bluuebunny/crossref_metadata_2025_split huggingface dataset:
CCS Well Control Impact of CO2 on Drilling Fluid Performance
Authors: Skogestad J. O. , Feneuil B. , N’Gouamba E. , Linga H. , Aasen A. , Skår G. , Roggeband S. M. , Nedrum J. , Helgeland S. 
Published: February 2024

Abstract
By the implementation of carbon capture and storage (CCS) as an important climate action, infill drilling in CO2-rich storage sites will be expected. This implies that actions for handling an influx of CO2 into the drilling fluid must be prepared for. This study aims to close some of the many knowledge gaps when drilling for CO2 storage wells.
To detect and handle CO2 well control incidents, it is necessary to develop software tools that take properly into account the properties of CO2 and drilling fluid and how the mixture of the two performs. To support the development of a such tool, experimental characterization of density, rheology, phase envelope, hydrate formation and drilling fluid stability are performed on drilling fluids mixed with CO2 for relevant pressures and temperatures. Based on these experiments and verified thermodynamic model descriptions, local models for properties of drilling fluid-CO2 mixtures are developed and integrated in an existing drilling software suite. Considerations on similarities and differences with natural gas kicks are made.
Results from the experimental campaigns are presented and analyzed with respect to how the drilling fluid properties are affected by the CO2, e.g.: How does the phase envelope of the drilling fluid-CO2 mixture change with different amounts of CO2, i.e., to which degree is CO2 dissolved in the drilling fluid under different conditions, and when is it a free gas or liquid? What is the impact of CO2 on the drilling fluid-CO2 mixture density? Under which conditions will the constitution of the drilling fluid break down, i.e., loss of rheological properties and risk of weight material sedimentation or drilling fluid component separation?
Furthermore, we consider how these effects can be captured in local constitutive models and integrated in a hydraulic model that can be used for simulating well control incidents. This study provides new insights into drilling fluid-CO2 interaction that are necessary in order to handle risks related to well control incidents when drilling into existing CO2 storage sites. The outcome of this study will help build experimentally verified model algorithms. Implemented in software tools this will enable more accurate simulations of such events than what is available now, and will help in evaluating risks, precautions and operational procedures associated with handling such events.
World's First Demonstration of Hydrogen Blending in Dual-Fuel Engines on a Drilling Rig
Authors: Kernan W. , Lowery J. , van Wissenkerke M. Snijder 
Published: February 2024

Abstract
As part of the global effort to decrease emissions through the use of alternative fuels, the first ever use of hydrogen fuel in a dual-fuel engine on a drilling rig was successfully demonstrated in the Appalachian region. The process of safely managing hydrogen on site is described along with the effects on engine emissions and performance.
Prior to the in-field demonstration, initial testing was performed in a controlled environment to simulate the site conditions and confirm engineering modeling of combustion characteristics. The demonstration consisted of initial testing at 5% hydrogen concentration and concluded with testing at 20% hydrogen. New safety requirements were established with regards to how hydrogen was managed on site. No changes to the drilling program were made in order to validate performance using real-world conditions. The hydrogen was blended into compressed natural gas (CNG) on-site, which provided flexibility to adjust the properties of the fuel mix.
The demonstration successfully proved that the existing dual-fuel engines can support a blend of up to 20% hydrogen without requiring any additional engine development, adjustments to the rig's control system, or hardware modifications. Transient re sponse of the engine was not compromised during load steps, load sheds, or gas shutoffs, even with the maximum hydrogen concentration. Aggressive engine substitution rates were maintained and a negligible change in engine efficiency was observed. Reductions in greenhouse gas (GHG) emissions were observed to be directly correlated to the concentration of hydrogen in the fuel mixture. Although existing infrastructure needs to grow before hydrogen becomes an economically viable fuel for drilling applications, significant investment in the hydrogen industry indicates a future where hydrogen is a widely adopted source of energy. This demonstration represents an important first step to prepare for the hydrogen economy of the future.
The Implementation of Safety-II: Learning from Normal Work in Practice
Authors: Koester Thomas 
Published: February 2024

