bluuebunny/crossref_metadata_2025_split huggingface dataset:
Out-of-Plane Bending (OPB) Test of Large Diameter Mooring Chains: Test Results
Authors: Karimi M. R. , Barros P. , Carlberg E. , Vargas P. 
Published: October 2021

Abstract
Chevron Corporation and Bluewater Energy Services performed a chain out-of-plane bending (OPB) test campaign, called OPB MAX hereafter, at DNV’s laboratory in Høvik-Norway. The test was performed to study the OPB phenomenon for a chain diameter which was larger than the maximum diameter tested by the OPB JIP [12]. The goal was to understand chain OPB physics for such a large diameter, measure interlink stiffness and maximum sliding moments and validate Bluewater’s in-house finite element model. The current study is a collaboration between all involved parties and the results are presented in three papers. The first paper, Barros et al. [1], summarized the test setup, initial observations and the assumptions used in the post-processing. The current paper describes some of the test results, compares them with the OPB JIP estimations and describes the observed chain OPB physics. The third and the last paper will present the FEA results done by Bluewater’s in-house finite element model.
Two seven-link chain specimens of R4 and R4S grades, both with the nominal diameter of 168 mm were tested. Five tension levels of 150, 200, 250, 300 and 350 t were used throughout the tests. Chain sliding was performed in both wet and dry conditions. Twenty strain gauges were attached to five links of each specimen except for the two end-links to measure three OPB and two IPB moments at mid-link. Twelve strain gauge rosettes were used on three links to evaluate SCF on the OPB hotspots. Seven inclinometers were used to monitor link rotations. DNV’s ARAMIS image processing tool was utilized to capture chain movements. A handheld temperature sensor gun monitored the interlink area’s temperature.
Interlink stiffness was measured at both ends of each specimen and four intermediate links. Several sensitivity studies were conducted to investigate the effect of loading speed, initial interlink angle and acquisition frequency. The interlink stiffness values that were initially found based on tests at small interlink angles (±0.2 °) were quite consistent and repeatable. Further tests that were performed at large interlink angles (±2.5 °) showed that interlink moment vs. angle hysteresis changes over time and is not unique. This was attributed to deformations observed at the interlink areas that had happened during the tests. The mentioned deformations directly influenced the hysteresis and the associated interlink stiffness values. The nature of deformations and stiffness variations was different during dry and wet tests at large angles. Furthermore, the interlink stiffness values measured on the R4S specimen were quite close to R4 results.
A Statistical ARIMA Model to Predict Arctic Environment for NSR Shipping
Authors: Wu Da , Lang Xiao , Zhang Di , Eriksson Leif , Mao Wengang 
Published: October 2021

Abstract
Reliable sea ice concentration (SIC) information assists the safe and energy-efficient ship navigation along the Northern Sea Route (NSR). In particular, the accurate SIC forecast is a top priority. This study proposes a statistical interpolation method to reduce the errors induced by the traditional interpolation method. An auto-regressive integrated moving average (AR/MA) model is developed based on reanalysis data. The AR/MA model can be used for short-term SIC forecasts along the NSR. Model validation has been conducted through a specially designed cross-validation. The route availability is estimated according to the SIC forecast. The results indicate that the specified NSR will be open for shipping from 2021 to 2024. The work also indicates the feasibility of the proposed statistical models to assist NSR shipping management.
Safety Assessment of Marine Structures Subjected to Underwater Explosion
Authors: Prasad D. H. S. , Rao S. K. , Patel B. P. , Ahmad Suhail 
Published: October 2021

