bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning Applications to Improve Pore Pressure Prediction in Hazardous Drilling Environments
Authors: Bui Huy , De Nicolias Nelly , Nye Rebecca , Estrada John 
Published: April 2022

Abstract
Pore pressure analysis can be imperative while drilling, especially in certain offshore environments where abnormal pore pressures can cause serious problems such as fluid influx, kicks, and blowouts. In order to avoid such events, the prediction in real-time, or preferably ahead of time, is required. These events can have a catastrophic impact on operations and the safety of the entire platform. Real-time wellbore logs can be used to assist in the prediction of pore pressure, however, due to the high cost of downhole data acquisition and risks associated with a tool getting stuck or lost in hole, comprehensive well logs are not always available.
In the absence of a measured sonic log, a predicted acoustic log can be used for input into the pore pressure prediction avoiding the risk and cost of downhole wellbore logs, however, accuracy is extremely important. A Gradient Boosting model is trained and validated on 3 different sets of features to predict acoustic wellbore logs, followed by a physics-based model for pore pressure prediction. The physics-based model is built using Eaton, density extrapolation, and other optimization methods to ensure speed and accuracy. Lastly, the model takes the predicted acoustic data to derive the pore pressure gradient, calibrated by other drilling parameters.
The Gradient Boosting model reduces any impact from the lack of data availability, significantly reduces the root mean square error (RMSE) and increases the overall accuracy. The results are then calibrated to drilling events to ensure the predictions are within the range of actual recorded event data. The result from a recent case study shows that pore pressure prediction using predicted acoustic logs correlates closely with recorded drilling events. The client successfully estimated pore pressures using predicted acoustic logs, reducing the for the need to acquire costly logging data downhole.
The Gradient Boosting model provides a solution to predict acoustic logs and pore pressure that is highly accurate in real-time. The drilling event calibration method then helps to avoid physical factors that cannot be captured by the model, increasing the overall reliability of the workflow. The method allows for pore pressure analysis to be carried out accurately, regardless of the downhole logs acquired.
Factors Required for the Lifetime Protection of Structures and Equipment Requiring Passive Fire Protection in Harsh Offshore Environments
Authors: Wade Robin John 
Published: April 2022

Abstract

Objectives / Scope
Many factors impact the longevity of passive fire protection which is required to protect structural steel and process equipment from degradation in harsh offshore conditions and accidental fire scenarios. This paper reviews the impact installation, environmental and operational processes have on the lifetime expectations of passive fire protection and subsequently the assets on which the passive fire protection has been applied.

Methods, Procedures, Processes
A review of key processes is made mapping out the stress and strain impact (maximum and fatigue) expected to be imposed on thick film passive fire protection coatings. Industry recognized standards, testing protocols and industry reviews are discussed highlighting where the expected stress and strain impacts are accounted for and where there are deficiencies yet to be addressed. Examples from the real world show how epoxy passive fire protection has met the challenge over the past 40 years and where further scrutiny is required in design and installation.

Results, Observations, Conclusions
Current standards have been heavily focused on temperate climates around the world at the expense of extreme cold and hot climates and process conditions. Furthermore, installation and construction methods have been developed which may inadvertently place greater strain on passive fire protection systems prior to in-service use. As such performance expectations are not always realized from the project specifications. New proposals and discussions have reviewed what is required to capture performance expectations of modern-day assets and passive fire protection systems. Whilst asset owners see merit in many of the proposals there is a significant upscaling of costs required to realize these tests and validate solutions. As a result of a limited scope of current testing, there is a significant risk that passive fire protection is treated as a "commodity item" rather than a "specialised safety critical element" given that it is expected to function for many decades in a multitude of different service environments.

Novel/Additive Information
Revisions to industry standard tests are discussed along with the basis for performance expectations.
New proposals for allowing for wider environmental and process conditions to be considered.
The impact of treating passive fire protection as a commodity item as opposed to a specialized safety critical element is a key highlight
An Integrated Solution to Enable Development of 100 Km Deepwater Liquid Subsea Tiebacks
Authors: Jarrell Brian , D'Souza Richard B. 
Published: April 2022

