A Zero Harmful Discharge Strategy in Practice on the Norwegian Continental Shelf, a Driver for Continuous Improvement

Abstract One of the crucial components of well integrity evaluation in offshore drilling is to determine the cement bond quality assuring proper hydraulic sealing. On the Norwegian Continental Shelf (NCS) an industry standard as informative reference imposes verification of cement length and potential barriers using bonding logs. Traditionally, for the last 50 years, wireline (WL) sonic tools have been extensively used for this purpose. However, the applicability of logging-while-drilling (LWD) sonic tools for quantitative cement evaluation was explored in the recent development drilling campaign on the Dvalin Field in the Norwegian Sea, owing to significant advantages on operational efficiency and tool conveyance in any well trajectory. Cement bond evaluation from conventional peak-to-peak amplitude method has shown robust results up to bond indexes of 0.6 for LWD sonic tools. Above this limit, the casing signal is smaller than the collar signal and the amplitude method loses sensitivity to bonding. This practical challenge in the LWD realm was overcome through the inclusion of attenuation rate measurements, which responds accordingly in higher bonding environments. The two methods are used in a hybrid approach providing a full range quantitative bond index (QBI) introduced by Izuhara et al. (2017). In order to conform with local requirements related to well integrity and to ascertain the QBI potential from LWD monopole sonic, a wireline cement bond log (CBL) was acquired in the first well of the campaign for comparison. This enabled the strategic deployment of LWD QBI service in subsequent wells. LWD sonic monopole data was acquired at a controlled speed of 900ft/h. The high-fidelity waveforms were analyzed in a suitable time window and both amplitude- and attenuation-based bond indexes were derived. The combined hybrid bond index showed an excellent match with the wireline reference CBL, both in zones of high as well as lower cement bonding. The presence of formation arrivals was also in good correlation with zones of proper bonding distinguishable on the QBI results. This established the robustness of the LWD cement logging and ensured its applicability in the rest of the campaign which was carried out successfully. While the results from LWD cement evaluation service are omnidirectional, it comes with a wide range of benefits related to rig cost or conveyance in tough borehole trajectories. Early evaluation of cement quality by LWD sonic tools helps to provide adequate time for taking remedial actions if necessary. The LWD sonic as part of the drilling BHA enables this acquisition and service in non-dedicated runs, with the possibility of multiple passes for observing time-lapse effects. Also, the large sizes of LWD tools relative to the wellbore ensures a lower signal attenuation in the annulus and more effective stabilization, thereby providing a reliable bond index.

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Implementation of Risk and Working Environment Analyses for Offshore Installations on the Norwegian Continental Shelf

10.3940/rina.icsot15k.2015.04 ◽

2015 ◽

Author(s):

K W Yang ◽

Keyword(s):

Continental Shelf ◽

Working Environment ◽

Offshore Installations ◽

Norwegian Continental Shelf

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An Overview of Regulatory Principles for Safety Demonstration on the Norwegian Continental Shelf

Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference ◽

10.3850/978-981-14-8593-0_4055-cd ◽

2020 ◽

Author(s):

Christine L. Berner Nyvik ◽

Andreas Falck ◽

Roger Flage

Keyword(s):

Continental Shelf ◽

Norwegian Continental Shelf

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Novel Application of Artificial Intelligence with Potential to Transform Well Planning Workflows on the Norwegian Continental Shelf

10.2118/206339-ms ◽

2021 ◽

Author(s):

Jon Gustav Vabø ◽

Evan Thomas Delaney ◽

Tom Savel ◽

Norbert Dolle

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Continental Shelf ◽

Data Science ◽

Search Algorithm ◽

Solution Space ◽

Good Decision ◽

Trade Offs ◽

Well Trajectory ◽

Norwegian Continental Shelf

Abstract This paper describes the transformational application of Artificial Intelligence (AI) in Equinor's annual well planning and maturation process. Well planning is a complex decision-making process, like many other processes in the industry. There are thousands of choices, conflicting business drivers, lots of uncertainty, and hidden bias. These complexities all add up, which makes good decision making very hard. In this application, AI has been used for automated and unbiased evaluation of the full solution space, with the objective to optimize the selection of drilling campaigns while taking into account complex issues such as anti-collision with existing wells, drilling hazards and trade-offs between cost, value and risk. Designing drillable well trajectories involves a sequence of decisions, which makes the process very suitable for AI algorithms. Different solver architectures, or algorithms, can be used to play this game. This is similar to how companies such as Google-owned DeepMind develop customized solvers for games such as Go and StarCraft. The chosen method is a Tree Search algorithm with an evolutionary layer on top, providing a good balance in terms of performance (i.e., speed) vs. exploration capability (i.e., it looks "wide" in the option space). The algorithm has been deployed in a full stack web-based application that allows users to follow an end-2-end workflow: from defining well trajectory design rules and constraints to running the AI engine and evaluating results to the optimization of multi-well drilling campaigns based on risk, value and cost objectives. The full-size paper describes different Norwegian Continental Shelf (NCS) use cases of this AI assisted well trajectory planning. Results to-date indicate significant CAPEX savings potential and step-change improvements in decision speed (months to days) compared to routine manual workflows. There are very limited real transformative examples of Artificial Intelligence in multi- disciplinary workflows. This paper therefore gives a unique insight how a combination of data science, domain expertise and end user feedback can lead to powerful and transformative AI solutions – implemented at scale within an existing organization.

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