bluuebunny/crossref_metadata_2025_split huggingface dataset:
Coupling Psychological Factors With Machine Learning to Improve Rig Technical Training
Authors: Curina Francesco , Abdo Elia , Asokan Ajith , Mustapha Hadi 
Published: March 2022

Abstract
Technical training is an essential activity for optimizing rig operations. Recently, the use of drilling simulators has revolutionized the way training is done and, accompanied with on-site assistance, it has ensured near optimal performance from the trained crews. This paper explains how machine learning and physiology can be used to improve rig technical training by monitoring the operator's stress, identifying the key operations where situational awareness is low and targeting these operations with dedicated exercises.
The developed methodology is based on a study of human psychological indicators captured through light biometric devices. These indicators are fed to a machine learning algorithm that calculates a stress index for the observed operator and uses this index to identify key operations where the operator lacks focus, is under high stress or feels a lack of preparation. The measured indicators are skin temperature, specific face movements, heart rate, and sweat. The model uses machine vision to identify key physiological parameters and a convolutional neural network to interpret them. Finally, a third algorithm correlates the stress index to specific operations.
The system can be used either in simulation environment or on the rig itself during operational studies. The primary results show high detection accuracy with minimal errors. Using this methodology for well control simulation, the main periods of high stress and low concentration were correctly identified. The repeated tests showed that different drillers or supervisors respond differently to the situation and may be stressed out by different operations. This highlighted a key drawback of the training that focuses on the same main operations for all participants. By customizing the second training session for each participant's needs, the high stress levels were significantly reduced. From the initial trials, a key point needed to be highlighted: for the study to be as non-intrusive as possible, the biometric devices used for monitoring stress need to be as light as possible. This led to a review of the devices used and a compromise between accuracy and lightness.
As with advanced military training, targeted training for drilling rig crews can deeply impact the outcome of the training and preparedness of the crew. Today, biometric devices combined with machine learning models finally, allow for an accurate detection and evaluation of human stress. Using this analysis methodology to customize training will prove essential soon and may revolutionize the way rig crews are trained.
Field Validation of an Automated Geosteering Algorithm in the Haynesville Shale
Authors: Heintzelman Levi , Willerth Marc , Elhaj Nicole , Eaton Josh 
Published: March 2022

Abstract
Traditional geosteering workflows rely heavily on the expert oversight monitoring of incoming logging-while-drilling (LWD) data and continuous manual updating of geologic interpretations to identify key points in a well where directional decisions need be made. The labor-intensive nature of this work means that a remote supervisor is often limited to only watching 2 or 3 operations simultaneously. Previous studies (Maus, et. al. 2020) have described an algorithm capable of automatically producing multiple geosteering interpretations to reduce the human workload needed to follow along with the drilling operation. This study compares the results produced by this automated geosteering algorithm with interpretations generated through traditional means. The amount of effort required to produce an equivalent outcome is quantified in order to estimate the number of rigs an expert could potentially monitor.
An automated geosteering algorithm was run in parallel with conventional geosteering for wells drilled in the Haynesville shale. Interpretations were checked for estimated landing point, modelled geologic structure throughout the lateral, and estimated footage of the wellbore in the target zone. In places where the automated interpretations provided improbable structures, control points were used to produce higher likelihood interpretations. The number of control points required was considered a proxy for the residual monitoring effort required of an operational geosteerer following the well using an automated system.
The automated interpretations showed nearly 100% agreement with human geosteerers in terms of estimated formation strata drilled through. There were a small number of segments where manual intervention was required to fix unrealistic structures and improve agreement between the geosteering algorithm and the supervising experts. The capability of the automated system to produce and maintain reasonable interpretations for large stretches of the lateral proves it could be an asset for expanding the number of rigs being supervised by an off-site expert in a remote operations center. Time that would normally be spent updating and maintaining simple geologic interpretations can instead be devoted to analyzing the most challenging intervals in each well, resulting in a more efficient allocation of personnel.
The ability to leverage automation and efficiently apply remote expertise is of increasing importance as operators aim to provide profitable returns at scale. This study quantifies the value potential for automation assisted geosteering workflows.
Wellbore Stability Assessment of an Anisotropic Shale Formation in the North Sea
Authors: Diaz Lopez Jairo Alexander , Skadsem Hans Joakim 
Published: March 2022

