bluuebunny/crossref_metadata_2025_split huggingface dataset:
Successful Execution of a Dynamic Driller's Roadmap Using an Automated Formation Top Detection Algorithm
Authors: McManus Brian , Wells Tony , Willerth Marc 
Published: March 2022

Abstract
Efficient drilling through heterogenous geology requires careful matching of drilling parameters to the rock being drilled. Failure to do so can lead to increased bit damage or poor drilling performance. The "driller's roadmap" is a list of such parameters to be changed at various depths where formation tops are expected to be crossed. Successful execution of these roadmaps relies on having a detailed geological prognosis of where specific formations will be encountered. When drilling the first well on a pad, the precise depths of geological boundaries may be unknown preventing successful roadmap execution. By providing a feedback loop between a driller's roadmap and an automated formation top detection algorithm, it is possible to identify and proactively respond to geology even when drilling in these uncertain conditions.
A roadmap was created with target drilling parameters for each formation that would be encountered when drilling. Critical formations were identified where there was a high degree of interfacial severity, suggesting a high potential for bit damage if cutter overloading occurs. To mitigate this, a transition to controlled depth-of-cut drilling would be made prior to formation entry. Gamma markers were identified for these formations, along with predictive gamma markers shallower than the transition zones. During drilling, an automated formation top detection algorithm was run on logging-while-drilling data providing a real-time alert when the predictive markers were detected. Remaining roadmap depths were updated based on whether these markers were shallower or deeper than the roadmap expectation. Gamma logs and drilling mechanics were monitored to confirm the depth where transition zones were encountered. This was repeated across the pad to confirm process reliability.
Two formation tops were identified that were expected to come in 450 and 170 feet shallower than the transition zone where the switch to depth-of-cut drilling would occur. When drilling the first well on the pad the automated formation top detection algorithm identified these tops as arriving nearly 100 feet shallower than anticipated by the pre-drill geology program. The roadmap was updated and the switch to depth-of-cut drilling was performed 100 feet earlier than originally planned. Post-well analysis showed the transition zone came in 95 feet shallower than expected, in line with the modified driller's roadmap, suggesting the modifications avoided unnecessary bit damage. Subsequent wells on the pad showed strong repeatability in formation top arrivals, and the updated roadmap was used without major modification.
The combination of driller's roadmaps and automated formation top detection is a key step toward efficient drilling/geology integration with an eye toward optimized drilling automation. This study shows this integration can lead to positive drilling outcomes even in areas with high levels of uncertainty in the location of formation boundaries.
Autonomous Directional Drilling Using the RSS Simulator for the Drillbotics 2020-2021 Virtual Competition
Authors: Saavedra Jerez Luis , Haque Md Fazlul , Magalhaes Melissa , Chevuri Rishyank , Sui Dan , Florence Fred 
Published: March 2022

Abstract
The automation of directional drilling is a recent development that is expanding with both physical and virtual concepts. In the same way, SPE's Drilling Systems Automation Technical Section (DSATS) supports the creation of new automated directional drilling models and ideas with its international competition Drillbotics®. This paper describes the design, algorithms, graphical user interface (GUI), and features of the rotary steerable system (RSS) Simulator V2.4, developed by the winning team of the 2020-2021 Drillbotics Virtual Rig Competition.
The RSS Simulator, developed in Python, simulates a drilling trajectory of a directional well based on target coordinates, dogleg severity (DLS) constraints, and cubic Bezier curves. The simulator has two general modules: the RSS Model and the Trajectory Control Optimizer (TCO). The first one simulates the behavior of the bit and bottom hole assembly (BHA) tool (e.g., force on the bit, inclination, azimuth) considering the uncertainties at survey stations using the RSS Model developed by the University of Stavanger. The TCO autonomously creates the well plan, controls the bit's deviation, and corrects the trajectory when it has diverged from the planned trajectory.
The simulator can drill either 2D or 3D trajectories. In the case study, one 3D trajectory example provided by the Drillbotics committee is illustrated. The results obtained show differences among the original (given), the planned (optimal, before drilling), and the simulated (during drilling) trajectories. Moreover, the comparison between the simulation with/without the TCO module reveals the necessity and importance of autonomous and real-time trajectory control. Besides, the BHA tool activation (due to the autonomous control of the simulator) is perceived in the simulation's results, which influences the rest of the parameters like the DLS, the force acting over the bit, and the rate of penetration (ROP) in the axial axis, inclination axis, and azimuth axis calculated with the RSS Model.
The work presents a novel way to predict the bit position and control the trajectory, considering bit position uncertainties, drilling parameters uncertainties, DLS, and tortuosity calculations in an automated way. It will help users to develop further automated algorithms and mathematical models for reaching autonomous directional drilling in the future.
Casing Wear Tests for Precise Wear Factor Evaluation
Authors: Osman Omer Alnoor , Merah Necar , Samuel Robello , Alshalan Meshari , Alshaarawi Amjad 
Published: March 2022

