bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and Qualification of a Purpose-Built 3,000,000 Lb Landing String System Enables 20k Exploration
Authors: Sanclemente L. , Broderick E , Primeaux A. , Plessis G. , Muradov A. , Marchman K. , Sommer M. 
Published: February 2025

Abstract
One of the first 20,000 psi Paleogene development projects is located in the Green Canyon area, approximately 140 miles (225 km) off the coast of Louisiana, in water depths of approximately 5,000 feet (1,524 m). The subject well will be drilled to approximately 30,000 feet and will require six drilling casings plus production liner and tieback. A purpose-built drilling landing string with 3-million-pounds capacity will be needed to land the 16" and 14" casing strings and the 10 1/4" by 11 1/4" production tieback string.
This paper documents an extensive development effort spanning over seven years. Initially, 6 5/8", 7" and 7 5/8" solutions were considered with landing capacities of up to 4-million-pounds. A 6 5/8" 3-million-pounds design was selected for qualification testing. The development of the landing string's design configuration and performance specifications are presented.
Qualification testing of the landing strings included: friction weld qualification, FEA to optimize the connection pressure sealing capability, FEA of elevator shoulder/upset region for cross-sectional and elevator shoulder yielding, shear testing of the pipe body and slip-proof sections, combined load testing of the landing string assembly, and slip-crush testing of the pipe body and slip-proof sections. Test procedures and results are presented.
This 6 5/8" landing string has the highest capacity at 3-million-pounds of any purpose-built landing string manufactured.
Bigger isn't Better: Fundamental Differences in Flow Regime Development from Rotating Pipe
Authors: Rathgber D. C. , Johnson E. L. , Anderson R. , Lucon P. , Todd B. J. , Downey J. , Richards L. 
Published: February 2025

Abstract
Operators often look to use drill pipe rotation to increase hole cleaning effectiveness in horizontal and high-angle sections of wellbores. However, the effect of drill pipe rotation on cuttings transport is dependent on a range of factors, from fluid rheology and density, rotation rate, hole diameter, and pipe diameter. To identify cause and effect of these parameters, several multiphase flow simulations were performed to identify possible changes in flow regimes that would affect cuttings transport efficiency.
Computational Fluid Dynamics (CFD) software was utilized to develop and simulate a range of single - phase and multiphase flow models replicating a range of typical high-angle tangent and production sections. Wellbore diameters ranged from 8 ¾" to 12 ¼", and drill pipe diameters ranged from 4 ½" to 6 5/8", with rotation rates ranging from 0 to 150 RPM at 30 RPM increments. In order to observe the effect of fluid rheology, 2 field-sampled muds were modeled - a low density water-based mud and a medium density oil-based mud. Cross-sectional data including velocity, effective viscosity, and turbulent kinetic energy, were analyzed from single-phase models for changes in flow patterns, and models that showed significant differences at adjacent RPM increments were selected for multiphase model runs to determine if changes had an observable effect on cuttings movement.
Data from simulation results indicate linear increases in velocity in connection to pipe rotation rates. However, marginal differences between different hole or pipe diameter sizes were recorded. Distinct differences between effective viscosity profiles were observed when comparing larger diameter wellbores with smaller wellbores, with sudden increases in bulk viscosity at high RPM in 12 ¼" hole. At high RPM (120+), large wellbores show a sudden decrease in both Reynolds and Taylor numbers, indication a sudden change in flow regime cause by high rotation rates. This trend differs from smaller wellbores in which both Reynolds and Taylor numbers continually increase with increasing pipe rotation. Data suggests that hole size plays a critical role in the impact drill pipe rotation has on cuttings transport through changes in fluid viscosity and fluid turbulence.
Having a better understanding of the effects of hole size, pipe size, fluid rheology and pipe rotation rate could play a significant role in delivering wells more efficiently, through the reduction of non-productive time from circulations. Additionally, the risk-reduction from reduced torque, drag, stuck tools would make expensive, extended reach drilling more appealing - reducing environmental impact through emissions, surface footprint, and economic cost.
Modeling and Simulation of Gas Migration Influx from Multiple Zones During Sampling in Drilling
Authors: Kang Yongfeng , Samuel Robello 
Published: February 2025

