&D forces. In the majority of cases, there has …

Trusting a machine-learning model is a critical factor that will speed the spread of the fourth
industrial revolution. Trust can be achieved by understanding how a model is making …

This paper focuses on the use of artificial intelligence (AI) and machine learning (ML)
algorithms to implement anomaly detection and shows how this concept can be extended to …

Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

Detecting anomalies in time series data of hydrocarbon reservoir production is crucially
important. Anomalies can result for different reasons: gross errors, system availability …

Diagnostic plots, introduced by KS Chan, are widely used to determine excessive water
production mechanisms. In this paper, we introduce a computer vision model that is capable …

The prompt detection and diagnosis of anomalies in oil wells are fundamental to reduce
production losses, maintenance costs and to avoid environmental damage. In this paper, a …