Abstract
The increasing complexities of wellbore geometry imply an increasing potential of damage resulting from downhole casing wear. The present method of using old wear factors results in unreliable predictions. The main objective of the present work is to develop new casing wear factors for drillpipes and casings to improve the accuracy of wear prediction. The paper focuses on design, manufacturing, control and measurements of important parameters contributing to casing wear as well as wear volume evaluation.
A new casing wear testing facility is designed and built by repurposing and automating an old lathe machine. Real drillpipes with tool joints and casing sections made of different materials are used. The lathe spindle controls the mandrill rotational speed on which the DP is tightly mounted. The versatile casing section holder is fixed on a sliding system comprising a dynamometer and a step-motor with a microcontroller allowing for the control and measurement of the side loadings and lateral displacements. A slurry fluid pump system is designed to feed water-based or oil-based muds at the contact surfaces.
The as-received drillpipes and casing materials were analyzed by microscope and optical emission spectrometry (OES) to determine their microstructure and chemical composition. The hardness of both casing and DP materials was also measured. The lathe main spindle was able to provide stable rotating speeds varying from 100 to 1000 rpm. Though the irregular tool joint hard-facing surface was machined to allow for better control of the side loadings, the measured radial load shows variations of +/− 15%. The designed pumping system was able to provide a continuous stream of water-based and oil-based mud, at the contact surface, during testing. The wear depth is measured both directly using a 3D optical profilometer and indirectly by the lateral sliding displacement, thus allowing for the establishment of a calibration curve that can be used for in-situ measurement of the wear depth and wear volume evaluation. The initial set of tests has shown that the designed system is performing satisfactorily. More tests are being performed to confirm the robustness of the design.
Prediction of Stuck Pipe Incidents Using Models Powered by Deep Learning and Machine Learning
Authors: Mal Anwesha , Ødegård Sven Inge , Helgeland Stig , Zulkhifly Sinaga Samuel , Svendsen Morten 
Published: March 2022

Abstract
One of the most common issues faced in the Drilling industry is a Stuck Pipe situation. Stuck Pipes lead to huge losses in cost, energy and productive time. The objective of this paper is to predict stuck pipe incidents prior to their occurrence by harnessing the power of machine learning and deep learning models.
Stuck incidents are some of the most difficult and challenging situations. The proposed method uses a two-step model and available historical data from prior drilling operations to predict the occurrence of such an incident well in advance so that it can be avoided. The first step of the model performs time series predictions using a Recurrent Neural Network (RNN) with Walk Forward Validation. The second part of the model uses a Random Forest Classifier to classify the predictions from the previous step and determine if a stuck situation is likely. The classification model is pre-trained using historical drilling operational data collected from old wells. It is possible to determine how far back the model should look at the data and how far ahead it should predict. For the purpose of this paper, the model looks back 5000 timesteps and predicted 3000 timesteps ahead. One timestep is 2 seconds in this case.
This model would be able to reduce nonproductive time, and help make drilling operations cleaner, faster and greener. The biggest benefit of such a model is that it can learn on the go and would not require manual intervention. Also, the model can upgrade and modify itself to changes in the drilling operations.
Event Detection in Drilling Remarks Using Natural Language Processing
Authors: Zhang Zheng , Hou Tianjun , Kherroubi Josselin , Khvostichenko Daria 
Published: March 2022

Abstract
Remarks in daily drilling reports (DDR) and daily mud reports (DMR) are an invaluable source of information for the analysis of ongoing operations and planning of future wells. These remarks are entered as free text and thus represent unstructured information, which requires expert knowledge for interpretation. Here, we aim to develop a machine learning algorithm to automatically detect events of interest and convert this information into a structured format. Several unscheduled events, such as losses, influx and stuck pipe, were selected to develop a prototype of our natural language processing (NLP) approach for daily DMR remarks. Data selection, data annotation and analysis workflow, and results are discussed in the manuscript.
Reducing Human Error Through Automatic Kick Detection, Space Out and Dynamic Well Monitoring
Authors: Curina Francesco , Abdo Elia , Mustapha Hadi 
Published: March 2022

