bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lost Circulation Prediction in South China Sea using Machine Learning and Big Data Technology
Authors: Hou Xinxin , Yang Jin , Yin Qishuai , Liu Hexing , Chen Haodong , Zheng Jinlong , Wang Junxiang , Cao Bohan , Zhao Xin , Hao Mingxuan , Liu Xun 
Published: April 2020

Abstract
Lost circulation is one of the frequent challenges encountered in the well drilling and completion process, which can not only increase well construction time and operational cost but also pose great risk to the formation. However, choosing the most useful treatments may still be a problem due to the complexity of the drilling and geological condition.
In this paper, machine-learning algorithms and big data technology are employed to mine and analyze drilling data of wells in South China Sea where lost circulation is severe. Geological characteristics, drilling fluids property parameters and operational drilling parameters are both considered. Moreover, an artificial neural network is employed to conduct supervised learning. The four metrics: accuracy, precision, f1 score and recall are used to evaluate the model. The trained artificial neural network model is employed to predict the lost circulation risks.
To train and test the proposed model, drilling operation parameters, geological parameters and drilling property parameters are collected for lost circulation events for 50 drilled wells over past two years in South China Sea. The trained model is excellent with the most important evaluation metrics, attaining an accuracy up to 92%, with f1 score, recall and precision up to 89% similarly. This suggests that the model have a good generalization ability and can be applied to other fields. Data analysis through an artificial neural network is carried out to develop a lost circulation prediction system model. This methodology can predict six lost circulation risks, each is defined according to drilling mud loss rate.
This is one of the first attempts to predict lost circulation using data-analytics and artificial intelligence. The proposed intelligent lost circulation prediction method can assist the drilling engineer to choose the optimal drilling parameters prior to drilling and avoid lost circulation events.
Quantum Dots: Low-Dosage Hydrate Inhibitors for Deep Water Flow Assurance
Authors: Deka Barasha , Sharma Rohit , Mahto Vikas , Polara Subhash 
Published: April 2020

Abstract
Flow assurance problem of gas hydrate formation in subsea transportation system of crude oil is addressed in this research work. The paper depicts the synthesis and evaluation of propylene glycol quantum dots as low-dosage hydrate inhibitor (LDHI) for preventing / delaying gas hydrate formation during transportation of crude oils in subsea pipelines. The thermodynamic hydrate inhibitor, propylene glycol, has been developed as anti-agglomerant, which is a type of low-dosage hydrate inhibitor, by converting it into its quantum dots. The developed quantum dots (QDs), which are a few nanometer sized particles are a new addition to oilfield chemicals. They are highly effective in increasing induction time of gas hydrate formation and hydrate agglomeration time for multiphase flow of crude oil with water cut up to 90%. Hydrate formation induction and agglomeration time study are performed on a fabricated setup based on visual method of hydrate study on crude oil –water system including tetrahydrofuran, forming a model hydrate system. The developed quantum dots are characterized to obtain their optical and other properties. The research work showed the effectiveness of propylene glycol QDs as LDHI, which may be applicable in deep water production systems to avoid flow assurance problem in transportation pipelines.
Modeling Current-Energy-Converter Devices in the Tanana River, Alaska
Authors: James Scott C. , Olson Sterling S. , McWilliams Sam , Jones Craig A. , Roberts Jesse D. 
Published: April 2020

Abstract
The effect a marine hydrokinetic device, or array of devices, has on the environment is a key component of design, permitting, and viability of a project. To accurately understand physical processes and their potential relationship to environmental stressors at a current-energy converter (CEC) site, a numerical model was developed using SNL-Delft3D-CEC-FM to facilitate an understanding of the potential changes to the system .
Conceptual flowchart of the modeling methodology.
Using turbine and river data provided by the Alaska Hydrokinetic Energy Research Center, a steady-state flow model was constructed for varying river discharge levels. The wakes of University of Alaska at Fairbanks New Energy Systems vertical-axis, 5-kW turbine as well as commensurate changes to the steady-state flow field were simulated. Finally, an example optimization study was undertaken for turbines arranged in various arrays to demonstrate the effects of lateral and downstream interference, wake recovery, and overall momentum removal on the flow conditions and power generation.
Considering a distribution of flow conditions over a 10-year period for the Tanana River at Nenana, variations in number of CEC devices and array layout provided insights into power production and environmental effects. Specific quantities of interest included changes in velocity and bed shear stress, which each showed changes in proportion to the number of devices in the array. Using SNL-Delft3D- CEC-FM with calibrated turbulence constants allowed for a design that maximized CEC array power while remaining within constraints of minimally altered environmental conditions. That is, array layouts could be selected to optimize power generation while minimizing flow-field changes.
This work highlights the importance of investigating array performance at each site under consideration. These findings are helpful in optimizing turbine arrangements in the Tanana River at Nenana that maximize power production and minimizes undesirable/unintended changes to the river's natural flow (flow depth, velocity, and bottom shear).
High-Voltage Subsea Connectors: Enabling Innovative Subsea Power Distribution Solutions
Authors: Plant Chris 
Published: April 2020