Abstract
Subsea tiebacks of oil discoveries to existing host platforms are currently, and will continue to be, the most sanctioned deepwater projects because they are fast paced and profitable. Deepwater liquid subsea tieback distances today are technically constrained to about 35 km. Globally, there are numerous discoveries with recoverable reserves too small to justify a stand-alone development, between 35 km and 100 km of an existing host platform, that are effectively "stranded". This paper presents an integrated solution that reimagines the extended length tieback to enable the development of many of these prospects, using only field proven technologies.
The integrated solution consists of two major components: a) an Autonomous Subsea Tieback Enabling Platform (ASTEP), which is a Normally Unattended Installation (NUI) semisubmersible platform that is moored adjacent to the subsea wells and provides power, chemicals, and controls for the tieback, and b) an Active Thermally Managed (ATM) flowline system that ties back the subsea wells to the host platform. A description of the principal components (topsides, hull, mooring, risers, subsea systems, and ATM flowline) is provided along with details of the NUI platform, flow assurance management and low carbon emission power generation options.
The ASTEP Integrated Solution (ASTEP IS) enables commercial development of stranded liquid discoveries up to 100 km from a host platform, employing field proven technologies. The ASTEP IS:
Minimizes host platform brownfield modifications and associated production deferment, Increases reservoir ultimate recovery by incorporating proven Enhanced Oil Recovery (EOR) methods, Improves Environmental, Social, and Governance (ESG) and Health, Safety and Environment (HSE) development aspects, Is readily adaptable to reservoir and regional conditions, Can be repurposed for multiple deployments
The technical and commercial viability of the proposed integrated solution is demonstrated by a case study of a 30 km deepwater extended tieback to a host platform in the Gulf of Mexico (GoM).
Effect of Magnetic Nanoparticles on Strength Development and Microstructural Properties of Cement Slurry
Authors: Mello e Alvim Thiago Monteiro , Maranhão Fabíola Da Silveira , Batista João Humberto Guandalini , Reales Oscar Aurelio Mendoza , De Souza Júnior Fernando Gomes , Toledo Filho Romildo Dias 
Published: April 2022

Abstract
The typical solution in the cement industry to respond to the application challenges is the use chemical additives to adapt cement characteristics to the scenario. However, this approach may weaken cement integrity especially in long term. The use of physical properties to obtain similar performance may be a new frontier as cement can be kept without changes and the new additives are inert to the mixture.
A proof of concept study was carried out on using magnetic nanoparticles (MNP) and an oscillating magnetic field source to replace chemical additives in the objective of reducing Waiting on Cement (WOC) time during cementing operations in oil and gas wells. A rigorous process was executed to select the most promising particles and several experiments testing different cement slurries designs were executed to confirm the feasibility of this patented technology.
Magnetite nanoparticles were blended in 15.9 ppg class G Portland cement slurries in amounts of 0.25% and 0.50% by weight of cement (BWOC). The slurries were cured in atmospheric conditions up to seven days and characterized to determine the influence of the nanoparticles on early age compressive strength development and microstructural properties. Compressive strength development was tested both by destructive testing and by ultrasonic cement analyzer (UCA). Microstructural properties were determined by isothermal calorimetry, scanning electron microscopy, X-ray diffraction and thermogravimetric analysis.
Destructive testing of samples cured at atmospheric conditions up to 168 hours showed that the use of magnetic nanoparticles did not affect the compressive strength of cement. UCA experiments, carried out in temperatures similar to those obtainable by magnetically induced heating, confirmed the potential of the technology to accelerate strength development. Isothermal calorimetry results showed that the presence of MNP did not interfere with the normal hydration reaction of cement, fact that was confirmed by the thermogravimetric analyses, where the amount of hydration products was found to be similar for all samples. Scanning electron microscopy imaging showed MNP agglomerations in the pores of cement slurry, which might compromise their heating potential. X-ray diffraction showed the formation of ettringite in all samples. It was concluded that the presence of MNP in the cement slurry was not found to be detrimental to the mechanical and microstructural properties, which open a new frontier to the use of free-additive cement slurries, a cheaper solution built with components where the degradation along the life is well understood, in the opposition to the chemical additives, which degrade faster than cement by itself especially in long term such as well abandonment and carbon storage applications (CCS).
Successful Drilling Optimization in Carboniferous Formation Sets New Benchmark Performance Standards for the Subandean Area of Bolivia
Authors: Tocantins João Pedro , Vazquez Carrizo Cecilia , Creamer Julia , Mendoza Diego , Ortuno Giovanny , Duran Romulo 
Published: April 2022