Abstract
Influx detection and well control are critical situations where the traditional human reaction has been the accepted standard for years. This paper discusses the results of the driller stress tests and the implementation of a system to assist the operator in kick detection, space out and preparation for well shut in. The system implements dynamic well monitoring to reduce the risk of false alarms. The objective is to prove how automation helps mitigate human factors.
A stress analysis test was conducted on a variety of experienced drillers to identify the specific operations with the highest risk factors influenced by operator stress. Once the target operations were identified, an automation algorithm was designed and tested to mitigate the human factor during these specific instances. The resulting system detects drilling kicks and automatically performs the space out operation. Dynamic trip and active tank monitoring were implemented to reduce false alarms. This provides the driller with an assistant for tripping operations. The package can be adapted to any type of rigs or blow out preventer (BOP) stacks without requiring additional hardware.
Testing was conducted in both simulated and real-life situations, during tripping and drilling operations. The system was able to predict tool joint positions in the well with a mean error consistently below 1%. The automation of the space out operation allowed the system to perform the operation in significantly shorter times and with higher accuracy, eliminating the risk of any tool joint being placed across BOP elements. During tripping operations, dynamic tank tracking effectively eliminated risks of kicks helping the driller keep a constant and adequate filling of the well. Finally, a comparison of the operator stress levels with and without the use of the automation package shows the positive impact such a system can have on situational awareness and concentration. The plug and play aspect of the system proved critical for easy and fast implementation, as well as ensuring a quick familiarization of the driller with the different functionalities. The tests also highlighted the importance of accurate and high-resolution sensors to ensure optimal working conditions.
Automation in the field of well control is a relatively new subject. This paper showcases the impacts this type of systems can have on operations and proposes an implementation method to profit from automation while minimizing its impact on critical operations. The field implementation showed how different sensor configurations can lead to different degrees of automation and, thus, different impacts on the operations.
Triaxial Leak Criterion for Generic Premium Connection Under Combined Tension and Pressure Loads
Authors: Goodman Malcolm A. 
Published: March 2022

Abstract
Recent papers introduced a triaxial leak criterion with hydrostatic dependence through the Mean Normal Stress P defined as the average of the three normal stresses. The new criterion with linear dependence on P required two leak constants. Example applications in these papers addressed American Petroleum Institute (API) connections. This paper applies the triaxial criterion to a generic premium connection and determines the leak constants from finite element analysis (FEA) for combined loads with tension.
Instead of a linear dependence on P, the FEA results for the generic premium connection correlate better with a quadratic relationship in terms of (P - σn) where σn is the Lubinski neutral stress. The quantity (P - σn) is proportional to effective stress. Quadratic dependence means that three leak constants are needed for premium connections. A plot of the leak limit curve superimposed on the pipe-body yield envelope shows load combinations where leak occurs before the pipe yields. A key conclusion is that internal leak cannot occur without tension for the loads investigated. Indeed, with sufficient tension, both internal and external leak can occur at low pressures for this modified generic premium connection. Actual premium connections should be designed so this triaxial limit curve for leak is outside (or mostly outside) the pipe-body failure envelope.
Automated Well Control Versus Traditional Well Control: A Human Error Comparison Process
Authors: Atchison Bryan Wade , Sarpangal Varun 
Published: March 2022

Abstract
Traditionally, well control systems have been entirely reliant on a single human reliably and accurately detecting an influx and shutting-in the well.  The human condition means the driller can be distracted, or unexpectedly influenced by extraneous factors. Over-reliance, then, on humans in well control can be dangerous, because of the inherent and constant exposure to human factors risk. An automated well control system has been designed to fully automate influx detection and shut-in sequences and reduce personnel exposure to rig-floor safety risks.
A comparative human factors study for the use of automated well control against traditional well control methods was performed. The analysis, completed by means of Human Reliability Assessment methodology, provides a qualitative and quantitative assessment of the human failure modes associated with the operation of the automated well control compared with the equivalent associated with traditional methods.
A Systematic Human Error Reduction and Prediction approach was used to provide the basis for subsequent quantitative assessments of human reliability. Once scenarios were subjected to the qualitative modelling process, the Success Likelihood Index methodology was applied to quantify the human error probabilities.
The outcome of the comparative analysis highlights a significant reduction in the human failure risks that automated well control brings to well control.
By examining the opportunities for human error/failure to impact upon the successful identification and shut-in of an influx, the study demonstrates that automated well control could achieve 94% reduction in the probabilities of human failures for a blowout, or 96% for a large influx.
This paper will describe the automated well control technology and summarise the outcomes of the comparative analysis between automated well control and traditional well control.
IADC Dull Code Upgrade: Objectives and Organization
Authors: Pastusek Paul , Daechsel Dustin , Macmillan Robin , van Kuilenburg Robert , Propes Chris , Shackleton David , Lyles Dustin , Oberkircher Jim , Sharples Jonathan , Neil Paul , Illerhaus Roland , Roberts Tom , Watson Willie 
Published: March 2022