This paper focuses on the development of the world's first high-voltage (HV) 45kV, wet-mate connector system1 qualified in accordance with the Subsea Electrical Power Standardization (SEPS) SP-1001 Penetrators and Connectors cross-operator standard. The connector is rated for water depths up to 3,000 meters and is a key enabler of numerous offshore applications, including subsea electrical power grids; independently retrievable, high-power step-down transformers for long step-out subsea gas compression; cost-effective installation of deepwater pipelines with direct electrical heating (DEH); and simplified repair of deepwater high-voltage umbilical power cores (among others). The high voltage and current rating of the connector provides the opportunity for commonality between the primary and secondary of a subsea transformer. This reduces the cost of mating tools, protective caps and test equipment, thus improving the economics of utilizing wet-mate connectors, which can often be cost-prohibitive.
The paper discusses the growing need for a clear industry standard that covers the development and qualification of HV wet-mate electrical connectors. It also explores the inherent challenges of subsea electrical connectors, including long-term reliability, subsea module integration activities, and long-term effects of sea-water immersion on insulating materials. These were identified through a policy of continuous stakeholder engagement and a thorough review of 40 years of low- and medium-voltage connector design.
To illustrate the benefits that HV wet-mate connectors can provide, multiple use cases are discussed, including long step-out subsea compression; deepwater pipeline DEH; and HV umbilical repair.
Certification Pathway for 3D Printed Parts - Unlocking the Barriers to Accelerate the Adoption of Additive Manufacturing in Offshore Industry
Authors: Kandukuri Sastry Yagnanna , Le Gallo Brice 
Published: April 2020

Abstract
Offshore industry assets are capital intensive and downtime can have severe financial consequences. Additive manufacturing (AM) based supply chains can potentially offer offshore industry stakeholders a strong value and a competitive advantage, from lower costs and lead times to greater flexibility and agility. However, the current adoption level of AM for the offshore industry is very limited, despite the consensus that such technology could have potential applications for spare parts, repair and even new builds. While adoption of additive manufacturing could be a source of positive change, inadequate understanding of requirements regarding approval, qualification and certification processes required by regulatory authorities could hinder the progress of AM adoption in the offshore industry.
Currently, there are only a handful of additive manufacturing standards available for early adopters of AM technology. Hence, costly and time-consuming nonstandard testing to ensure the integrity of the 3D printed parts is deterring the wider applications of additive manufacturing in the offshore sector, underscoring the need to develop optimal practice guidelines and standards from design to part build to operation.
This paper aims to highlight several key challenges that hinder the adoption of AM in the offshore sector and to propose various solutions that can help to overcome these. Due to its novel approach, the risk-based certification pathway discussed in this paper will help the offshore industry and its supply chain ecosystem to build trust and confidence in the adoption of this emerging technology, which otherwise might not be possible.
The Assessment of Microbial Surfactants as Asphaltenes Dispersants: Sophorolipids vs Dodecylbenzenesulfonic Acid
Authors: Aguiar Janaina I. S. , Rogers Jonathan , Mahmoudkhani Amir 
Published: April 2020

Abstract
Asphaltenes are a major problem for oil industry. Their variability in size, structure and polarity presents a challenge to develop formulations that are effective against precipitation and deposition for all types of asphaltenes. Also, changes in production systems, such as the rate of water break through, can also impact the efficiency of dispersants. In this paper sophorolipids, a class of microbial surfactants, that are renewable and biodegradable with low aquatic toxicity, are tested as asphaltene dispersants. In order to achieve the objectives of this study, asphaltenes were extracted from a heavy oil and their solubility and stability were evaluated at different concentrations of n-heptane and water. The behavior under n-heptane concentration sweep was found to be in line with previous experimental works and water did not change that behavior. The sophorolipids were able to inhibit ~100 % of the polar asphaltenes precipitation at low concentrations. Under similar conditions dodecylobenzesulfonic acid, which was used as a benchmark for comparison, did not perform at concentrations up to 1000 ppm either with or without water. The high performance of biosurfactants is attributed to their enhanced dispersing properties for organics such as wax and asphaltenes.
Comparison of Seismic Responses of a Fixed Offshore Structure for Ground Motion Records Considered Near Fault Effect and Fling Effect Generated Using Three Methods
Authors: Duman Servan , Vasala Haritha , Sari Ali 
Published: April 2020

