bluuebunny/crossref_metadata_2025_split huggingface dataset:
First Use of ROV Remote Operations from Shore in the Gulf of Mexico
Authors: Silva Simao , Terrell Blake , Philip Mark , Rouge Nicholas , Angelidis Diogenes , Sosa Julio , Collins Robert , Rauf Zain 
Published: August 2021

Abstract
Oil and gas companies across the spectrum are moving toward digitalization. Leveraging technology to access real-time data has allowed companies to streamline activities and gain operational efficiencies while at the same time improving worker safety by reducing the number of personnel required offshore. This evolution optimizes operations by enabling better decision-making by subject matter experts (SMEs) located around the world working as one interconnected team. Functions once performed exclusively by offshore personnel are being carried out today by onshore workers via remote technology. By capitalizing on the ability to communicate offshore via high-speed internet, it is now possible to carry out ROV operations using a team that includes onshore based personnel.
A recent project illustrates how ROV activities controlled from an onshore remote operations center in Louisiana were carried out successfully on a production Tension Leg Platform (TLP) in the Gulf of Mexico (GoM).
The technology used onboard the TLP is not new; operators have been remotely managing a range of functions on offshore assets for years. However, the project does apply this proven approach to ROV piloting operations for the first time commercially in the GoM.
Transferring ROV control from the offshore platform to a facility onshore is possible using a communication link that connect real-time data from the offshore asset to the onshore remote operations center (OROC). The two-way communications link provides a redundant system in which controls can be executed either from the offshore platform or from the remote operations center, allowing specialized roles that historically have been executed offshore, including that of the ROV pilot, subsea engineer, and company representative directing the work, to be transferred to a land-based team. The increase in data required from the offshore asset for the GoM project was managed via a dedicated link that provided data transfer at a minimum speed of 3 Mbps upload/download with a fail-safe system that automatically default control to the offshore ROV team in case of any failures in the communication link.
Remotely piloting an ROV from shore and coordinating with an offshore crew not only delivered a reduction in HSE exposure but reduced overall personnel costs on the asset by more than 30% for 24 hours of operations. This approach to ROV operations has the potential to reduce costs by reducing the number of workers required offshore even further if additional staff associated exclusively with the project subsea work scope is directed to work remotely from shore.
Machine Learning-Based Optimization for Subsea Pipeline Route Design
Authors: Bhowmik Subrata 
Published: August 2021

Abstract
Optimal route selection for the subsea pipeline is a critical task for the pipeline design process, and the route selected can significantly affect the overall project cost. Therefore, it is necessary to design the routes to be economical and safe. On-bottom stability (OBS) and fixed obstacles like existing crossings and free spans are the main factors that affect the route selection. This article proposes a novel hybrid optimization method based on a typical Machine Learning algorithm for designing an optimal pipeline route. The proposed optimal route design is compared with one of the popular multi-objective optimization method named Genetic Algorithm (GA).
The proposed pipeline route selection method uses a Reinforcement Learning (RL) algorithm, a particular type of machine learning method to train a pipeline system that would optimize the route selection of subsea pipelines. The route optimization tool evaluates each possible route by incorporating Onbottom stability criteria based on DNVGL-ST-109 standard and other constraints such as the minimum pipeline route length, static obstacles, pipeline crossings, and free-span section length. The cost function in the optimization method simultaneously handles the minimization of length and cost of mitigating procedures. Genetic Algorithm, a well established optimization method, has been used as a reference to compare the optimal route with the result from the proposed Reinforcement Learning based optimization method.
Three different case studies are performed for finding the optimal route selection using the Reinforcement Learning (RL) approach considering the OBS criteria into its cost function and compared with the Genetic Algorithm (GA). The RL method saves upto 20% pipeline length for a complex problem with 15 crossings and 31 free spans. The RL optimization method provides the optimal routes, considering different aspects of the design and the costs associated with the various factors to stabilize a pipeline (mattress, trenching, burying, concrete coating, or even employing a more massive pipe with additional steel wall thickness). OBS criteria significantly influence the best route, indicating that the tool can reduce the pipeline's design time and minimize installation and operational costs of the pipeline.
Conventionally the pipeline route optimization is performed by a manual process where the minimum roule length and static obstacles are considered to find an optimum route. The engineering is then performed to fulfill the criteria of this route, and this approach may not lead to an optimized engineering cost. The proposed Reinforced Learning method for route optimization is a mixed type, faster, and cost-efficient approach. It significantly minimizes the pipeline's installation and operational costs up to 20% of the conventional route selection process.
Feasible SCR Configuration from FPSO with Large Motions, by Applying Residual Curvature Methods
Authors: Ramiro Andre Amorim , Orimolade Adekunle Peter , Karunakaran Daniel Nalliah 
Published: August 2021