Abstract
The oil and gas industry has for decades learned about safety from the analysis of failure, adverse events, incidents, and accidents. This approach is called "safety-I". Safety-I has helped the industry reach a plateau of general high levels safety, measured as the absence of incidents, compared to other industries, but there is still room for further and continuous improvements. Researchers in human factors and safety have suggested that safety critical industries, in the future, should not only learn from failure, but also from the normal everyday successful work. This approach is called "safety-II" (Hollnagel et al. 2015), and, in a report from The International Association of Oil & Gas Producers (2022), "learning from normal work". The paper describes the practical implementation and roll-out of safety-II and learning from normal work on board operating drilling rigs.
The learning from normal work project covers 3 phases:
Phase 1: The onboard studies of positive practice, i.e., ethnographic observations of normal daily work and conversations with crew members, e.g., on the drill floor. Phase 2: The development and prototype testing of learning material based on the onboard observations and training of onboard facilitators in the learning team format. Phase 3: The final validation of the learning from normal work concept in a ramp-up case study on a rig starting a new drilling campaign.
More than 160 examples of positive practice from 5 different rigs (2 drillships, 2 jackups and 1 semisubmersibles) were collected in the first phase of the learning from normal work project. The examples illustrate how crews add safety to the process by, e.g., the way they communicate and interact, the way they use technology or the way they plan and discuss their work. The positive examples were filtered and used to generate learning material in the form of learning cards for interactive discussions of safety in learning teams and positive examples to be used in safety meetings and presentations for inspiration as an alternative to failure and incident cases.
One important conclusion from the learning from normal work project is that the crews contribute to the safety of the process through their behavior and small actions in the daily routine work. Thereby, they put many small "safety nets" in place in the system making it resilient and strong.
Looking at positive practice and positive events is new when discussing safety. It changes the narrative and the focus of the discussion from "what to avoid" to "what to do more" and "what to develop and support". This philosophy is based on positive reinforcement as a motivating and driving factor.
Flexible Self-Repairing System: A permanent Isolation Solution in The Gulf of Mexico
Authors: Melo N. , Cardozo J. , Rivera M. Munoz , Antunes Arthur , Taranto Richelle 
Published: February 2024

Abstract
The Gulf of Mexico plays a pivotal role in the United States’ energy landscape, serving as the primary offshore source of oil and gas. It contributes approximately 97% of all oil and gas production on the US Outer Continental Shelf (OCS). Within this dynamic offshore environment, numerous major operators manage a variety of platforms and systems. Notably, a longstanding pioneer in the Gulf region has been at the forefront of technological advancements in the design, construction, and operation of world-class oil and gas production facilities for over four decades. The prominence of being one of the Gulf’s key operators comes with both advantages and challenges that must be addressed as part of the daily operational landscape. Historically, this operator has adhered to its established best practices and remained loyal to proven strategies, which can pose challenges when introducing new products or technologies.
In May 2021, a situation arose when a client contacted a multinational service company, seeking assistance with a well in the production phase located adjacent to the Mississippi Canyon. A case of Sustained Casing Pressure (SCP) emerged in the B-Annulus, necessitating a resolution within an extremely tight timeframe of just 2-3 weeks. In response to this critical situation, our organization was tasked with delivering a solution to remediate the well afflicted by SCP and hydrocarbon (HC) flow issues. Within our extensive technology portfolio, a Flexible Self-Healing Cement (FSHC) system was identified as the ideal solution to ensure well integrity from drilling through abandonment, providing a reliable annular pressure seal and safeguarding against hydrocarbon leaks and SCP at the wellhead. Tailored to meet the client’s unique challenges and specifications, this system was deemed the most suitable choice for the operation. This paper delves into the comprehensive development, execution, evaluation, and post-job analysis of the inaugural application of the Flexible Self-Healing Cement system in the Gulf of Mexico. This innovative system promises to address the pressing challenges posed by SCP and HC flow, further enhancing the operational efficiency and well integrity in this strategically vital region.
Improving Weight-On-Bit and Differential Pressure Measurements with Connection Automation Systems and Pattern Recognition
Authors: Kaya M. , Lanzet B. , Christopher C. , Groh A. , Poludasu S. , Dunbar D. 
Published: February 2024

Abstract
Measuring the values of weight on bit (WOB) and differential pressure (DiffP) through accurate taring is critical for ensuring downhole tool health and creating accurate parameter roadmaps for drilling optimization. Current industry standards for taring rely on the driller to manually select a particular time when these values are zero, which is often imprecise and/or inaccurate. This paper will detail the journey this drilling contractor made to automate the taring process as part of a broader connection automation initiative, how initial solutions challenged organizational assumptions on downhole behaviors, and how a pattern recognition algorithm allowed for better tare consistency and accuracy over existing field procedures.
An initial process automation procedure was designed and implemented in the rig control system (RCS) to manage the connection process, starting with coming out of slips and ending with tagging bottom. As part of this process, the system included a step to consistently tare when the drill bit is one foot off bottom and descending at a constant rate. During field trials, this method gave encouraging results over previous manual tares. Though the result did show improvement over the traditional manual taring method, discrepancies in the relationship between WOB and DiffP eventually led to an improved method of taring which utilizes a novel pattern recognition algorithm. This algorithm detects when the bit tags bottom and establishes a reference hookload (HKLD) at that point. A pattern recognition algorithm was included into the automation sequence replacing the step to tare one foot from bottom.
The initial process automation routine provided consistency to the taring process which was observed during testing. The improvement in consistency revealed inconsistency in WOB tares which was supported by variance in DiffP when WOB was constant. A pattern recognition method was developed to identify when tagging bottom. This algorithm was applied to historical data providing evidence of its benefit to WOB tares. The algorithm was included in an application and then adapted and applied to DiffP tares. The application was run over a historical dataset to qualify its viability. After proving viability, it was exchanged with the taring step in the initial process automation routine and run on live wells. Taring using the pattern-recognition based application presented better results than the manual and initial automation taring methods. The overall operational impacts of each method will be discussed in further detail.
The results of this project challenge underlying assumptions about how taring should be performed and improves overall understanding of the impact of different automation routines. The pattern recognition method discussed in this paper constitutes a novel application which utilizes new technology to improve existing processes.
Mechanical Specific Energy (MSE): Claims and Implications – Facts, Fallacies, and Pitfalls (Part-2)
Authors: Mensa-Wilmot Graham , Samuel Robello 
Published: February 2024

