bluuebunny/crossref_metadata_2025_split huggingface dataset:
Combining Downhole Axial and Surface Oscillation Tools, What Are the Consequences on Tool Face Control Performance?
Authors: Mahjoub Mohamed , Dao Ngoc-Ha , Nguyen Khac-Long , Menand Stéphane 
Published: March 2022

Abstract
When drilling in sliding mode, axial oscillation tools (AOT) and surface oscillation tools (SOT) are two possible solutions that are used in the industry to overcome the friction forces, especially in unconventional well trajectories with a long lateral section. The AOT is assembled in the drillstring and run in hole such that it is generally placed in the middle of the lateral section. It is actuated by the mud flow to produce high-frequency axial vibrations. As for the SOT, it is placed at surface and produces low-frequency torsional oscillations in the drillstring by alternating the rotation direction. For some challenging trajectories, the idea of combining both tools, where the SOT reduces the friction on the top part of the trajectory and the AOT deals with the bottom part, seems appealing. However, when coupling the two types of oscillations, one should be careful to the possible implications on tool face control performance.
In this paper, time-domain dynamics modeling is used to investigate the AOT and SOT behavior, whether they are employed separately or together. When simulated separately, the tools' influence zones can be identified, and their operating parameters can be tuned accordingly. When combining both tools, if their influence zones are overlapping, coupled axial and torsional oscillations occur and could have consequences on the tool face orientation (TFO). The observations obtained from simulations are confronted to data coming from a well drilled with both tools.
Numerical simulations show that the AOT helps the propagation of the torsional motion downhole, because it takes a part of the friction that opposes the torsional oscillations. If the SOT and AOT parameters are jointly well calibrated, then the steering from surface becomes faster, and the tool face control becomes more accurate. However, if the SOT's parameters are calibrated without regard to the AOT influence, then TFO variations could be obtained, indicating unstable tool face control leading to a tortuous well bore.
The use of time-domain modeling to jointly characterize the behavior of downhole axial and surface oscillation tools introduced in this work allows a better understanding of the coupling between the two tools. This work provides an opportunity to increase the extent of the lateral sections without impairing the wellbore quality.
IADC Code Upgrade: Data Collection and Workflow Required to Conduct Bit Forensics and Create Effective Changes in Practices or Design
Authors: Watson William , Dupriest Fred , Witt-Doerring Ysabel , Sugiura Junichi , Pastusek Paul , Daechsel Dustin , Abbas Raafat , Shackleton David 
Published: March 2022

Abstract
This paper introduces a forensic workflow that may be used to link drill bit and bottom hole assembly (BHA) damage to drilling dysfunction. This paper will also discuss the data that should be collected and how it should be processed to enable the operational practices and engineering design changes to address these issues.
There is a vast amount of data collected in all drilling operations that can be used to improve performance if utilized within an effective forensic workflow. Several drilling forensic case studies were developed and critically reviewed by subject matter experts from across the industry. From the causal analysis for each case study an assessment was performed on what information was (A) available, (B) required to diagnose the cause and (C) not available but would have been preferred. The way the team communicated and acted on the data was also documented.
By combining the learnings from these case studies, it was observed that a guided approach can improve data collection and lead to a more consistent, accurate, timely causal analysis with appropriate remedial actions. The process discussed within has been refined to support data collection for forensic analysis and provides a reference for field and office-based drilling professionals. These practical guidelines have been developed to offer a foundation for a drilling forensic data collection methodology as well as training for the industry - they have been created such that they can grow organically and, in the future, can be used for developing subsequent industry publications.
The work described in this paper is part of a joint International Association of Drilling Contactors (IADC) / Society of Petroleum Engineers (SPE) industry effort to revise the IADC dull grade manual.
Development of Automatic Recognition and Recording System for Rig Jobs
Authors: Ge Yunhua , zhang Yanlong , Ge Su , Mei Yunyi , Wang Wenzhi , Yong Fengwei , He David , Zhang Josh , Wang Zhenyu , Wang Yuxin 
Published: March 2022