Abstract
The mechanical stability of wellbores is governed by formation stresses, the local pore pressure and strength properties of the rock. These formation and rock properties are often associated with uncertainty that in turn affects the confidence of collapse and fracture prognoses. We use formation stress and well-specific rock strength data to perform a mechanical wellbore stability analysis for a deep, anisotropic shale formation in the North Sea. Uncertainty in the available data and stress prognoses is accounted for and reflected in the safe mud weight window for the formation. We base the wellbore stability analysis on predictions of shear and tensile failure at the borehole wall for different wellbore trajectories through the shale formation above the reservoir. We utilize in-situ stress and pore pressure prognoses in combination with results from rock mechanical tests of 11 oriented core specimens from the actual shale formation, including unconfined and triaxial tests with different bedding plane orientations relative to the specimen axis. We propagate uncertainty in both rock strength and anisotropy, and formation properties to generate operational mud weight windows in terms of fracture and collapse pressures using a Monte Carlo approach.
The formation stress prognosis for the actual shale formation suggested the largest in-situ stress to be the vertical overburden, while the two horizontal stresses are of nearly equal magnitude. The wellbore stability analysis shows a relatively minor sensitivity in the predicted mud weight window to the azimuthal direction of the trajectory and improved stability for near-vertical compared to highly inclined trajectories. The observation is in line with established field practice of limiting the inclination angle drilled through the shale. Due to the relatively low values of cohesion in the shale, the formation collapse pressure is sensitive to the internal friction angle of the shale which in turn varies with bedding plane orientation. A safe mud weight window for varying trajectories through a deep shale formation in the North Sea has been presented. The study demonstrates how uncertainty in formation and rock properties can be accounted for to determine the confidence level of the mud weight window. The resulting operational pressure window is essential for avoiding drilling problems and optimizing casing setting depths.
Maximizing the Value of Downhole Drilling Data: A Novel Approach to Digital Drilling Data Management and Analytics
Authors: Isbell Matthew , Neal Jim , Copeland Hunter , Foster Nicole , Patrick Scott 
Published: March 2022

Abstract
Drilling operations generate a wealth of digital data that can be examined to improve efficiencies. Downhole high-frequency accelerometers and gyro sensors have been around for years but were typically used to study single well intervals. Sensor data, matched to surface measurement, is being more routinely captured across multiple wells as costs have decreased. The authors have developed a platform and workflow allowing experts to use the downhole data native resolution easily. The authors will systematically use data analysis to link drilling dynamics and downhole tool function to system design, automated rig processes, and operating parameters.
The downhole sensor data, surface drilling data, and other relevant time-based and depth-based data streams must be cleaned, synced, and combined to provide a single source of data. This is not a trivial step due to various data quality issues such as sensor clock resets. The combined data is then loaded into a web-based viewer designed to allow for analysis at the native resolution of each data stream. The operator followed this process on ten wells of a new well design with a larger horizontal hole size to benchmark and improved performance in the horizontal interval.
Managing data of this size is not often in the realm of drilling expertise, leading to unusable or lost datasets. Data consistency, timeliness, and accessibility are essential to engineers and analysts but are often lacking. The net result is that engineers can't exploit the full resolution downhole sensor data, often causing analyses to end up with few answers. Many drilling phenomena like micro-stalling of the motor and high-frequency torsional oscillation are only identifiable with high-resolution downhole data. The operator used the described platform and workflow to find and characterize the drilling limiters in the drilling system and extends work first described in SPE 204099. Examples depicting downhole tool function, including failures, will show how downhole information is used to interpret surface observations and diagnose the drilling limiters at play over the wells.
Capturing and structuring high-frequency downhole sensor data builds on the traditional approach of drilling optimization using surface parameters and shock statistics. A dataset for analytics allows engineers and service companies to monitor downhole shocks and vibrations across wells and evaluate their effects concerning drilling parameters and procedures. Metrics beyond simple shock and vibration levels better assess drilling performance as the view into the downhole environment becomes clearer.
Redefining Operations Reporting Codes to Support Digitalization of Well Operations
Authors: Baumgartner Theresa , Ridgway Marcus , Bruce Cameron , Cormier Alexander , Honey Alastair , Johannes Scott , Wheeler Nick 
Published: March 2022