Offshore platforms, fixed or floaters, are a well-established design concept for oil explorations. It is fundamental to design these structures such that they can resist moderate to severe earthquakes without causing any severe damage to the occupants and to maintain the integrity of the structure. Hence, it is important to examine the behavior of these offshore structures for all kinds of earthquake scenarios to determine the risk levels that may occur in the structure. This paper intents to compare the seismic responses for a fixed offshore structure by employing ground motion records generated using three different methods.
The design spectrum determines the seismic hazard of a region. Therefore, it is necessary to use acceleration records compatible with the design spectrum in seismic analyses. In this paper, Scaling, Spectral matching and Stochastic methods were used for comparison. Peerscaled tool was used for scaling in the time domain. RspMatch was used for the Spectral Matching. New acceleration records were generated by QuakeSim. A series of non-linear time histories using acceleration records of scaled ground motions were carried out to compare the damage levels and responses of a fixed offshore structure.
Spectral matching method was observed to cause higher displacements than scaling method. The occurrence of inelastic events, such as buckling was observed to be high when the acceleration records considered the near-fault effect. And, permanent deformation was observed when acceleration records considered the fling effect. Ground motion records generated by different methods if adopted while performing seismic assessments of offshore structures will reduce the risk level for the structure.
Physics Based Machine Learning for Annular Blowout Preventer Health Monitoring
Authors: Mallek Moadh , Wassar Taoufik , Franchek Matthew A. 
Published: April 2020

Abstract
Physics-based models are efficient solutions capable of predicting the dynamic performance of engineering systems. Owing to the rapid data growth in sensor technologies, enormous datasets are readily available creating a unique opportunity to seamlessly fuse analytical physics based models with system data. This integration has produced a physics-based machine learning (PBML) knowledge base that overcomes the costly limitations of deep learning solutions and associated false discoveries of machine learning (ML) methods.
Presented in this paper is a University of Houston invented PBML approach to monitor the condition and performance applied to an annular preventer. A PBML model that describes the relationship between annular closing pressure and its cylinder displacement is derived. The model coefficients are adapted via system identification methods using real time collected data. The resulting coefficients are clustered to classify annular health from early age to middle age to aged cycles using the K-means method. Life cycle test of multiple annular preventers is performed using a full-scale testing facility. The results show that annular elastomer degradation can be detected and quantified using the proposed real-time adaptive physics based model solution given sensing information.
Pilot Study of Integration of Wildlife Impact Analysis into Spatial Environmental Assessment Tool for Marine Hydrokinetic Energy
Authors: Coates Shannon , Lockhart Gwen , Courbis Sarah , Raghukumar Kaustubha , McWilliams Samuel , Jones Craig 
Published: April 2020

Abstract
This study aimed to produce a pilot spatial model, building upon the existing quantitative Spatial Environmental Assessment Tool (SEAT), to evaluate risks associated with marine hydrokinetic energy (MHK) development within the Pacific Marine Energy Center South Energy Test Site, located off Newport, Oregon. The goal is to establish an approach for integrating natural resource sensitivities with changes in physical conditions caused by wave energy converter arrays to inform planning and impact assessment. To generate the model inputs, a benthic substrate proxy within the project area was defined as a "sensitive resource" input (Receptor), using the Risk (Rτ) output points from the SEAT model to identify areas that could be impacted by the operation of a MHK device (Threshold). Risks were further investigated by overlaying federally designated Habitat of Potential Concern, Essential Fish Habitat, and critical habitat to identify sensitive benthic resources (Secondary Constraint) within the project area. The last iteration of the model focused on using nearshore habitats for threatened coho salmon (Oncorhynchus kisutch) outmigrating from rivers to demonstrate how a higher taxon could be incorporated into the pilot model by assigning risk based on proximity to the shoreline. This approach allows the analyst to weight the sensitivity of natural resources and place secondary constraints and ocean conditions into the assessment for an integrated picture and can accommodate multiple taxa, seasons, habitats, and locations. Overall, the results of the four models were dependent on the weighting and scale of the data. The outcomes of this study are meant to demonstrate ability to integrate models and are not based on particularly defensible criteria. More rigorous literature review and risk assessment process would be necessary to demonstrate the project specific risk of impacts from MHK devices. This type of modeling can inform resource management and planning, and ultimately assist in identifying priority impact issues and mitigation needs throughout project siting and development, which will reduce risk and cost to developers.
Legged Robots for Autonomous Inspection and Monitoring of Offshore Assets
Authors: Ramezani Milad , Brandao Martim , Casseau Benoit , Havoutis Ioannis , Fallon Maurice 
Published: April 2020