Abstract
Keeping a record of drilling process automatically in real time is a requisite for KPI studies and getting support from ROCs experts. There are five categories of technical data describing a well's drilling process, which are rig jobs in time sequence, job operational parameters, unit lithology by depth, wellbore configuration by depth and job appraisal respectively. Among them, the data of rig jobs in time sequence covers whole process of well construction and is collected manually via Daily Drilling Reports or Shift forms with little data quality control and is easy to be filled by mistakes or omissions. The other four types of data are mainly collected via mud logger, MWD/LWD tools, wire-line logging tools, etc and mostly realized automatic recording with strict data quality controls. In order to solve the shortcomings of data collecting and recording of rig jobs in time sequence, an automatic recognition and recording system for rig jobs is developed.
Previously, the team had tried to identify 25 common rig jobs such as on-bottom drilling, tripping-in, tripping-out, circulating, reaming, etc by developing a judgment algorithm based on real-time operating parameters from mud logger and is proved to be inaccuracy and incompleteness due to insufficient data resolutions and limited data points especially when drill string is out of hole. This time, the team takes advantages of video data from on-site surveillance cameras and develops an automatic recognition and recording system for rig jobs by integration of AI video recognition algorithm with previous method. The AI video recognition is realized by applying U-Net, Attentioned, GAN, etc. and the AI model trained by a mass of labeled data. Finally, the system is deployed in sync with Wellsite Data Platform (WDP) and tested on line with real-time data from both cameras and mud logger.
The online tests showed that the system could recognize various rig jobs from spud-in to end of well with correctness rate of more than 97% and improved the correctness rate of previous method by more than 20%. In May to November of 2020, the system was first deployed in sync with WDP in a deep exploration well GQ-6. The video and mud logging data of its six drilled offset wells were loaded, replayed, and the rig jobs in time sequence of each well were recognized and recorded in the WDP database. With share of the accurate rig jobs data, the ROC experts could easily find out the causes of NPT and ILT and optimize drilling operations to realize the well GQ-6 the fastest well in the block The system also save much of manual work for data record of rig jobs.
Enhanced Method for Dimensional Measurement of Oilfield Tubulars to Optimize Operations and Minimize Risk Factors
Authors: Rodriguez Andres , Logan Kevin , Nickles James , Carlson Greg 
Published: March 2022

Abstract
Technological advances in the nondestructive evaluation of Oil Country Tubular Goods (OCTG) have brought about enhanced methods to minimize risk factors associated with the drilling or transport of oil and natural gas. This paper focuses on a multidimensional, nondestructive testing (NDT) inspection and measurement package combining wall thickness and outer diameter (OD) measurement data into a single inspection report per tubular. This method of dimensional measurement is performed on the front end of a project, providing end users with numerous benefits from a cost and time perspective, as well as the critical data needed to optimize operations.
The package provides high-resolution inspection data for the manufacturer or end user, yielding higher precision burst and collapse strength predictions than previously available. Other benefits of this method include optimization of material placement in casing string design, estimation of cementing fluid volume, and downhole tool clearances among other applications. Confidence and safety levels of burst and collapse ratings are of particular interest to drilling engineers.
The wall thickness variations and tubular eccentricity are measured using an automated, nondestructive ultrasonic testing system, utilizing compression wave transducers. The OD measurement is acquired using an automated, nondestructive laser-based testing system. Laser-based testing uses two laser-scan micrometers positioned horizontal to the tubular and 180° apart (Fig. 1). Each laser measures the distance between the tubular and the receiver. These two measurements are combined to calculate the tubular OD.
The level of accuracy provided by this technology continues to push the industry forward with higher quality tubulars ranging from the mills to end users.
Drilling Parameter Optimization in Real-Time
Authors: Oedegaard Sven Inge , Helgeland Stig , Mayani Maryam Gholami , Trebler Andrey , Bjorkevoll Knut Steinar , Skogestad Jan Ole , Lien Morten , Weltzin Tore , Rudshaug Bjørn 
Published: March 2022

Abstract
The objective of this paper is to demonstrate how drilling parameter optimization in real-time provides a drilling team with an Edge-system that can continuously improve performance and avoid problems without the need for subject-matter experts.
An Edge-system based on cloud technology with Model based reasoning in Artificial Intelligence (AI) is made to give real-time and forward advice for operational parameters, see (Lahlou et al, 2021) for description. The key enabler for such system is "automatic" auto-calibration of models to be used for multiple forward-looking and what-if to find optimal drilling parameters within the well envelope ahead. A simplified configuration has been made so that the rig-team can operate and maintain the system without the need for subject matter experts. "Automatic" Auto-calibration at stable conditions and/or during ramping conditions removes the need for such experts.
Results from testing of the Edge-system on multiple wells from several operators will be presented both related to automatic auto-calibration of real-time prediction models and for optimization of drilling parameters. As expected, a major challenge has been to design a calibration algorithm that improves accuracy of calculations without being kicked out by any data quality issues, and without masking upcoming actual anomalies like kicks, losses and issues related to hole cleaning. This challenge has been approached by using a combination of time-delayed robust calibration methods and testing on a comprehensive set of data from diverse operations.
DDNet: A Multi-Agent Decision Making and Evaluation in Drilling with Looking-Ahead Simulation
Authors: Yu Yingwei , Jeong Cheolkyun , Chen Wei , Shen Yuelin , Vesselinov Velizar , Chambon Sylvain 
Published: March 2022