Abstract
During well operations, an influx of hydrocarbon may be released into the well from the reservoir. When an influx is majorly composed of gas, it migrates upward along the well. As the gas migrates, it expands and displaces the mud out of the well, leading to a decrease in well pressure. If the pressure decreases excessively, it can result in an underbalanced condition that can trigger further influx, potentially leading to a disastrous situation.
This study models the migrating velocity of gas bubbles, and their expansion in response to changes in temperature and pressure. By considering the effect of time, the location and volume of gas bubbles in the well are calculated. An iterative method allows to obtain a pressure profile along the well. The pressure history at any given depth of interest is also derived. Compared to the pore pressure, the time-to-be-underbalanced at which the well becomes underbalanced at that depth can be determined. This information can guide well sampling operations in drilling by indicating how long the operation can be safely operated and when to take action for preventing issues.
This study models and simulates gas migration during sampling operation, providing pressure and temperature histories at key depths, including the casing shoe and influx depths. It also presents the total influx volume history which could be interpolated as pit-gain at the surface. Generally, well pressure decreases as gas bubbles migrate upward, especially as they near the wellhead. Here, rapid expansion causes a swift increase in total influx volume and a corresponding drop in well pressure. This suggests that operators should intervene before bubbles approach the wellhead. The calculated time-to-be-underbalanced is a valuable indicator of whether and when the well will become underbalanced. If the well remains balanced even when all bubbles reach the wellhead, it suggests the well will not become underbalanced. The total influx volume is flat once a bubble front reaches surface and bubble end not yet reaches surface. The study also explores special scenarios where well pressure decreases firstly then "abnormally" increases.
This study provides insights into well pressure behavior during sampling with gas influx. It introduces a time-to-underbalanced indicator, informing engineers whether and when the well may become underbalanced. This knowledge empowers operators to conduct operations confidently, minimizing potential gas kicks, thereby safeguarding lives and other values.
Drilling Automation: Revisiting the Digital Drilling Program
Authors: Cayeux E. , Daireaux B. , Pelfrene G. , Mihai R. 
Published: February 2025

Abstract
In parallel with the increasing adoption of drilling automation at the rig site, there is a notable emphasis on the digitalization of the drilling program. These activities should not be viewed in isolation, as reinventing the drilling program may lead to significant breakthroughs in how automated processes are seamlessly integrated into drilling operations. Drilling operations are largely predictable, with parameters such as the number of sections and stands typically set in advance and rarely updated. However, significant uncertainty often exists regarding formation depths, and geomechanical limits may differ from those prognosed. Additionally, the state of the drilling system is never known with certainty and can be significantly impacted by factors such as cuttings transport or drill-string vibrations. Furthermore, unexpected drilling events occur frequently, forcing the drilling crew to adapt to unforeseen circumstances that were not defined in the original plan. Efficiently managing uncertainty requires more of a strategy than a plan, enabling adaptation to various situations while fulfilling the objectives of the drilling operation. A set of strategies can then be directly utilized by drilling automation functions during well construction. These strategies form the foundation of a new version of the digital drilling program. The implementation of the digital drilling program presented in this paper is based on a top-down analysis that refines well design choices by controlling the level of detail. These choices are guided by constraints and uncertainties established by a multidisciplinary well engineering team. To avoid a combinatorial explosion in the number of considered drilling scenarios, the algorithm balances exploration and exploitation constraints at each stage of the optimization process. A similarity metric is defined to group together connectively equivalent scenarios, which are passed to the next step of the iterative process, resulting in only a few hundred possible solutions to work with, rather than an overwhelming number. At any stage, each possible choice is evaluated from a multi-objective perspective, such as through directional, hydraulic, or mechanical simulations. However, the level of detail for this evaluation varies with the depth of investigation. This approach helps uncover, as early as possible, alternatives that are not viable, allowing them to be discarded. Using this framework, the engineering team is left with a manageable set of design choices that respect its own rules and constraints, widely and efficiently sample the parameter space, and can be filtered and sorted to retain the most robust and effective well construction solutions. Digitalization is not only about the seamless transfer of information. By envisioning a comprehensive digital workflow from well design, engineering, and planning to construction, it is possible to optimize drilling operations to an extent previously unattainable.
Early Warning System for Diesel Engine Failures in Drilling Operations using Long Short-Term Memory Network
Authors: McCarthy K. , Ziehm T. , Mitkus A. , Gee T. , Reynerson P. , Lee H. , Gupta P. , Willerth M. 
Published: February 2025

