A significant effort is made by the industry through analyses and field monitoring to ensure delivery of safe and reliable wells. Fatigue analysis is an important aspect of well integrity assurance. Structural fatigue damage arises from stress changes caused by environmental cyclic loads acting on the riser system. In practice, the conductor-soil interaction under cyclic loading is modeled using the soil resistance-displacement (P-y) springs. Use of an appropriate soil model is essential for accurate determination of the fatigue damage. The American Petroleum Institute recommendations (API 2011) for P-y curves, which are often used for conductor-soil interaction analysis, have originally been developed for piled foundation and are inappropriate for well fatigue analysis. To that end, a new approach was developed by Zakeri et al. (2015) to derive P-y curves specifically for well fatigue analysis. Ultimate performance of each soil model can be determined and verified with field monitoring. This paper presents results of a field monitoring campaign for a well drilled in 354 ft water depth within a complex seabed stratigraphy comprising sands (loose to dense) and clays (very soft to stiff). Design, calibration and verification of the riser/conductor structural model using field data are presented in a companion paper (Ge et al. 2017). Herein, the effect of soil modeling on wellhead fatigue is discussed and predictions made with the API (2011) and the Zakeri et al. (2015) soil P-y springs are compared to field monitoring data. For the case presented herein, the results indicate that the Blowout Preventer (BOP) stack motion response is significantly affected by the soil stiffness and modeling methods. The predictions made with the Zakeri et al. (2015) model provided BOP response similar to those observed in the field both above and below the mudline. Whereas, the analyses done with the API (2011) model significantly overestimated the 'measured' conductor fatigue life above the mudline and underestimated it below. The results of this monitoring program are a step forward in better understanding system behavior of offshore wells.
In-situ riser structural monitoring has been used by a number of operators and drilling contractors to support drilling operations. The measurements have been used to understand the level of accuracy present in the up-front fatigue analysis and to confirm the integrity of the drilling systems, especially during extreme environmental events. However, the field data analysis requires detailed understanding of drilling riser modeling, loading conditions, operations, sensor characteristics and signal processing. Several methods have been developed to calculate stresses and fatigue damages in the riser using measured motion data. Careful selection of the analysis approach will determine the system configuration and validity of the measured results. Mode shape matching has been widely used for the data analysis where riser response is dominated by Vortex Induced Vibration (VIV). Another riser fatigue method has been recently developed and is based on analytical transfer functions used to convert measured accelerations into curvature. Another method considers FEA-based transfer functions. Some of these methods perform fatigue calculations in time domain while some others in frequency domain. Each method exhibits benefits and limitations depending on the characteristics of the measured riser response. Selecting the most appropriate fatigue methodology depends on the riser response and instrumentation system design. In this paper, three fundamentally different riser fatigue methods utilizing measured motion data are described and compared with each other. The advantages and disadvantages of each approach are evaluated and recommendations are provided for when each method should be considered.
The objective of the wellhead fatigue joint industry project (JIP) is to provide a measurement-based foundation for drilling riser and wellhead modeling practice in the oil and gas industry and hence to ensure that the fatigue response assessment is performed with adequate but not overly conservative analysis parameters. To this end, the JIP utilizes field measurements from ten (10) drilling campaigns in GoM and North Sea. In order to maintain drilling campaign diversity, the field measurements are selected for a range of environments, water depths (110 to 1,900 m), soil characteristics, riser and wellhead configurations, and vessel types. The study commences with field data QA and filtration. Measurements are classified into wave-dominated events, VIV events and combined wave and VIV events. The finite element models of the as-built riser and wellhead systems are generated using industry standard analysis parameters, and simulations are conducted using measured motions near the top of the riser. The resulting numerical responses for the wellhead are compared with the measured motions to determine the level of conservatism (or otherwise) in the wave fatigue analysis. Additionally, SHEAR7 models driven by measured current profiles are used to compare predicted VIV fatigue response to that based on field measurements. Analysis results indicate that industry standard approach for wave and VIV fatigue assessment is indeed conservative. However, it should be noted that wellhead fatigue predictions through numerical simulations are affected by various analysis parameters, and it is impossible to determine the correct values for each of these parameters by using field measurements alone. In literature, several previous studies compared measured and predicted wellhead response. However, they often focus on a single drilling campaign, which makes it difficult to apply their findings to another drilling campaign. This JIP is the first in the industry to provide a combined assessment of full-scale field data from multiple drilling campaigns. Using consistent analysis techniques for all datasets offers valuable insight into riser and wellhead response characterization and safe drilling operations.
In-situ wellhead structural monitoring is used for one of the industry’s first deep water high-pressure high-temperature (HPHT) development projects in the Gulf of Mexico to confirm the integrity of the wellhead system during drilling operations. As part of the monitoring program, the field measurements are collected using acoustic and standalone monitoring systems. In this paper, wellhead fatigue accumulations determined based on different motion sensors, data acquisition systems, and data analysis methods are compared. For the HPHT well drilling period, the subsea stack motions (i.e., LMRP/BOP accelerations) are measured and transferred to the top side in power spectral density (PSD) format via acoustic data communication system. At the same time, the stack motions (accelerations and angular rates) are also measured using another set of standalone motion sensors. Acoustic data transmission provides near real-time feedback to drilling operations while standalone motion sensors store motion measurements locally for future data processing once the instrumentation is retrieved. Wellhead stress transfer functions, which correlate BOP/LMRP motions with stresses at the fatigue hot spots, are developed for both standalone and acoustic sensors using a finite element model of the as-built riser/wellhead system. Next, field data obtained from these different sources (acoustic and standalone sensors) along with fundamentally different analysis methods are used to calculate wellhead fatigue. Each monitoring method exhibits benefits and limitations depending on the characteristics of the measured wellhead response and monitoring objectives. It is shown that the fatigue results determined based on these monitoring methods can be different if the most appropriate monitoring configuration is not selected. In literature, there are several previous studies presenting wellhead fatigue calculations based on monitoring data. However, these studies discuss either acoustic or standalone sensor data analyses. This paper provides the combined assessment of acoustic and standalone monitoring approaches for the same monitoring period and compares fundamentally different data analyses methods for HPHT applications. The advantages and disadvantages of each approach are evaluated, and recommendations are provided for when each method should be considered.
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