Abstract
This paper describes an alternative and innovative solution to Steel Lazy Wave Risers (SLWR) in order to allow the rigid riser be designed in a free hanging configuration from a floater with large hang off vertical motions.
This work demonstrates that the residual curvature method can reduce the stress at the touchdown zone (TDZ) within acceptable limits and improves fatigue life when compared to a Steel Catenary Rigid (SCR).
The state-of-art concept eliminates the necessity of buoys in a SLWR concept. Further, there will be saving in installation time, this combined reduces the CAPEX to develop new projects, making it a very attractive solution in recent market scenario.
The results for Brazil offshore scenario at Santos Basin and in Gulf of Mexico, in a water depth of 2100m indicate a considerable reduction on the stress at the TDZ within acceptable limits.
Fatigue results indicate a slight improvement when compared to a SCR in a free hanging configuration. The fatigue performance for the Gulf of Mexico condition has sufficient fatigue life. However, it has potential margin to be developed in the future and achieve better results.
The new concept was evaluated numerically through a comprehensive load case matrix contemplating extreme and fatigue analysis. Results indicated the feasibility of the solution, making it a considerable alternative to replace SLWR in harsh environments.
Mitigating Wellhead Fatigue While Reducing HSE Risk, Deck Spread, Deployment Time, and Crew Size
Authors: Ulland Gunnar , Hilsen Gunnar , Croatto Stefano 
Published: August 2021

Abstract
Subsea wellhead systems have a design fatigue life that is expected to withstand the damage incurred from stress caused by cyclic loading during its operation. Wellhead fatigue is a critical factor when drilling offshore wells because the condition of the wellhead determines the length of time drilling activities can be carried out safely.
The presence of the BOP on top of the wellhead affects fatigue life. Initially, these units were designed for 6-in. and 10-in. diameter casing and weighed slightly less than 1,400 lb. [ASME, 2003) Over time, BOPs evolved, and today's units are considerably larger and heavier than their predecessors, weighing in at approximately 400 metric tons. This increase in size and weight on the wellhead negatively impacts fatigue life.
In recent years, the oil and gas industry has begun to look for ways to reduce wellhead fatigue to extend the life of the wellhead and expand the margins for safe drilling operations. A new ROV-operated Wellhead Load Relief (WLR) system, developed specifically to mitigate fatigue, uses special tensioners that are tethered individually to the BOP. Each tensioner contains a hydraulic spooling unit with a lock- and-pull mechanism that allows the ROV to tighten adjustable tethers subsea, pulling them from slack to a maximum tension of 35 metric tons.
This approach to installation is a departure from common industry practice, which necessitates the configuration of the predetermined tether lengths on the topside. The ROV-operated WLR system described in this paper is a compact, high-capacity BOP tethering system suitable for both template and seabed anchoring. It provides a new and efficient way of tethering the BOP to avoid wellhead fatigue and delivers additional benefits that include minimized HSE risk, a smaller deck spread, decreased deployment time, and a smaller crew.
The WLR system was operated subsea for the first time in December 2019. By precisely tensioning each tether and limiting the load transferred to the wellhead, the WLR significantly lessened wellhead fatigue, resulting in an almost complete halt in BOP movement.
This new technology enables the operator to make optimal use of the fatigue life of the wellhead without compromising efficiency or safety.
Subsurface Characterization Using Ensemble Machine Learning
Authors: Leiceaga Gorka G , Balch Robert , El-kaseeh George 
Published: August 2021

Abstract
Reservoir characterization is an ambitious challenge that aims to predict variations within the subsurface using fit-for-purpose information that follows physical and geological sense. To properly achieve subsurface characterization, artificial intelligence (AI) algorithms may be used. Machine learning, a subset of AI, is a data-driven approach that has exploded in popularity during the past decades in industries such as healthcare, banking and finance, cryptocurrency, data security, and e-commerce. An advantage of machine learning methods is that they can be implemented to produce results without the need to have first established a complete theoretical scientific model for a problem – with a set of complex model equations to be solved analytically or numerically. The principal challenge of machine learning lies in attaining enough training information, which is essential in obtaining an adequate model that allows for a prediction with a high level of accuracy. Ensemble machine learning in reservoir characterization studies is a candidate to reduce subsurface uncertainty by integrating seismic and well data. In this article, a bootstrap aggregating algorithm is evaluated to determine its potential as a subsurface discriminator. The algorithm fits decision trees on various sub-samples of a dataset and uses averaging to improve the accuracy of the prediction without over-fitting. The gamma ray results from our test dataset show a high correlation with the measured logs, giving confidence in our workflow applied to subsurface characterization.
Solving Decommissioning and Abandonment Problems When Planning to Shear 9 5/8-inch Casing in a 13 5/8-inch × 10k RAM BOP
Authors: Cain David Earl , Klopfenstein Keith Albert , McMullan James Robert 
Published: August 2021