Abstract
Under increasingly volatile market conditions, operators face pressure to increase performance and reduce costs through standardization of engineering, operations, services and equipment. To this effect, an operator has redefined the way it reports onsite operations through the development of a standardized set of reporting activity codes, designed as the backbone of a standardized and digital well design and execution process. The operator recognizes the value of industry wide standardization of fit-for-purpose operations codes and offers to donate the codes to the Open Subsurface Data Universe (OSDU) project.
Reporting of well activities with free text descriptions and classifications in 15-minute intervals is a common practice in well operations. The reports have traditionally informed a variety of stakeholders on operations. With a rise of analytics tools and increasingly interconnected digital systems, operators now potentially have millions of hours of classified activity data at their fingertips, with an enormous potential to improve planning and predictability of new operations. With an increased use of data comes an awareness of poor data quality that eliminates performance gains from the data collection efforts.
This work describes new concepts that helped to redefine the well activity reporting code set. An interdisciplinary team worked on defining a new operations code set for the requirements of digitized operations reporting and analysis as well as facilitating industry wide data exchange. The new codes were defined based on desired outcomes for data quality, performance analysis, automated key performance indicators (KPIs), insights from manual offset analysis, interdisciplinary interfaces and integrations with a digital well design process. In addition to breakdown of time spent on specific operations, the concepts focus on measuring performance of planned activities only. Thousands of combinations of these codes were redefined drawing on historic examples and engineering expertise from a set of activities across all types of well operations.
The effort yielded significant improvements in data quality during reporting through simplification of the coding system and assigning codes to operations in the planning stages of the well construction process to enable the rig team to report actual operations against the plan. Good data quality and well-defined codes in turn enabled calculating KPIs automatically from the data.
The standardized operations code set presents a novel way of defining operations codes for requirements beyond reporting, but with digitalization, automation and well design in mind. In addition, the operator offers to donate the set to the industry to lay the foundation for an open-source environment that enables digital technologies to flourish. A copy of the full code set is attached to the publication of this paper.
Influx Management Decision Tree for Deep Water Managed Pressure Drilling Operations with a Below Tension Ring Surface Back Pressure System
Authors: Van Noort Roger , Spriggs Paul , Arseneault Leo 
Published: March 2022

Abstract
Managed Pressure Drilling (MPD) in Surface Back Pressure (SBP) mode can recover from an influx efficiently if well conditions, MPD system capabilities and recovery procedures are understood and executed accordingly. A considerable number of operational and contingency procedures are required to execute MPD on a deepwater rig. The Driller remains responsible for well safety and he/she must work with the MPD Operator to conduct these procedures expediently to remain within the agreed MPD operational envelope. Changes in conditions, especially when an influx is detected, requires an immediate and correct response between the Driller and MPD Operator. It can be challenging to have multipage procedures for rig floor personnel to react to an influx. By the time the procedure is referenced, the influx may have exceeded the agreed MPD Operations Matrix’ limits.
This paper's authors have developed a concise decision tree to better enable the Driller and MPD Operator's response to a potential influx. Certain steps need to be taken quickly by both members to limit influx size and stay within the agreed MPD Operations Matrix. Depending on influx size, water depth and well depth, further decisions may need to be taken to either circulate the influx up the riser or perform a MPD assisted shut in. The aid that has been developed and implemented on a recent deepwater well, is the MPD Influx Management Decision Tree (IMDT).
The IMDT is a single page, graphical representation of a multipage procedure normally written for influx management in MPD operations. Using decision tree logic shapes, as well as an assigned color scheme, the Driller and MPD Operator can quickly navigate the MPD influx management procedure to bring the well under control.
The IMDT:
Prescribes who does what action and in what sequence. Is consistent with the MPD Operations Matrix color scheme (yellow, red). Illustrates decision points to be made by the Driller / MPD Operator as they execute the procedure.
IMDT specific content may vary from project to project. The value of IMDT is in how the agreed steps are clearly, concisely and logically conveyed to the Driller and MPD Operator. The one-page format means both the Driller and MPD Operator can have it readily available should it be required.
Automated Mud Check with an AI-Enhanced Automated Mud Skid: Results of a Long-Term Permian Field Trial
Authors: Gul Sercan , El-Zein Mohamed , Aldin Hamad , Aldin Munir , Mullin Chris , van Oort Eric 
Published: March 2022