Abstract
The underwater explosions due to an accident or other potential reasons cause severe shocks in the fluid medium. Safety assessment is an essential design requirement where the blast load concentration is expected due to smaller standoff distances. In the present work, the probabilistic analysis of structural response due to the underwater explosion is studied using NESSUS software. The study helps improve the concepts of improved blast mitigation strategies. The shock loading considered varies spatially and temporally. The fluid pressure is transformed into structural nodal forces. Taylor’s plate theory is used to estimate the shock loading. In the analysis, the material is considered to be rate-dependent elastoplastic with temperature softening. Maximum central deflection against shock factors is obtained to arrive at the safe thickness of the stiffened and unstiffened plates for the optimum design characteristics using LS-DYNA software. The finite element approach adopted efficiently captures expected failures for various combinations of structural properties leading to an optimized design to withstand the target explosion. Johnson-Cook dynamic failure model is employed as a limit state function for reliability assessment. Sensitivity analysis enables to achieve target reliability.
Development of a Simplified Three Degrees of Freedom Model to Represent the Installation of a Subsea Manifold
Authors: Salvador Lopes Filipe , Sena Sales Junior Joel , Martins de Andrade Emerson , Fernandes Antonio Carlos 
Published: October 2021

Abstract
Due to risks involved during the installation of subsea equipment, it is necessary to simulate the installation process to determine a safe operating window. However, most of the software capable of running these kinds of simulations are very expensive, and these simulations usually take a long time to be made. It is then very convenient to develop a simplified model, capable of running these analyses in a short period of time while still providing us with reliable results.
This model was developed using the Python programming language, where a fourth-order Runge Kutta method was implemented to solve the equation of motions that governs the manifold’s installation process. The assumptions are that the wave forces are applied to the ship executing the manifold installation. The ship’s motions were applied at the top of the cable, connected to the crane, so the manifold motions underwater and the cable tension could be calculated.
Previously, a simplified one-degree of freedom (1DoF) model was developed and compared to other numerical models and experimental data. In this present work, the model was then expanded to motions in a vertical plane, that is, three degrees of freedom (3 DoF), in order to better represent the physics of the real problem. Its results were then compared to the ones obtained by the 1 DoF model and to the experimental results. The 3 DoF model resulted in a dynamic response closer to the ones observed in the experiments, which shows that it is a better representation of the problem.
The Effects of Gas Mixture Composition on the Mass Transfer and Individual Diffusion Coefficient in CH4-CO2-Light Oil Systems
Authors: Zheng Taiyi , Luo Yongcheng , Shi Yu , Liu Xiangui , Yang Zhengming , Luo Yutian , Zhang Yapu 
Published: October 2021

Abstract
Limited CO2 resources considerably narrow down the field application of CO2 EOR for improving oil recovery in tight formation. Considering that CH4 and CO2 have similar EOR mechanisms, CH4, as a by-product of produced oil, is a relatively cost-efficient agent to be injected into the tight formation with CO2. In this work, experimental and mathematical methods are proposed to probe the effect of CH4 composition on the mass transfer between a CO2-CH4 gas mixture and crude oil collected from a tight oil reservoir. Experimentally, the pressure-decay tests for different CH4-CO2-light oil systems are conducted at a constant temperature in a pressure / volume / temperature (PVT) setup. Also, the gas mixtures’ compositions before and after the experiments are analyzed with gas chromatography to investigate the mass transfer of different components. Theoretically, mathematical formulations are developed to describe the mass transfer between the gas mixture and light oil based on translated Peng Robinson equation of state (PR-EOS), a real gas equation, and one-dimensional convection-diffusion equations. The individual diffusion coefficients of CH4 and CO2 as well as the concentrations distribution can be obtained by minimizing the deviation between the calculated pressure and the measured ones. The results indicate that the higher the content of CO2 in the initial gas phase, the faster the pressure drops are and less time it takes for the oil and gas phases to reach a stable pressure, which implies a high mass transfer rate with an increase in CO2 composition. In particular, the diffusion coefficient of CO2 is found to be about 2 times larger than that of CH4 the same composition condition. However, it is noted that the individual diffusion coefficients of CH4 or CO2 are not constants. A high molar fraction in the initial gas sample will lead to a large diffusion coefficient in different CH4-CO2-light oil systems.
Optimization of Frequency Domain Fatigue Analysis for Unbonded Flexible Risers
Authors: Yuan Jiabei , Hou Yucheng , Tan Zhimin 
Published: October 2021