Abstract
Efficient maintenance of rig components is vital for uninterrupted and efficient drilling. Common practices rely on maintenance scheduled by hours of use, often leading to either unnecessary costs or critical failures due to overlooking the scope of service intensity during operations. Excessive maintenance can lead to unnecessary repairs and waste, while insufficient maintenance risks failures interrupting drilling operations with costly and time-consuming repairs.
Condition-based maintenance leverages continuous monitoring of a component's health to schedule service more efficiently but requires having an accurate model of system performance. Building these models is challenging due to system complexity and interactions among various input channels. This study proposes a workflow for a continuous health assessment of diesel engine generators that power rigs.
This study employs machine learning techniques specifically designed to capture complex, non-linear relationships within system performance data. By focusing on key indicators of component health, the model identifies patterns that inform proactive maintenance decisions.
For a given engine, a Long Short-Term Memory (LSTM) network is trained on historical healthy engine data to accurately predict exhaust temperature, a critical indicator of engine health. This training process creates a detailed model of expected engine behavior under normal conditions. Post-training, the model's predictions are compared with actual exhaust temperature measurements. Significant deviations between the predicted and observed values serve as early indicators of potential engine issues, guiding maintenance actions. To evaluate the effectiveness of this workflow for continuous engine health monitoring, it was tested on multiple sets of historical engine data, including cases of both normal operation and known failures.
After the training period, the models developed through this workflow show mean absolute errors (MAE) ranging from 7 to 15 °F when comparing predicted exhaust temperatures to measured ones. Engines that do not experience heat-related maintenance issues continue to exhibit low MAE values, whereas increasing MAE values are observed before heat-related failures. This pattern demonstrates the workflow's ability to provide early warnings before failures occur. By enabling timely interventions, this approach significantly improves maintenance efficiency, leading to reduced costs and downtime.
The application of data-driven machine learning for condition-based rig maintenance provides a step change from current common practice, with the potential for cost savings and decreased rig downtime. The strategy of using degrading model predictions as an indicator of physical system change is also a novel use of an LSTM model. These methods are not unique to engines and similar workflows could be followed for condition-based maintenance of additional rig components.
Automated Drilling and Production Event Detection Using Advanced Time-Series Pattern Recognition Techniques
Authors: Montes Abraham C. , Sudyodprasert Kulpitcha , Wu Yuxing , Ashok Pradeepkumar , van Oort Eric 
Published: February 2025

Abstract
The identification of the well/rig state in time is a key component in the construction of accurate risk-assessment, event-detection, and efficiency-tracking tools in drilling and production operations. Traditionally, this state identification has relied on insufficient rule-based systems, which often results in inaccurate predictions and leads to non-reliable risk-assessment tools, imprecise event detection, and biased estimated efficiency. This paper compares three state-of-the-art, scalable methods for automatically identifying the well/rig state and presents three use cases in drilling and production stages.
Identifying the well/rig state is a time-series multi-class classification problem, in which the data is collected at high frequency (typically 0.02–1 Hz) with sensors installed inside the well, on the wellhead, or on the equipment intervening the well, such as a rig or a coiled-tubing unit. This paper presents three solutions to this classification problem, namely a first-order logic inference system, a recurrent neural network (RNN) classifier, and a transformer-based classifier. We implement and compare these methods in three applications in drilling and production operations, including the detection of stuck pipe incidents and pressure trend abnormality.
The models were evaluated on a withheld, pre-labeled test dataset consisting of 75 hours of 1-Hz drilling data and 1072 h of 0.17-Hz production data. This evaluation showed that the transformer-based classifier outperformed the other three methods in all three applications. Additionally, we observed that the deep learning-based classifiers were only slightly more computationally expensive than inference systems, making all models suitable for real-time prediction. In the test, we also investigated the value of accurate well/rig state identification in the assessment of drilling and well-integrity risks. The test revealed that the lack of accuracy in this identification task can severely bias the risk assessment, for instance, by overestimating the risk of pipe sticking and generating spurious predictions of abnormal pressure trends. This, in turn, can result in unnecessary, costly preventative measures. The applicability of the analyzed methods is not limited to the examples here provided; they are applicable to any task requiring the classification of time-series data and thus, can be employed in other well operations/stages and applications of event detection.
The novelty of this paper is twofold. First, it is the first study to compare various methods for automatically identifying well/rig state, including one not evaluated before—a transformer model. Second, it is the first to propose an automated engine for well state identification during the production stage. This paper also analyzes the advantages and shortcomings of well/rig state identification models through three different applications.
Challenges and Successes of Integrating Natural Gas Generators with Energy Storage Systems
Authors: Harrist J. , van Wissenkerke M. Snijder , Barbee R. 
Published: February 2025

