Abstract: Different probabilistic generative AI models are used to estimate wellhead fatigue damage. A load probabilistic generative AI model is used to determine the load for the wellhead based on the design of the wellhead and the metocean conditions for the wellhead in conjunction with the riser and facility information. A stress probabilistic generative AI model is used to determine the stress on the wellhead based on the design of the wellhead and the load for the wellhead. The fatigue damage on the wellhead is determined based on the stress on the wellhead and fatigue resistance parameters of the wellhead materials. The fatigue damage on the wellhead is used to make operational decisions for the wellhead.

Abstract: The wave characteristics of the water (e.g., ocean) above a wellhead may be used to determine a wave-induced wellhead fatigue damage rate for the wellhead, and the current profile of water around a riser connected to the wellhead may be used to determine a riser vortex-induced vibration caused fatigue damage rate for the wellhead. The wave-induced wellhead fatigue damage rate and the riser vortex-induced vibration caused fatigue damage rate for the wellhead may be combined to obtain the total wellhead fatigue damage rate for the wellhead, which may be used to make operational decisions, such as connections or disconnections, for the well.

Abstract: A computing system, method, and computer-readable medium includes a fatigue tracking service that assists with monitoring fatigue damage in a subsea wellhead system. The fatigue tracking service uses historical environmental data to determine a fatigue damage rate. The fatigue tracking service determines a fatigue allowance based upon the fatigue damage rate, a number of days assigned to a well operation, and a total allowable fatigue damage. The fatigue tracking service compares the fatigue allowance to measurements providing an accumulated fatigue damage to facilitate the monitoring of fatigue damage in the subsea wellhead system.

Abstract: A method for remotely evaluating fatigue damage of a subsea wellhead may include obtaining a plurality of values associated with measurements of a parameter associated with the fatigue damage of the subsea wellhead during field operations, wherein the measurements are measured by a sensor device positioned separately from and adjacent to the subsea wellhead; executing an algorithm using the measurements to generate a result; comparing the result of the algorithm with a range of acceptable values; and determining that the subsea wellhead has a potential failure when the result falls outside the range of acceptable values.

Abstract: A wellhead bracing system for a subsea well includes a base plate assembly and a wellhead brace assembly. The base plate assembly comprises a top surface, a bottom surface, a base plate aperture, and a plurality of anchor holes. The wellhead brace assembly comprises a vertical plate assembly having a cylindrical shape, a horizontal plate assembly to which the vertical plate assembly is attached, and a vertical support assembly attached to an outer surface of the vertical plate assembly and attached to the top surface of the horizontal plate assembly. Horizontal plate holes of the horizontal plate assembly are configured to attach to studs on the top surface of the base plate to secure the wellhead brace assembly to the base plate assembly. The wellhead bracing system slides onto a wellhead of the subsea well and braces an exterior of the wellhead.

Abstract: Metocean conditions for a wellhead, such as current profile and wave characteristics, are used to determine wellhead fatigue damage rate for the wellhead. The wellhead fatigue damage rate is determined using an interpolation approach or an extrapolation approach. In the interpolation approach, the metocean conditions of the wellhead are input into one of multiple clustered machine learning models to determine the wellhead fatigue damage rate. In the extrapolation approach, a curve is generated to fit cluster centers of the multiple clustered machine learning models, and the wellhead fatigue damage rate is determined based on the curve and the distance between the metocean conditions of the wellhead and null metocean conditions.

Abstract: The wave characteristics of the water (e.g., ocean) above a wellhead may be used to determine a wave-induced wellhead fatigue damage rate for the wellhead, and the current profile of water around a riser connected to the wellhead may be used to determine a riser vortex-induced vibration caused fatigue damage rate for the wellhead. The wave-induced wellhead fatigue damage rate and the riser vortex-induced vibration caused fatigue damage rate for the wellhead may be combined to obtain the total wellhead fatigue damage rate for the wellhead, which may be used to make operational decisions, such as connections or disconnections, for the well.

Abstract: Current profile of water (e.g., ocean) around a riser connected to a wellhead may be measured and used to determine integrated current load on the riser at any given moment in time. The integrated current load may be used to estimate the wellhead fatigue damage rate at that moment in time. The estimated wellhead fatigue damage rate may be used to make operational decisions for the well.

Abstract: The present embodiments disclose a device for dampening the lateral vibrations on a blowout preventer (BOP). The device is a cylinder with a support bar and a mass contained within. The mass can move along the support bar and press against springs, thus dampening the vibrations made against a BOP and wellhead.

Abstract: A wellhead bracing system for a subsea well includes a base plate assembly and a wellhead brace assembly. The base plate assembly comprises a top surface, a bottom surface, a base plate aperture, and a plurality of anchor holes. The wellhead brace assembly comprises a vertical plate assembly having a cylindrical shape, a horizontal plate assembly to which the vertical plate assembly is attached, and a vertical support assembly attached to an outer surface of the vertical plate assembly and attached to the top surface of the horizontal plate assembly. Horizontal plate holes of the horizontal plate assembly are configured to attach to studs on the top surface of the base plate to secure the wellhead brace assembly to the base plate assembly. The wellhead bracing system slides onto a wellhead of the subsea well and braces an exterior of the wellhead.

Abstract: Current profile of water (e.g., ocean) around a riser connected to a wellhead may be measured and used to determine integrated current load on the riser at any given moment in time. The integrated current load may be used to estimate the wellhead fatigue damage rate at that moment in time. The estimated wellhead fatigue damage rate may be used to make operational decisions for the well.

Abstract: A computing system, method, and computer-readable medium includes a fatigue tracking service that assists with monitoring fatigue damage in a subsea wellhead system. The fatigue tracking service uses historical environmental data to determine a fatigue damage rate. The fatigue tracking service determines a fatigue allowance based upon the fatigue damage rate, a number of days assigned to a well operation, and a total allowable fatigue damage. The fatigue tracking service compares the fatigue allowance to measurements providing an accumulated fatigue damage to facilitate the monitoring of fatigue damage in the subsea wellhead system.

Abstract: Methods, systems, and storage media for predicting physical changes to a wellhead coupled to a riser in an aquatic volume of interest are disclosed. Exemplary implementations may: obtain training data; obtain a machine learning algorithm; generate a riser response model by applying a machine learning algorithm to the training data; store the riser response model, obtain target environmental data, target tension data, and target mud data, generate predicted riser response data, transform predicted riser response data, generate a representation, and display the representation.

Abstract: Methods, systems, and storage media for predicting physical changes to a wellhead coupled to a riser in an aquatic volume of interest are disclosed. Exemplary implementations may: obtain training data; obtain a machine learning algorithm; generate a riser response model by applying a machine learning algorithm to the training data; store the riser response model, obtain target environmental data, target tension data, and target mud data, generate predicted riser response data, transform predicted riser response data, generate a representation, and display the representation.

Abstract: A system for use in monitoring operation of a rotor assembly is provided. The system includes a plurality of clearance sensors including at least a first clearance sensor configured to measure a distance between the first sensor and a surface of a lockwire tab, and a monitoring unit coupled to the plurality of clearance sensors, the monitoring unit configured to receive measurements from the plurality of clearance sensors, and determine whether a crack exists in the rotor assembly based on the received measurements.

Abstract: A system for use in monitoring operation of a rotor assembly is provided. The system includes a plurality of clearance sensors including at least a first clearance sensor configured to measure a distance between the first sensor and a surface of a lockwire tab, and a monitoring unit coupled to the plurality of clearance sensors, the monitoring unit configured to receive measurements from the plurality of clearance sensors, and determine whether a crack exists in the rotor assembly based on the received measurements.