Abstract
Fatigue analysis of flexible risers is a demanding task in terms of time and computational resources. The traditional time domain approach may take weeks of time in global simulation, local modelling and post-processing of riser responses to get fatigue results. Baker Hughes developed a fast hybrid approach, which is based on a frequency domain technique. The new approach was first implemented at the end fitting region and then to all other regions of the riser. Studies showed that the hybrid approach achieved convenient and conservative results in a significant shorter period of time.
To improve the accuracy and reduce conservatism of the method, Baker Hughes has further optimized the analysis procedure to seek better results approaching true solutions. Several methods were proposed and studied. The duration of representative cases and noncritical cases have been extended. The steps to predict stress spectrum based on transfer functions have also been updated. From previous studies, only one transfer function was built for fatigue load cases with similar response spectra. This assumption linearizes the system response and produces certain level of discrepancy against true time domain solution. In this study, multiple ways of spectrum prediction are evaluated and compared.
The paper summarizes several techniques to further optimize the hybrid frequency domain approach. The updated fatigue results are found to be more accurate. The optimized approach therefore gives more flexibility to engineers to approach the true solutions, which were originally acquired from full 3-hr time domain simulations. The approach requires less analysis time and reduces iterations in pipe structure and riser configuration design, which leads to faster project execution and potential cost reduction.
Development of Digital Twins for Drilling Fluids: Local Velocities for Hole Cleaning and Rheology Monitoring
Authors: Gharib Shirangi Mehrdad , Aragall Roger , Ettehadi Reza , May Roland , Furlong Edward , Thompson Charles A. , Dahl Thomas G. 
Published: October 2021

Abstract
In this work, we present our advances to develop and apply digital twins for drilling fluids and associated wellbore phenomena during drilling operations. A drilling fluid digital twin is a series of interconnected models that incorporate the learning from the past historical data in a wide range of operational settings to determine the fluids properties in realtime operations. From several drilling fluid functionalities and operational parameters, we describe advancements to improve hole cleaning predictions and high-pressure high-temperature (HPHT) rheological properties monitoring. In the hole cleaning application, we consider the Clark and Bickham (1994) approach which requires the prediction of the local fluid velocity above the cuttings bed as a function of operating conditions. We develop accurate computational fluid dynamics (CFD) models to capture the effects of rotation, eccentricity and bed height on local fluid velocities above cuttings bed. We then run 55,000 CFD simulations for a wide range of operational settings to generate training data for machine learning. For rheology monitoring, thousands of lab experiment records are collected as training data for machine learning. In this case, the HPHT rheological properties are determined based on rheological measurement in the American Petroleum Institute (API) condition together with the fluid type and composition data. We compare the results of application of several machine learning algorithms to represent CFD simulations (for hole cleaning application) and lab experiments (for monitoring HPHT rheological properties). Rotating cross-validation method is applied to ensure accurate and robust results. In both cases, models from the Gradient Boosting and the Artificial Neural Network algorithms provided the highest accuracy (about 0.95 in terms of R-squared) for test datasets. With developments presented in this paper, the hole cleaning calculations can be performed more accurately in real-time, and the HPHT rheological properties of drilling fluids can be estimated at the rigsite before performing the lab experiments. These contributions advance digital transformation of drilling operations.
Structural Health Monitoring Data Analysis for Ageing Fixed Offshore Wind Turbine Structures
Authors: Yeter Baran , Garbatov Yordan , Guedes Soares Carlos 
Published: October 2021