Abstract
The Margarita-10 well is located in the Caipipendi block of southern Bolivia. This block accounts for 30% of the gas production for the entire country. An operator faced a unique challenge to reduce well construction costs for this well while drilling through a harsh Carboniferous formations sequence. The Carboniferous interval is extremely hard and abrasive with unconfined compressive strength (UCS) values ranging from 12,000 psi to 24,000 psi, averaging 20,000 psi with 32,000-psi peaks. This interval is a complex drilling environment consisting of interbedded, conglomeratic, hard, and abrasive lithologies that presents several drilling obstacles, such as formation heterogeneity and multiple geological uncertainties. These obstacles often impact drilling operations, including the bit's cutting structure being prematurely damaged that can cause nonproductive time typically related to the low rate of penetration (ROP) and poor drillsting dynamic behavior with severe levels of shocks; thus, multiple unwanted bottomhole assembly (BHA) trips.
The operator's major objective was to reduce the high well-construction costs in this environment; hence, motivating the search for a novel drill-bit cutting structure design that would produce a step change in drilling performance. To accurately determine the environmental factors that influence bit performance, a detailed formation mapping analysis was performed using geological data from nearby wells. To comprehend the causes of previous failure modes, an engineering program for rock-strength identification was used to identify the rock types and lithology characteristics, such as rock UCS, expected formation abrasion, and impact indexes. This information was used to adjust the digital rock file and better reproduce the actual drilling conditions in the Carboniferous strata virtually to develop a suitable cutting structure, predict drillstring vibrations, and recommend optimal drilling parameters to maximize the performance of the product downhole.
The operator successfully deployed the novel drill-bit cutting structure, achieving above field average results in terms of ROP and footage. The first drilling run in the Margarita-10 well drilled over 300% additional footage (compared with the offset average) at double the ROP and was pulled out of the hole (POOH) with an excellent dull condition. The drilling second run was equally successful, with 332% additional footage drilled and 45% higher ROP compared with the offset average. These results reduced the rig time by 20 days, resulting in significant well construction costs savings for the operator. Moreover, considering 2.6 kg of CO2 per liter of diesel burned daily resulted in allowing more than 600 t of CO2 emission reduction.
Optimizations of Downhole Sealing Devices and Their Validations
Authors: Zhong Allan , Fan Zhihua , Purohit Ankit , Jutu Mothilal Balaji Prasad 
Published: April 2022

Abstract
Due to complexity and strong nonlinear behavior, design of sealing devices such as packer elements has heavily relied on empirical knowledge for many years. Since downhole environments continue to grow harsher, especially in high-pressure, high-temperature (HPHT) conditions, there are large gaps between existing empirical knowledge and the notable challenges faced in the sealing element development process. These gaps have often led to a trial-and-error approach to design, extending development time through frequent delays and resulting in significant cost overruns. Moreover, empirical knowledge gained through the process for a particular design has very limited application to other designs. To overcome these deficiencies of a traditional development approach, a combination of computational modeling, materials science, and small-scale tests was used in the early stage of a development project to optimize sealing element design, which substantially reduced the number of physical tests and ultimately reduced the overall development cycle time and cost.
The main feature of this optimization/improvement of sealing element design is to use engineering mechanics principles, primarily via computational modeling, as guidelines for sealing device development instead of relying heavily on empirical knowledge. The computational models are calibrated and validated through small scale physical tests, which ensure the fidelity of the computational models. The optimum packer element designs are obtained through a systematic numerical scheme along with machine learnings. Considering the approximations and limitations of numerical models, the numerically determined optimum design is further evaluated, examined against real application conditions, and adjusted as needed, before being finalized.
The result is an efficient, physically calibrated and validated computational model for packer element behavior. This model along with machine learning algorithms is applied to the optimization of a 9 5/8" packer element which successfully passed both API 11D1 type V3 and V0 testing on the first attempt. Using the same optimization approach, a 7" packer element was developed in a short period of time and passed API 11D1 type V3 and V0 tests on the first attempt again. These two development case histories demonstrated the generality and effectiveness of the engineering mechanics principle-based optimization approach; it substantially reduced the number of physical validation tests and shortened the development cycle time resulting in a cost savings of over 25%. This design approach has been extended to additional challenging new product development projects.
Reducing Hazards and Risk Through Virtual Risk Assessment
Authors: Boyd Leah , Mills Laura , Jacintho Andrea , Martinez Gus 
Published: April 2022