Abstract
Accurate, consistent, and frequent measurement of circulation fluid properties is essential for optimum well construction. A full mud check is conducted by mud engineers typically twice a day using equipment that is now many decades old, providing data that is often of questionable quality and reliability. Sporadic and inaccurate mud check information can be the cause of fluid-related non-productive time and increased drilling costs associated with e.g. wellbore instability, lost circulation, well control and hole cleaning problems. As an alternative to manual measurements, an AI-Enhanced Automated Mud Skid (AMS) is introduced here. The AMS digitalizes and automates mud measurements, monitoring, and reporting. The current design is able to provide more than 30 mud checks at elevated temperature during a 24-hour period.
The AMS uses the data obtained from various inline sensors (i.e., Coriolis flowmeter, pressure and temperature sensors, oil-water cut analyzer, etc.) together with machine learning algorithms to report automated mud check results for water and oil-based drilling fluids. During a field trial in the Permian basin with an independent operator to evaluate the overall reliability and accuracy of the system, the unit provided real-time measurements of non-Newtonian YPL viscosity, rheological parameters (3, 6, 100, 200, 300 and 600 rpm readings), friction factor, critical Reynold's number, pressurized density, and oil-water ratio with temperature control up to 150°F. The system also provides remote equipment control, system cleaning, health monitoring, real-time read-out of mud properties, and the connection to a real-time hydraulics software for ECD calculation and drilling margin control.
During the field trial, the unit performed more than 500 WBM tests at 120°F and 1000 OBM tests at 150°F with an average accuracy of 95% to estimate rheological properties, density, and oil/water ratio of drilling fluids. Overall operational reliability was calculated as 93% taking into account mechanical failures and lessons learned from the initial field trial phase. The AMS provided benefits by avoidance of excess chemical mud treatment and dilution, efficiency gain with increased ROP from improved hydraulics, decreased fluid-related non-productive time (NPT) and trouble cost, improved consistency and accuracy of drilling fluid measurements, and novel ways of working for the mud engineer. The technology described here opens the door to fully automated fluid monitoring, maintenance, and solids control in well construction operations. This was shown to benefit well construction efficiency (e.g., lowering drilling costs by better cuttings transport), quality, productivity (e.g., reducing reservoir impairment from unwanted solids), and safety (e.g., lowering the incidence of well control and lost circulation events due to better equivalent circulating density (ECD) management in narrow margin drilling environments).
Characterizing Drilling Dysfunction: Taking the T Out of HFTO
Authors: Johnson Ashley , Bhoite Sameer , Long David , Reagan Chris 
Published: March 2022

Abstract
High-frequency torsional oscillation (HFTO) is recognised as one of the more damaging drilling dysfunctional mechanisms. The HFTO can cause significant tool damage in addition to nonproductive time (NPT). This vibration has been shown to be more prolific in motor powered steering assembles where it is thought that the long stick-out resonates torsionally with the energy coming into the system at the bit and a reflection of torsional energy at the motor.
Using data derived from a next-generation measurement-while-drilling (MWD) tool, which includes high-speed vibration and internal pressure measurements, we will show that these dynamics are more complex than generally visualized.
HFTO is in fact three different families of vibration. The conventional HFTO, where the stickout below the motor is resonating, is considered to be Type 1. In the Type 2 group, energy is trapped in the lower bottomhole assembly (BHA). Typically, this vibration has a higher frequency, than the Type 1, and is independent of the tools in the upper BHA. The characteristics of this are determined by the contact points; thus, it is dominant in the curved section or where heterogeneities trigger microdoglegs. Finally, we will show that HFTO can develop when there is a steerable motor in the BHA, the Type 3 group. The torsional waves from the bit couple through the motor causing pressure perturbations; hence instabilities in the weight on bit.
This new understanding of HFTO explains some of the anomalies in the performance of different mitigation strategies for HFTO. In particular, why HFTO is much more prominent in heterogeneous formations and while drilling a curved section.