Abstract
The objective of the present study is to perform a systematic data analysis of structural health monitoring data for ageing fixed offshore wind turbine support structures. The life-cycle extension of the first offshore wind farms is under serious consideration since the support structures are still in a condition to be used further. Big data analytics and machine learning techniques can aid to extract useful information from the monitoring data collected during the service life and build models for future predictions of an optimal life-extension. To this end, it is aimed to analyse the big data provided by embedded control systems and non-destructive inspections of ageing offshore wind turbine support structures using pre-processing techniques, including denoising, detrending, and filtering to remove the noise of different nature and seasonality as well as to detect the signal-specific contents affecting the structural integrity in the time and frequency domain. The effectiveness of the Welch method is investigated in terms of dealing with noisy signals in the frequency domain. Besides, the principal component analysis is carried out to reduce the dimensionality of the data and to select the most significant features that are responsible for most of the variance in the structural health monitoring data. Moreover, nonparametric statistical methods are used to test whether the data before noise being added and the data after cleansing the added noise came from the population with the same distribution. Further, permutation (randomisation) testing is performed to predicate that the results of the nonparametric test are statistically significant. The outcome of this study provides refined evidence that enables to feed the condition monitoring data into the training of the deep neural network to be able to discriminate different structural conditions.
Modelling and Analysis of Hydrodynamics of a Submerged Structure in Extreme Waves Using a SPH-Based Tool
Authors: Islam Mohammed , Seo Dong Cheol , Raman-Nair Wayne 
Published: October 2021

Abstract
The applications of a Smoothed Particle Hydrodynamics (SPH)-based, a Finite Volume Method (FVM)-based and a Boundary Element Method (BEM)-based tools to investigate the nonlinear interactions between large waves and a submerged horizontal circular structure and to some extent a rectangular cylinder at various submergence depths in deep water conditions are presented. The main aim is to validate the Lagrangian technique based SPH tool to predict the wave-structure interaction forces under large waves.
The features of typical force curves in a wave cycle, the magnitude of wave forces, and the influence of relative axis depth of the structure in deep water conditions are investigated, primarily using an open-sourced SPH tool. Simulations were carried out in 2D with one deepwater wave at multiple submergence depths. The water surface elevations are predicted at different near- and far-field locations. The time-averaged mean and the average amplitude of the horizontal and vertical forces acting on the cylindrical model at various submergence depths are plotted and then physically interpreted. The wave forces and surface elevations are compared with the available published experimental studies and CFD (both FVM and BEM) predictions. Good agreement between the SPH predictions and the measurements was obtained for the submerged body’s surface elevation and hydrodynamic forces at all submergence depths. The FVM tends to overestimate the wave forces compared to the SPH predictions and the measurements, particularly for the shallowly submerged structure when extreme wave breaking occurs. The BEM predictions are reasonable for the non-wave breaking cases.
Machine Learning Applications in Drilling Fluid Engineering: a Review
Authors: Gul Sercan 
Published: October 2021

Abstract
Drilling fluid (mud) serves various purposes in drilling operations, the most important being the primary well control barrier to prevent kicks and blowouts. Other duties include, but not limited to, maintaining wellbore stability, removing and transporting formation cuttings to the surface, cooling and lubricating downhole tools, and transmitting hydraulic energy to the drill bit. Mud quality is therefore related to most of the problems in drilling operations either directly or indirectly.
The physics-based models used in the industry with drilling fluid information (i.e., cuttings transport, well hydraulics, event detection) are computationally expensive, difficult to integrate for real-time analysis, and not always applicable for all drilling conditions. For this reason, researchers have shown extensive interest in machine learning (ML) approaches to alleviate their fluid-related problems.
In this study, a comprehensive review of the abundant literature on the various applications of ML in oil and gas operations, concentrating mainly on drilling fluids, is presented. It was shown that leveraging state-of-the-art supervised and unsupervised ML methods can help predict or eliminate most fluid-related issues in drilling. The review discusses various ML methods, their theory, applications, limitations, and achievements.
Well Testing of Fracture Corridors in Naturally - Fractured Reservoirs (NFR)
Authors: Guo Yingying , Wojtanowicz Andrew 
Published: October 2021