Abstract
A decommissioning and abandonment requirement to shear 9 5/8-inch casing in certain circumstances with a 13 5/8-inch × 10,000 psi rated working pressure, RWP, Shear RAM type blowout preventer, BOP, resulted in a need to develop a novel casing shear device and shear calculation method. Results of shear testing, future engineering planning guidance, the new shear calculation method, and comparison to legacy technology are included in this paper.
Interaction with the end user to understand requirements, a five-step problem solving procedure, a basis of design process, materials justification, verification analysis, validation testing, and describing an improved shear operator force/pressure calculation are all described. Shear larger casing in the required and restrictive RAM BOP and well bore presented a problematic challenge. Equally, tubular fish size was required to support fishing extraction operations following shear. Validation test results exceeded requirements and resulted in the need for a new approach to the shear calculation method.
The novel shear RAM geometry was developed utilizing shear calculation methods which were based on legacy considerations. API 16A shear validation procedures and two legacy shear calculation methods where employed. Shear calculations are used to anticipate the RAM BOP operator pressures required to shear a specific tubular. The larger than historically allowed casing size to be sheared in a 13 5/8-inch × 10,000 psi RAM BOP meant higher operator pressures were anticipated for each operator option. A Novel shear RAM geometry was developed as a design intent to lower shear force/pressure. There was an observation during validation testing that the geometry exceeded expectations to lower shear pressure significantly. This observation led to a conclusion that an improved shear calculation method was required for this application. This novel calculation method description / statistical treatment, test results, RAM design methods, and tabular shear engineering planning information are included in this paper.
One additional requirement of the shear RAM geometry was to provide an upper and lower fish deformed surface which could be easily retrieved thru the 13 5/8-inch BOP bore. An additional observation was that the fish width requirement was achieved.
The novel shear calculation method allows an engineer to precisely plan for bonnet actuation pressures when larger casing is sheared. The precise calculation of shear force/pressure also assists with BOP operator size and type selection. The engineer will gain casing size versus shear pressure for specific operator options in tabular format. Planners will gain insight into tubular fish deformation estimation allowing mitigation of tubular extraction risk during operations planning.
Predicting Porosity and Water Saturation from Well-Log Data Using Probabilistic Multi-Task Neural Network with Normalizing Flows
Authors: Lee Jinwoo , Kwon Minsu , Hong Youngjun 
Published: August 2021

Abstract
In the oil and gas exploration process, understanding the hydrocarbon distribution of a reservoir is important. Well-log and core sample data such as porosity and water saturation are widely used for this purpose. With porosity and water saturation, we can calculate hydrocarbon volume more accurately than using well-log solely.
However, as obtaining core sample data is expensive and time-consuming, predicting it with well-log can be a valuable solution for early-stage exploration since acquiring well-log is relatively economic and swift. Recently, numerous studies applied machine learning algorithms to predict core data from well-log. To the best of our knowledge, most works provide point estimation without probabilistic distribution modeling.
In this paper, we developed a probabilistic deep neural network to provide uncertainty via confidence interval. Besides, we employed normalizing flows and multi-task learning to improve prediction accuracy. With this approach, we present the model's uncertainty that can be reliable information for decision making. Furthermore, we demonstrate our model outperforms other supervised machine learning algorithms regards to prediction accuracy.
Development of Design Criteria and NDE Method for HPHT Equipment
Authors: Pathak Dilip Parth , Padmanabha Kishore , Wilhelmi Robert Joseph , Coutinho Luiz Henrique Velloso , Vampati Veera Venkata Rao Brahmaji 
Published: August 2021