Abstract
Operators drilling a directional well may suffer the uncertainties from the downhole environment, the equipment, and the human decision-making process. We proposed a method to evaluate steering decisions of multiple reinforcement learning agents by calculating the estimated total future reward (ETR) through a validation agent using a simulator. The final decision would be chosen as the one with the maximum total reward among the multi-agent decisions. The multi-agent approach stabilizes the final decision-making for directional drilling and results in a higher success rate and better overall steering performance.
In our previous work (Yu et. al, 2021), a single agent was trained to make decisions for directional drilling through the deep reinforcement learning framework. To improve the stability of the decisions, we extend the single-agent approach to this multi-agent framework including a working environment and a validation environment. The system includes a working environment that includes a pool of working agents. The working environment represents the current physical drilling environment (e.g., rig state, geology, bit location, and planned trajectory). In addition, a validation agent is working in a validation environment using a hole propagation simulator for projecting the ETR.
When the system is in action, the working agents receive inputs such as surveys and planned information in the working environment and each of them makes their own decisions. Their decisions, as action proposals, are passed to the validation agent while the validation environment synchronizes the state from the working environment. The validation agent then carries out each action proposal in the virtual environment through the simulator and continues drilling to the target multiple times given a variety of formation priors. An average score of ETRs is then calculated for each action proposal. As a result, the action proposal with the maximum average score wins the competition and goes back to the working environment for execution.
The multi-agent framework could optimize and stabilize the sequence of decisions while minimizing uncertainties. Our validation results demonstrate that the total rewards were successfully improved than the single-agent decision-making.
Quantification of Our Carbon Footprint While Drilling
Authors: Johnson Ashley , Mäkinen Anna , Fahim Syed , Arevalo Yezid 
Published: March 2022

Abstract
Reducing our operating carbon dioxide emissions is a critical step in mitigating the impact of our well construction operations. To demonstrate a quantifiable benefit, we need a starting point. We need to measure our current emissions and identify the main drivers of the footprint. From there, we can identify best practices to reduce environmental impact and quantify how changing our drilling systems can lower CO2 generation.
Analyzing the data available from two modern land rigs we have measured the CO2 released for drilling different wells in different basins. We have also segmented the emissions by the rig systems using the power they consume. We show that the mud pumps are the biggest culprit for the CO2 release. The top drive draws less power; and while the draw works have a very high-power capacity, they only draw this for a very short period so overall their footprint is much less significant.
In order to make this work relevant for more than a small number of high specification modern rigs, we have built and validated an emissions model, thanks to which, we can calculate the CO2 released from measured drilling parameters. As such, we can generate, in real time, a carbon emissions log quantifying the footprint and the contributions of the major systems on any rig where we have access to these surface data.
Using the same model, integrated with our well planning processes, we can accurately predict the footprint from a well construction scenario and quantify the benefits which changes to the BHA, the drilling practices or the well design would bring. In the same manner, based solely on the drilling data, the comparison of the environmental impact of all of our drilling operations with a high level of granularity is enabled. With data, which is already available, this lets us identify opportunities to reduce emissions and less efficient operations rapidly at a global scale.
Applying Digitalization for Energy Efficiency Insight and Advancing It Towards Carbon Emission Reduction Targets
Authors: Puskarskij Konstantin , Loftager Rune , Hoffner Jan , Haastrup Peter , Kugendran Sajith , Moody James 
Published: March 2022