Abstract
Geological folding/faulting may create naturally fractured reservoirs containing a semi-parallel system of sparsely-spaced fracture corridors. The pressure behavior of wells completed either in highly conductive corridors (fracture wells) or in the exclusion zone (matrix wells) would be quite different. In this study, a unique simulation model has been built for corridor type naturally fractured reservoirs by combining a local model of fracture well or matrix well with adjacent fracture corridor(s) and a “homogenized” global model of the remaining corridor network. The global model generalizes the corridor network using the single-porosity and radial permeability approach, which is verified as being sufficiently accurate. Pattern recognition technique is used to analyze diagnostic plots of pressure drawdown generated by simulated flow tests with commercial software (CMG). This study aims to build a new simulation model for corridor-type NFRs and apply the well testing technique to differentiate corridor-type NFRs from conventional NFRs, detect the well’s location, and estimate reservoir properties. This study also employs cumulative logit statistics to assess the accuracy of the estimated well-to-corridor distance.
Free Surface Effect Assessment of the Flow Around the DARPA SUBOFF-5470 Submarine Using OpenFOAM
Authors: Quintuña María T. , Paredes Rubén J. 
Published: October 2021

Abstract
An important parameter to submarine navigation and control is determining the hull speed relative to the surrounding sea. The object of this paper is to find possible locations for speed sensors on a submarine. The probes should be fitted to the hull areas where the water flow is free of turbulence structures, vortices, or bubble formation to obtain a reliable measurement. In this work, a rational procedure is proposed to identify the probe installation site on the hull of the DARPA SUBOFF-5470 submarine, through numerical simulations using OpenFOAM. Three different depth conditions at three different navigation speeds were considered to assess the free-surface effect. First, verification and validation procedures were completed at deep water conditions (H/D = 5.4). The results of this analysis indicate a convergence ratio of 0.49 with an uncertainty of 0.04%SC. Later, a grid convergence analysis was completed at periscope depth conditions (H/D = 1.1), within the highly nonlinear Froude range. These results show an oscillatory convergence with an uncertainty of 0.78%. Finally, the hull region between 5 and 15% from the bow of the submarine length is recommended for installing the speed probe, considering the linearity of the flow, without high gradients and vortex structures.
Buoyant Miscible Flows in Axially Rotating Pipes: the Effects of Pipe Rotation and Viscosity
Authors: Lyu Shan , Taghavi Seyed Mohammad 
Published: October 2021

Abstract
The primary cementing operations of oil and gas wells involve pumping a sequence of fluids into the well (initially within a circular casing and eventually within an annular region) to displace in-situ drilling mud. The fluids involved can be miscible, and they can also have different density and viscosity ratios. It is believed that a casing rotation can generally improve the displacement process, within both the circular casing and the annulus. However, there have not been a lot of laboratory studies to prove that such rotation is indeed effective for the displacement within the casing. In fact, due to the lack of knowledge, the casing axial rotation may not be still among the top recommendations to enhance the displacement occurring within the casing. This is in spite of the fact such a rotation would be feasible using various types of casing heads and special adaptors. In this work, we conduct simulations to understand the fluid mechanics behind buoyant displacement flows that occur within the casing (pipe). Our focus is to analyze the effects of the axial rotation speed of the pipe, the viscosity of the fluids and the viscosity ratio between the two fluids on the flow behaviours. Other flow parameters are also present: the fluids are miscible, and they have a density difference; the pipe inclination angle is considered to be near-horizontal (i.e. the most challenging case in terms of creating efficient displacements). We investigate important flow features, such as the behaviours of the interface between the fluids, the mixing between the fluids, the fluid front velocities, etc. Our results help develop a deep understanding of how casing rotation can be used to enhance displacement flows in the primary cementing operations of oil and gas wells.