Abstract
Mechanical Specific Energy (MSE) is used extensively in the drilling industry for numerous purposes, most commonly as a strategy for drilling optimization. Additionally, MSE is used for determination of rock hardness, development and assessment of drilling parameters roadmaps, benchmarking, and drilling efficiency calibrations. MSE, in addition to being used for identification and remediation of drilling dysfunctions, is also utilized in completions design optimization for unconventionals. These applications of MSE are important in all drilling operations, regardless of asset class, from exploration through development.
MSE deployment is grounded in several strongly held positions, with associated implications. In Part-1 (SPE 212508) of this critical conversation, which attracted intense industry debate, it was revealed that most MSE claims lack factual validations, and that the assertions had numerous fallacies and pitfalls. These findings, which established limitations and inefficiencies in MSE deployment, are critical for the industry. Most importantly, awareness of MSE shortfalls will ensure development of effective, and verifiable solutions for drilling efficiency improvement. MSE initiatives and claims must be held true, supported by operational objectives, and ratified by physics and drilling mechanics principles.
This follow-up paper identifies additional MSE deficits, with primary focus on its claims. These assertions, as will be discussed include the following – performance benchmarking, drilling efficiency quantification and improvement, identification of performance risks and limiters, recognition, and remediation of drilling dysfunctions – regardless of asset class and/or application type. Fallacies and pitfalls associated with these claims will be identified and analyzed, through appropriate physics-based processes, where the erroneous implications of these claims, will be identified and discussed. In addition to supporting these positions with appropriate field data, a new systematic "Five-Step" process (AIVReC) for discussing dysfunctions will be introduced. It is worth noting that this paper’s positions promote development of appropriate workflows for data analytics and drilling automation processes. These needs cannot be achieved with current MSE positions and claims, because of the numerous fallacies and pitfalls.
A Novel Artificial Intelligence-Based Cement Slurry Design Recommendation System
Authors: Sistla Sai Shravani , Licht Justin , Sadiq Nauman , Drews Tim , Wilberg Dean , Coquilleau Laurent , Manoharan Monisha , Arevalo Yezid , Makarychev-Mikhailov Sergey 
Published: February 2024

Abstract
Cementing is a critical stage in the well construction process, providing zonal isolation, support, and protection for casing. Cement slurry design is a key element in cementing and represents a complex chemical process. Designing slurries with numerous chemical properties is challenging as it must account for often-contradictory requirements. Conventional slurry design relies heavily on using traditional search methods, based on applying numerous filters containing slurry composition, test conditions, additives, etc. This approach is highly ineffective, tedious, and time consuming, and can lead to suboptimal results, because of an engineer’s subjective experience and biases. In this paper we present an artificial intelligence-(AI-)based cement slurry design recommendation system that recommends the most relevant slurry designs based on slurry composition, well conditions, and laboratory test results. The original database contains ~230,000 formulations and ~2 million tests completed since 2018. These data are cleaned, preprocessed, and fed into the recommendation system, which is built in two steps. First, vectorization of all historical slurry records is performed with consistent feature engineering. Key slurry features include slurry composition, well conditions, and laboratory test results. Second, these vectors are used to compute similarity metrics between slurry records applying AI-based algorithms, such as clustering, exact, and approximate nearest-neighbor methods. In the inference phase, the system uses the computed similarity metrics to recommend the most relevant slurries for a set of design requirements. The AI-based system is data-driven and objectively recommends the most relevant slurries for a set of design requirements, from the database. A comparative study of the tested AI algorithms and their corresponding outcomes is presented. Specific evaluation metrics are proposed to evaluate the recommendation results. The recommended slurries are visualized in both tabular and graphical forms, for user-friendly analysis. In addition, several examples are provided that demonstrate how this innovative approach improves the slurry design methodology. The proposed approach enables retrieval of the relevant slurry candidates from a huge database in seconds, compared to performing manual search and analysis, which can take hours. The recommended slurries form a solid foundation for later stages of the slurry design process. Furthermore, smart selection of slurries saves many hours of expensive laboratory testing.
Achieving ISCWSA MWD+IFR1 Tool Codes Without Airborne Magnetic Surveys
Authors: Paynter Ryan , Paré Andrew , Crookston Glenna , Berarducci Alec 
Published: February 2024