Abstract
Inspection and monitoring of assets are repetitive and expensive tasks and have higher risk when facilities are located offshore. Robotics holds the promise of improving the efficiency and safety of such platforms by allowing inspection and continuous remote monitoring of difficult-to-access facilities.
Legged robots, such as quadrupedal robots, are promising machines to achieve this goal: they have high maneuverability both indoors and outdoors, they are designed for accessing and navigating facilities that are built for humans (e.g. stairs, step-over piping, narrow passageways) and can carry a variety of sensors targeted at inspection and monitoring tasks.
In this paper we introduce our approach for autonomous inspection of oil & gas platforms using legged robots. Our approach is being developed as part of the ORCA Hub (Offshore Robotics for Certification of Assets), a UK robotics research hub. We envision a highly autonomous robotic system that conducts inspections with minimal intervention by human operators. The robot can navigate through facilities, accomplishing crucial tasks such as 3D mapping, monitoring of thermal build-up using thermal cameras, pressure sensing and also using color cameras to detect people and to carry out general visual inspection. We demonstrate and evaluate the system's perception, locomotion and inspection capabilities on a training facility that realistically simulates an oil rig at the Fire Service College, Moreton-in-Marsh, UK and an industrial area in the Offshore Renewable Energy Catapult Facility, Blyth, UK. We show the result of both autonomous and real-time teleoperated missions, and analyze the accuracy and efficiency of the system.
Fit for Purpose LWD Shear Sonic Logging in Surface Hole Sections: Ensuring Quality Data for Drilling Risk Mitigation
Authors: Blyth Matthew Peter , Sakiyama Naoki , Yamamoto Hiroaki , Fahim Ud Din Syed Muhammad 
Published: April 2020

Abstract
The largest risks in deepwater well construction often occur in the shallow, surface hole sections where seismic uncertainty is high. This seismic uncertainty translates into increased error when estimating the position and extent of shallow hazards such as gas charged sediments, high pressure zones, wellbore stability concerns, faults, etc. Should these hazards be encountered unexpectedly, or without adequate preparation, then the associated operational costs and risks to safety can be extremely high. It is therefore critical that these risks are managed appropriately to ensure drilling success. Minimizing the uncertainty in the surface seismic requires adequate knowledge of both the compressional and shear velocities of these shallow sediments. Being able to turn this information into real-time drilling solutions to mitigate risk requires the acquisition of these measurements using logging while drilling (LWD) sonic tools in a wide variety formation types, often in large, and potentially washed out, surface holes and in a wide variety of mud types. This paper will discuss the physics of acquiring quadrupole shear measurements in large boreholes and slow formations. The limits of measurement will be explored, using both modeled data and real well examples, in a variety of formations, with different mud types, and in different borehole sizes. The effect on measurement quality as a result of the interplay among these three key parameters is revealed. The results show that, although the acquisition of reliable shear data in these conditions is challenging, it is not impossible, provided that the LWD tool is correctly designed, the physics are understood, and suitable processing applied. The latest generation of LWD sonic tools can therefore offer unique solutions to managing drilling risk.
As LWD sonic tools have become more advanced, they have become more routine in use and are now commonly used in multiple hole sections from surface to total depth. Utilizing the compressional and shear measurements from these tools as inputs into analysis to address concerns such as wellbore stability or seismic remigration requires a thorough understanding of the physics of the measurement, the effects of the borehole conditions and the limits of their capability. The challenges inherent in acquiring compressional velocity in large holes and slow formations are well documented, but the effect of these formations on measurements of LWD quadrupole shear has been much less discussed. The newly conducted intensive modeling study and field data analysis of the LWD quadrupole modes reveal that the effect of the drilling mud properties (slowness and density) is also important as well as the formation properties and the borehole size for deriving robust shear slowness.