Well Control Space Out: A Deep-Learning Approach for the Optimization of Drilling Safety Operations

Abstract

As drilling of new oil and gas wells increase to meet energy demands, it is essential to optimize processes to ensure the health and safety of the crew as well as the protection of the environment. Drilling operations represent a dynamic and challenging environment with natural and mechanical factors that need to be closely managed. Well control refers to the technique employed while drilling for balancing the hydrostatic and formation pressures to prevent the influx of water, gas, or hydrocarbons that would ultimately result in an uncontrolled flow to the surface. In the event of a well control incident, the crew must take proper and prompt actions to mitigate the risks and shut-in the well. In this study, we introduce the Well Control Space Out technology, an internet-of-things (IoT) environment that couples cameras and an edge server to implement stateof-the-art deep-learning models for the real-time processing of video images recording the drillstring. The computational models automatically perform object detection to keep track of key drilling rig components. The results from the video analysis are displayed on a dashboard describing the state and steps to follow in a well control incident without the need for any time-consuming, manual calculations. The internet-of-things edge foundation laid in drilling can be seamlessly expanded to other upstream sectors, where time-sensitive, critical decisions can be made in real-time, in the field, closer to operations. Finally, this technology can be seamlessly integrated with the current technologies to develop an automated closed-loop control system.INDEX TERMS Automation, deep-learning, computer vision, edge computing, internet-of-things, oil and gas drilling, well control.

Advances in Well Control: Early Kick Detection and Automated Control Systems

“…A regional convolutional neural network is used for image classification. This technology, which ensures that the BOP valve is not closed across a tool joint, is a key component towards the implementation of an automated closed-loop control system [3]. The technology is represented with a schematic in Figure 5.…”

“…A diverter assembly is used to divert a gas kick encountered at shallow depth in a safe direction when only a conductor casing is installed as the surrounding formation tends to be too weak to contain a shut-in kick. Generally, an annular-type preventer is installed on top of the conductor pipe beneath which a diverter line of large enough diameter to sustain unrestricted flow is run to a pit [1,3,4]. The position of a diverter system is shown in Figure 1(b).…”

A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector

“…The application of AI for accurate sand production prediction in wells [140] and for shale well production [141] have also shown promising results. The use of discrete event simulation DT in studying the operational risk in oil sand mining and processing of bitumen in response to geological uncertainty was shown to be a good coordination tool [142], enhancement of drilling operations using IoT and AI models [143] and detection of fault in submersible screw pumps in oil wells [144]. The use of AI in the detection of casing damage due to non-uniform in-situ stress has been explored in [145]- [147].…”

Application of Machine Learning Methods in Modeling the Loss of Circulation Rate while Drilling Operation

“…Most artificial intelligence (AI) and machine learning (ML) techniques can potentially solve practical problems by learning from large historical data sets, something that conventional analytical models cannot do. , The applications of AI in drilling operation activities have evolved over recent years due to their flexibility in classification, optimization, prediction, and selection . These applications include, but are not limited to, identification of formation lithology, estimation of pore and fracture pressures during the drilling operation, , real-time prediction of drilling fluid properties, formation identification while drilling using mechanical surface parameters, early warning signs detection while drilling horizontal wells, use of an Internet-of-things (IoT) environment integrated with cameras and high-computation edge server to implement a deep learning model for proper drill string space out when a well control incident occurs during drilling, employing of raw drilling data to estimate the drilling bit- wear in real time using a bidirectional long short-term memory-based variational autoencoder, and determination of downhole vibrations while drilling surface hole sections to mitigate premature drill string failures …”