Abstract
Accurate wellbore placement and collision avoidance are increasingly important in today's tightly spaced horizontal wells and congested fields. Modern well plans incorporate hydraulic fracture geometry, reservoir characteristics, and other factors to maximize reservoir extraction while minimizing drilling costs and safety risks. Directional surveys that have minimal uncertainty are crucial to drilling the optimized well plan. This work enables more accurate directional surveys and wellbore placement through site-specific high accuracy magnetic models.
Under the Industry Steering Committee on Wellbore Survey Accuracy (ISCWSA) error model, the use of In-Field geomagnetic Referencing (IFR) dramatically reduces some of the largest sources of error in wellbore location. Unfortunately, IFR can be expensive or out of reach for smaller or less mature basins because of the high cost of flying or purchasing high-resolution aeromagnetic surveys. An economic and timely alternative has been developed, strategically taking ground magnetic measurements at sites along the wellbore(s) with absolute instruments, combined with a high-definition global magnetic model to produce IFR at the pad scale.
This method was tested in the Denver-Julesburg Basin in Colorado. A small survey team collected ground magnetic measurements over a typical pad design in one day. The data was then processed and combined with a high-definition global magnetic model to produce IFR corrections locally for the pad. This study demonstrates that the procedure reduces the horizontal wellbore position uncertainty by 46% for the study area and more generally from 15 to 50 percent when compared to standard MWD (Measurement While Drilling) survey technology. Furthermore, this method was compared to a typical IFR model created from airborne measurements. We found that the results from the comparison agreed within tolerance to the IFR model and validated the use for IFR corrections at a pad scale. This technique improves confidence in wellbore placement, mitigates collision risks, improves reservoir drainage, and helps to maximize production.
Much work on the construction of IFR magnetic models from airborne total field data for large areas has been done. This work is novel in that it describes the construction of an IFR magnetic model from a small number of ground magnetic vector measurements, in combination with a high-definition global magnetic model for a small area. This innovative approach makes IFR corrections significantly more accessible to drilling programs with shorter timelines, fewer wells, and smaller budgets.
Comparing Drilling Anomaly Prediction by Purely Data-Driven and Hybrid Analysis Methods - Case Study of Utah FORGE Geothermal Wells
Authors: Montes Abraham C. , Ashok Pradeepkumar , van Oort Eric 
Published: February 2024

Abstract
Automatic prediction of drilling incidents can be conducted through either a purely data-driven approach or a hybrid approach. In the first approach, the variable space is typically limited to surface measurements and downhole sensor data, while in the second approach, the variable space is expanded to include information from physics-based models. This paper analyzes the additional value of incorporating physics-based information to predict drilling incidents such as stuck pipe, illustrated using data from the Utah FORGE geothermal wells.
In our study, we trained three anomaly detection models with two distinct variables spaces. In the first one, we considered the real-time signals only, while in the second one, we included physics-based information derived from cuttings-transport, tortuosity, and torque-and-drag models. We selected three models that showed promising results in recent studies and represent the taxonomy of machine-learning-based anomaly detection algorithms. Specifically, we utilized recurrent neural networks, autoencoders, and clustering. Finally, a comparison between the two approaches was performed in terms of the fidelity of the warnings they generated.
We observed that the inclusion of physics-based information is key to improving the performance of models for predicting drilling incidents. Specifically, we noted a reduction in the number of false alarms, which, in turn, increases the reliability of the models. In addition, we found that physics information can guide the selection of prediction time windows when drilling anomalies develop, thereby eliminating bias in the models' construction. Finally, we observed that some drilling anomalies, which were previously believed to occur suddenly with little warning, can, in fact, be predicted in a timely manner with hybrid models. These observations demonstrate that the use of hybrid models can significantly increase the performance of drilling anomaly predictions, providing sufficient forewarning time for their prevention and associated NPT avoidance.
State-of-the-art methods that implement purely data-driven and hybrid approaches have individually demonstrated high accuracy in predicting incidents on specific datasets. However, no previous comparative study has been conducted to analyze the value of incorporating physics-based information. This paper is the first to perform such an analysis for models aiming at the early detection of drilling anomalies. The results from this study provide valuable guidance for future NPT avoidance in drilling operations.