Abstract
This paper describes the progress in upgrading the IADC dull code system. The IADC code is a compact, text-based way to describe the bit condition. The current PDC code was created in 1987 (Clark et.al. 1987) and revised in 1992 (Brandon et. al. 1992) along with roller cone bits (McGehee et. al. 1992) and more recently proposed for other cutting tools (Ulvedal et. al. 2011). It has held up well, however with the introduction of automated systems, machine learning, evolving products, and requirement for more detailed analysis, there is a substantial need to improve the system.
The key objectives are to: rework the IADC dull grading system to better support continuous improvement workflow, root cause analysis, reduce the subjectivity inherent in the current system, support human and emerging automated bit dull grading systems, and create a best practice for a digital storage format including searchable metadata.
Over 160 volunteer subject matter experts have been divided into four workgroups to focus on: 1) a qualitative classification scheme for PDC cutters, drill bits, and tools, 2) BHAs, motors, RSS tools, and stabilizers, 3) case studies, and 4) data definition, storage, retrieval, and exchange.
The bit and BHA sub groups are defining a standard set of examples and descriptions for each damage classification. They are gathering and labeling a set of photo examples for human training and machine learning. Once complete, the proposed codes will be sent out for an end user trial of the new system. Updated codes and best practices instructions will be created based on this feedback and submitted to the IADC for publication.
The Case Study group is collecting and documenting case studies for continuous improvement and forensics analysis to provide examples for the industry based on the input of a large group of industry and academic experts. Much of this work is presented in a companion paper. (Watson et. al. 2022)
Last, the required metadata needed to support this effort has been documented and examples of best practice storage methods for image and metadata are offered to the industry for voluntary adoption. This is the scope of workgroup 4.
This is the first major revision of the IADC dull code system in 35 years. It is taking into account changes in the data needed and available for continuous improvement, with a plan for future developments in photometrics, AI classification, and storage technologies. It is also the first joint industry attempt to document case studies and forensics workflow best practices.
Engineering and Data-Led Sustainability for Real-Time Monitoring
Authors: Samuel Robello , Adari Rishi , Srinivasan Nagaraj 
Published: March 2022

Abstract
The environmental impact of oil and gas wells from exploration to abandonment is a primary focus during well planning and drilling. However, there is no industry standard to quantify the impact of complex indicators and associated variables, particularly when the well is drilled in real time. Thus, a quantification method using the associated variables used for real-time well monitoring as the well is drilled and produced can be useful. A new model for the sustainability index for well design and engineering during drilling is presented and validated. This proposed method avoids some of the vagueness of well sustainability and can be used and applied in a practical manner. It is based on various metrics and weightage assigned when a well is drilled and compared to the planned metrics. Evaluating the index for well engineering is based on elements of environmental impact, well design and engineering, the impact of functionality and optimization, well and maintenance costs, health and safety, and societal impact. Each element contains subelements. This process involves individual indexing through backpropagation of neural networks combined with the bat algorithm to obtain the echo location to identify the overall index.
The proposed method uses a well-engineering-based approach that defines boundaries and thresholds as the well is drilled. The Powell Method, designed for nonconstraint optimization, is also used for fast convergence for this derivative-free problem. The algorithm is fast and provides results based on an iteration method. Because engineering during the well lifecycle involves several nonlinear system and asymmetric inputs, it has been observed that identifying the effects of input uncertainties and other related calculation uncertainties (i.e., variation and errors in log data, survey data, etc.) create an effect. It was also observed that uncertainty analysis provided an optimized index without assigning preferential weightage to some of the components. The method does not reduce uncertainty, but can be used to estimate the influence of various parameters on the sustainability index. To achieve runaway uncertainty, a mutation strategy is introduced to maintain the population diversity of the indices within the defined limits.
Annular Temperature Variation and its Impact on Fracture Gradient in a Deepwater Environment
Authors: Rizvi Aijaz , Hashemian Yahya , Yao Ziheng , Nelson Mark , Yuan Zhaoguang 
Published: March 2022

Abstract
Fracture gradient prediction is a crucial step in wellbore planning, casing setting depths, and fluid design. Knowing the exact fracture gradient during the drilling phase of well construction is also essential to avoid lost circulation and other hole-related problems. Most commonly, fracture gradient modeling uses a previously known formation pressure integrity test, i.e., leak-off test and rock properties but fails to consider various effects that can reduce fracture gradient. Annular drilling fluid temperature is one of the most important. This paper aims to investigate annular temperature variation and develop a practical method to avoid wellbore problems in deepwater wells.