Abstract
This paper details the successful development and application of digital solutions, providing insight into fuel and energy consumption onboard offshore drilling rigs. Technical details behind the solutions are outlined in the paper, as well as how they can be applied to pursue industry sustainability goals by reducing the environmental footprint from offshore drilling operations.
Different methods for providing the insight were applied; utilizing a precise measurement of fuel consumption or, alternatively, using diesel generators’ performance, and advanced data analytics to determine the energy consumption of offshore drilling rig equipment and systems. Where feasible, one of these two methods has successfully been rolled out on offshore drilling rigs and confirmed to deliver very close results with high accuracy.
Such information is used to provide fleet-wide monitoring of fuel consumption, Green House Gas (GHG) emissions, and how it is compared against a planned baseline. In addition, such a tool provides insight that helps to identify areas with potential for improvements, and to support the right conversations offshore, based on drilling rig-specific data. This highlights where offshore rig crew, individually, can contribute to improving energy efficiency and take the first step towards implementing an increased awareness and thus reducing the overall environmental footprint related to drilling operations.
This paper also provides technical details on how sensor data is being processed in the cloud, where it is managed, quality checked and visualized to guide on- and offshore decision-makers.
It further discusses, through real examples, how important knowledge can be derived through insight and transparency to optimize the operation and behavioral performance, achieving significant decarbonization of the offshore drilling operation, including optimized operation of the rig power plant. Finally, the solution unlocks possibilities for qualified conversations on best practices while benchmarking across a fleet of drilling rigs.
Design and Field Deployment of a Torsional Vibration Damper
Authors: Hohl Andreas , Kulke Vincent , Ostermeyer Georg-Peter , Kueck Armin , Peters Volker , Reckmann Hanno 
Published: March 2022

Abstract
Different vibration phenomena and vibration dysfunctions are excited by the drilling process. The associated dynamic loads result in downhole tool failures and inefficient drilling if not properly managed. Vibration mitigation strategies based on adjustment of operational parameters often compromise the rate of penetration (ROP). The purpose of downhole tools for vibration mitigation is to enlarge the envelope of drilling parameters with low levels of vibrations. The associated more aggressive drilling parameters enable a higher ROP. A torsional vibration damper that is designed to mitigate high-frequency torsional oscillations (HFTO) is introduced in this work along with the results of first field deployments.
A damping-threshold needs to be exceeded by a damper to mitigate self-excited HFTO. The damping threshold is defined through the energy input resulting from the bit-rock interaction and depends on the instantaneous operational parameters. The design space to provide damping, however, is limited. Thus design and dimensioning of the torsional vibration damper is optimized based on theoretical insights, downhole vibration data analysis, modeling and simulations, and laboratory testing at different scale. This process is presented in the paper.
The placement of the damper elements within the drilling system has been identified as a critical task to effectively dampen the critical modes related to HFTO. The simulated results for the BHA that is deployed in the field are presented. In a case study the first field deployments of the torsional vibration damper are discussed. The results of the damper deployments and a comparison to offset well data prove that the damper effectively mitigates HFTO. Snapshots of high-speed vibration data sampled with 1000 Hz are analyzed and related to the modeling results. The suppression of HFTO enabled the application of more aggressive operational parameters and led to an increase in the ROP compared to benchmark runs. First trends prove a high reliability of the BHA with torsional vibration damper tool.
The torsional vibration damper has been successfully introduced in the field and has proven to effectively suppress HFTO. A holistic methodology has been developed that allows for an application specific optimization of the damper deployment. The torsional vibration damper enables a higher rate of penetration at low levels of vibrations and an increased reliability of the tools deployed and, ultimately, decreases the well delivery cost significantly.
Automated Merging of Time Series and Textual Operations Data to Extract Technical Limiter Re-Design Recommendations
Authors: Yi Michael , Ashok Pradeepkumar , Ramos Dawson , Bohlander Spencer , Thetford Taylor , Shahri Mojtaba , Noworyta Mickey , Peroyea Trey , Behounek Michael 
Published: March 2022

Abstract
During the construction planning phase of any new well, drilling engineers often look at offset well data to identify information that could be used to drill the new well more efficiently. This is generally a time-consuming process. The objective was to develop a recommender system that would automate the process of identifying potential hazards and current technical limiters.
The developed methodology consisted of three parts. First, a system is developed that can parse textual information found in daily reports to identify key events that occurred in offset wells. Second, time series data from these same offset wells is processed to identify events directly from the patterns in the data, and a reconciliation is done between the time-series data and the contextual data wherever there is a conflict between the two datasets. Finally, KPIs are computed that enable the comparison of various drilling choices and their consequences across the set of offset wells and recommendation are automatically generated for improvements in the construction of a new well.
The system was developed on a set of 7 recently drilled wells chosen from a specific North American land operation. The recommendations were compared to recommendation made through manual processing of the data for validation of the approach. The recommender identified invisible lost time (ILT) and potential non-productive time (NPT) scenarios, optimal depth-based drilling parameter for the new well, and recommendations on BHA and flat time improvement areas. Open-source natural language processing libraries were used in this project and were very effective in extracting events from textual data.
An automated system was built to guide the drilling engineers in the planning phase of a well construction activity. Given a set of offset wells, the system combined both the time series data and textual data to arrive at these recommendations. The approach upon further refinement is expected to save 30 to 40 hours of the engineer's time per well and shorten the learning curve.