Abstract
Energy storage systems are an innovative solution that have been deployed at scale on land rigs in the U.S. in recent years. Designed to reduce fuel consumption and emissions by using lithium-ion batteries to store energy during low-demand periods and release it during peak demand, these systems help optimize generator usage, keeping generators in their most efficient operating range. Historically, energy storage systems have only been deployed on diesel and dual-fuel rigs, which are the most common generator types in the industry. Natural gas generators have proven to be a viable alternative to diesel, as they can utilize local natural gas infrastructure where available, offering many advantages including reduced fuel costs and ultra-low NOx emissions. Coupling these energy storage systems and natural gas generators has allowed operators to compound the fuel cost savings and emissions reductions of both systems.
Work to combine these technologies began in 2023 and a field-ready solution was first integrated onto a land drilling rig in early 2024. As natural gas generators have inherently different responsiveness to diesels, this effort involved setting up a natural-gas powered rig in a controlled environment and conducting an extensive series of tuning exercises to perfect the responsiveness of the system. Another significant challenge was overcoming the traditionally longer startup cycle times and developing a system to allow the generators to come online quickly in cases where supplemental power is needed. A custom system was developed to automate lubrication cycles and maintain sufficiently warm oil and coolant.
The solution was fully integrated with the existing rig control system and generator control system which enabled automation to control the starting and stopping of generators based on the requirements of the battery system. The natural gas generators, which were rated for periodic overload, were able to operate more aggressively than traditional diesels due to the additional capabilities. Since the initial deployment in early 2024, this integrated system has demonstrated higher savings than traditional diesel applications while drilling over half a million feet. During this period, CO2 emissions were reduced by up to 20%, fuel consumption was reduced by up to 19% and generator run hour were reduced by up to 44%.
Future work will include combining another energy source – grid power – allowing the energy storage system to perform peak shaving for the grid while paralleling natural gas generators during periods of sustained high demand.
Optimizing Drilling Operations in Real-Time: The Role of Digital Twins in Reducing Risks and Enhancing Performance
Authors: Cao Jie , Gocmen Emre , Nabavi Josef , Oedegaard Sven Inge , Kristiansen Tron Golder , Khosravanian Rasool , Solem Kristian 
Published: February 2025

Abstract
This paper presents an innovative approach to optimize drilling operations in real-time, offering enhanced risk management, improved efficiency and cost reduction. The focal point is an automated rate of penetration (ROP) optimization system powered by digital twins and artificial intelligence (AI) technologies, which effectively enhances drilling performance while mitigating potential issues.
At the core of this system are digital twins continuously calibrated using real-time data, a risk detection module leveraging model-based reasoning in AI and a fast auto-optimization module. It incorporates predictive analytics and multiple what-if scenarios to propose forward-looking advice on potential risks and operational parameters. To enhance performance while reducing risks, the system evaluates imminent risks and anticipated hole-cleaning requirements for ROP optimization.
The effectiveness of the advisory system has been rigorously tested and verified, with a specific focus on risk detection and auto-optimization modules. As shown in the case studies, the system automatically detects potential risks including poor hole cleaning, poor cuttings transport, and a leakage or washout in real-time operations. Moreover, it optimizes real-time drilling parameters by evaluating multiple scenarios on combined objectives of reducing potential drilling risks, ensuring hole cleanness, and improving drilling efficiency, ensuring the optimized results are risk-free in pursuing higher ROP. It results in substantial time savings, with up to 4.75 hours saved during a single drilling section. The results clearly demonstrate the value created through reduced drilling time, empowering the rig team to operate independently and minimizing reliance on back-office support.
Overall, this real-time optimization system offers a transformative approach to drilling operations, yielding significant time and cost reductions. Its supervisory capabilities streamline the implementation of advanced digital twin models and parameter optimization, thereby improving operational performance and driving further integrated drilling automation.
Fabricated loofah sponge supported indigenous tannery effluent Proteus Mirabilis bacteria - A Strategy for Heavy Metal Removal
Authors: Jayakumar Santhiya , Sharmila K J , Kalaiarasu Suganya 
Published: February 2025

Abstract

Background: The widespread contamination of heavy metal caused by anthropogenically introduced acidic tannery effluent into environment causes threatens living organisms.
Experimental: The paper presents the first scientific study of chromium, copper, and nickel detoxification by novel fabricated loofah sponges immobilised with indigenous organism.
Results: The antibiotic susceptibility test revealed that the indigenous isolated organism was susceptible and intermediate to 30 tested antibiotics. The strains revealed to be Proteus mirabilis R10SANT [PQ069783], Proteus mirabilis MPE4069 [PQ069764.1], Proteus mirabilis R4SANT [PQ069776.1], Proteus mirabilis R8SANT [PQ069781.1], and it showed good heavy metal detoxification potential. Chromium, copper, and nickel degradation reached 79.03, 89, and 75.35% for free cells at a dose of 150 ppm, and further increased to 85.40, 91.2, and 80.90% at a dosage of 200 ppm. Increased adsorption for chromium, copper, and nickel has been proven by immobilized strain in innovative modified loofah sponges to levels of 175.1, 184.56, and 168.02 mg/g; at 200 ppm dose, this increased by 2.51%, 1.8%, and 3.7%, respectively.
Novelty: This study proves heavy metal detoxification by novel modified loofah sponge supported indigenous organisms so it can be used for tanneries and other heavy metal contaminated sites.