Abstract
Subsea production systems have been using API-specified methods and detection criteria for nondestructive examination (NDE) for equipment up to 15,000-psi rated working pressure. With higher pressure requirements and use of methods for evaluation of the fatigue life of HPHT equipment, existing criteria cannot be sufficient to achieve the desired fatigue life. This paper demonstrates more stringent design criteria and a detection method that was developed to achieve HPHT fatigue life, yet is cost and schedule effective.
When fracture mechanics was used for fatigue life estimation, an initial flaw size is stated as a starting point in determining design life. Using API-specified NDE criteria of 1/16-inch detection of surface flaw, project design life was not achieved for certain components without a major redesign and retest. Smaller flaw lengths were preferred in critical areas instead of standard flaw sizes. This created a need for a high-sensitivity penetrant inspection technique to effectively detect this new minimum flaw size in critical areas. Various methods of NDE were considered, and a conclusion was taken in selecting the best inspection method.
Fracture mechanics and finite element analysis required a minimum detectable flaw size length of 1/32 inch to meet the project design life without changing equipment technology. By using surface NDE with fluorescent liquid dye penetrant of sensitivity levels 2 and 3, all seeded flaws of 1/32-inch were consistently detected in the validation test coupon, which enabled the use of this stricter criteria for the analysis. Detailed procedures were established, and validation testing results were documented with photographs of detected flaws. Seeded flaw coupons were manufactured for validation of procedures and to train the various facilities that will use these techniques for production equipment. Additionally, procedures and guidelines were provided to inspectors and inspection facilities to ensure proper implementation of the methods. Measurement system analysis for repeatability and reproducibility was conducted at the facilities. This enabled the fatigue design of the HPHT equipment to advance beyond the boundaries of traditional methods and acceptance criteria set by current industry standards.
New and tighter acceptance criteria were developed to improve HPHT fatigue life. High-sensitivity penetrant inspection technique, capable of detecting flaw sizes as small as 1/32-inch, was established and implemented. This inspection technique is not common to the oil and gas industry because of the ability of standard methods to readily detect the API-required criteria. The method has improved detection capabilities and has the potential to move toward adopting advanced design methods to address HPHT requirements.
Real-Time Digital Chemistry Offshore Transforms Flow Assurance Management
Authors: Lovell John R. , Kulbrandstad Omar , Madem Sai , Meza Daniel 
Published: August 2021

Abstract
Managing asphaltene accumulation in offshore Gulf-of-Mexico wells is a significant challenge. Until recently there was no real-time chemical monitoring that could advise on whether chemical inhibition was making a particular well more, or less, stable. This changed with the development of real-time hardware that directly measures the ratio of asphaltene flowing in the oil. A new generation of that hardware has now been launched which meets all of the Qualification and HSE requirements for deployment on offshore platforms.
A microwave resonator was designed to receive fluid at wellhead conditions, i.e., without a reduction in pressure or temperature, and the parameters of that resonator were optimized to maximize microwave intensity for typical oilfield fluids. The microwave circuitry is incorporated in an explosion-proof container with Class 1 Div 2 rated electrical and fluid connections. By combining that resonator with a solenoid that can generate a large magnetic field around a flowline, the resulting device resonates electrons within asphaltene molecules to create a unique signature that is proportional to the total asphaltene count. Estimates of oil-water cut and gas-oil ratio are also obtained as part of the processing and this combination gives the percentage of asphaltene within the oil. The use of this hardware with controlling software and cloud processing creates a unique Internet-of-Things device which can be used to optimize asphaltene-related flow assurance challenges offshore.
Pressure testing up to 5ksi and 120C gives the device a working envelope well exceeding typical offshore production hardware requirements. For a fixed fluid, the computation of asphaltene ratio was shown to be independent of applied pressure. Conversely, it was found that in a live well chemical properties of fluids can change over the course of a few hours even when the surface pressure and flow-rates stay the same. In one well, the surface asphaltene percentage within an oil was seen to vary from 0.3% to 3% because of alternating deposition and erosion of an asphaltene layer that had been forming along the ID of production tubing. Over the course of a series of tests in the Middle East, it was observed that those wells with uniform asphaltene percentage were seen as less troublesome to manage compared to wells with a higher deviation. In two Permian fields subject to CO2 flooding, a geographic variation in asphaltene percentage which correlated to the long-term exposure to injected gas was observed.
It has long been standard for chemical properties of fluids to be obtained by sending samples to a lab. This paper demonstrates additional value that can be obtained from getting that data in real-time, especially when viewed in the context of an overall chemical management program.
Deepwater Riser & Mooring Life Cycle Digital Integrity Management
Authors: Renzi David , Nadathur Varadarajan , Wong Steve , Zhang Michael , Chen Yong , Zarate Luz , Fan Shejun , Ayewah Daniel , Chakraborty Ishita , Kucukcoban Sezgin , Mistric Matt , Kurrus Keith 
Published: August 2021

Abstract
API SC2 has issued integrity management recommended practices to compliment the planning, design and construction standards that has led to a GOM installed base of 50 floating production facilities. Integrity management involves risk-based inspections in which the collection and analysis of characteristic and conditional data determines if integrity can be assured. The challenge to manage unstructured data with the same design and construction approaches yields inefficient and repetitive activities that can be time-consuming and may lead to a less optimal outcome.
The primary objective of the study is to understand the benefits of utilizing a digitized IM approach which involves high resolution metocean data to determine fatigue damage accumulation for tendons and risers instead of traditional design/assessment methods that use condensed metocean criteria data.