Abstract
For companies with worksites around the globe, assessing risks can be a challenge, particularly when different countries might adhere to different rules and regulations regarding workplace operations. This paper reviews a novel risk assessment template designed to evaluate a wide variety of risks on a "level playing field" for each manufacturing site office, offshore platform, and other work locations. The template, which is completed by qualified personnel at the specific site, ranks facilities based upon their environmental, health, and safety (EHS) risks as well as ISO requirements incorporating quality and EHS standards.
How the First Jones Act Compliant Wind Turbine Installation Vessel is Helping to Develop the U.S. Offshore Wind Supply Chain
Authors: Huang Wei , Cheung Tak On , Wagner Karel , Morandi Alberto , Humberson Karl E 
Published: April 2022

Abstract
Meeting the U.S. offshore wind target of 30 GW installed capacity by 2030 will present significant supply chain opportunities for port infrastructure developments, major windfarm components, increased steel demand, and construction of transportation and installation (T&I) solutions in U.S. shipyards such as specialized wind turbine installation vessels (WTIVs). This paper discusses how the design and construction of "Charybdis" –– the first Jones Act Compliant WTIV –– are helping to drive the supply chain for offshore wind development and the energy transition toward more sustainable solutions.
A consortium of designer and shipyard led by a major developer, Dominion Energy is building the Charybdis WTIV. This paper highlights how the project team leveraged experience in the oil and gas industry and offshore wind from Europe to develop the first specialized WTIV for U.S. wind farm construction and East Coast operation. The paper also discusses important aspects such as project execution, risk assessment, technology innovation, and compliance with U.S. Regulations and safety standards.
The Charybdis is one of the largest WTIVs in the world and an important enabler for a safer and more cost-effective deployment of U.S. offshore wind energy and a move toward sustainable energy solutions. The insights and experience gained with the concept development, design, and construction of Charybdis, will benefit the next generation turbine installations in the U.S. and worldwide, and promote the collaboration of future wind turbine supply chain development.
Classification of an Offshore Wind Farm
Authors: Almeida Patricia Lace de , Samuelsson Lars Henrik , Santos Filipe Branco 
Published: April 2022

Abstract
Floating offshore wind is a relatively young industry but is not all new. Many of the items we see in a floating offshore wind turbine installation, can also be found in traditional offshore installations, and it is important to build on these past experiences.
This paper will clarify how Class was used in the WindFloat Atlantic project to benefit from past experiences and at the same time considering the unique risk profile and loads acting on a floating offshore wind farm. This paper also addresses how the Class approach satisfied many of the local authorities’ requirements.
Predictive Digital Twin for Performance and Integrity
Authors: Straw Matthew , Mistry Rikesh , Lewis Simon , Aglave Ravindra , Kolak Doug 
Published: April 2022

Abstract
The concept of the digital twin can be thought of as a virtual representation of a physical product, engineering system or facility. This paper presents the role of predictive engineering analytics, alongside operating data, in the digital twin. Using case studies, the authors demonstrate how predictive approaches can be developed to provide data where it cannot be measured and predict future operating data to improve performance, life and integrity of equipment, systems and facilities.
The digital twin is fundamentally based on data, larger datasets provide greater insight. Sensor and inspection data are critical. However, there are scenarios in which engineers require data where it cannot be measured or requires data that cannot be measured. Engineers require ways to extract this data, this can be done through predictive engineering analytics. Predictive engineering analytics, in the form of science-based simulations, combines multiple approaches, often based on fundamental principles of physics and engineering. This paper will demonstrate how high-fidelity approaches, such as Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD), combined with system-level simulation and reduced-order modelling can work together with field data to provide this data in real-time.
Two case studies presented show how combinations of different levels of science-based modelling approaches can help. A subsea thermal digital twin demonstrates how high-fidelity simulations, undertaken during system design, can be the foundation for reduced order system models capable of capturing critical thermal performance in real time; aiding hydrate risk management during operation. The methods used to train the real-time predictive approach are demonstrated. The second case presented focuses on structural integrity of a heat exchanger showing how real-time sensor data can be translated into structural integrity data and insight through simulation. The cases presented demonstrate the value of science-based predictions to generate data that cannot be obtained from operational sensor data alone. The authors aim to show how the predictive element of the digital twin can be first generated during design and evolved into real-time predictive approaches that provide operations with data that cannot be gained from sensors; when and where it is needed.
Digital twins typically use physical data, limiting operators of in-field equipment to make operational decisions based on information from sensor locations and historical data alone. This can limit assessment of operational performance and integrity of complex production and process systems. In this paper the authors aim to show how, by combining predictive approaches, at differing levels of fidelity and based on fundamental scientific principles, it is possible to generate the missing data required, in both space and time. This can inform and guide operations; filling the gaps where and when physical data is not available.