bluuebunny/crossref_metadata_2025 huggingface dataset:
Drilling Fluids Design and Field Deployment for the First HTHP Deepwater Production Project in the US Gulf of Mexico
Authors: Jaimes Juan Pablo , Croy Seth , Bouguetta Mario , Williford Mike
Published: March 2022

High-temperature, high-pressure (HTHP) describes wells where the bottom hole static temperatures (BHST) are at least 300°F. Such wells are not a new topic these days. For a decade, drilling fluid systems have been able to exceed what was traditionally considered HTHP into the realm of ultra-HTHP, meaning wells where the temperature is greater than 500°F. However, in the US Gulf of Mexico (GOM), Bureau of Safety and Environmental Enforcement (BSEE) defines HTHP as greater than 15,000 psi at the wellhead and greater than 350°F (Nannen 2020).

For the first ever HTHP production project in the GOM to receive BSEE approval, reservoir drilling fluid (RDF) formulation was designed for deepwater use. It was formulated to withstand the temperature rigors of an over 350°F BHST. The lower intermediate section also exceeded the limit of the typical synthetic-based mud (SBM) systems used in the US GOM. A high performance, low-sag low-equivalent circulating density (ECD) drilling fluid system was redesigned into a new variant, extending the temperature stability to 340°F for prolonged static periods.

Emphasis was placed on key testing parameters to ensure the drilling fluids would still meet performance requirements in a deepwater environment, while enhancing stability in the high temperature wells. Particular emulsifier packages and weight material were utilized to improve long term sag stability, with static aging stretching up to 28 days. Specialized fluid loss reducers were employed to maintain emulsion stability and tight filter cakes during extended HTHP testing at BHSTs over 350°F.

This paper will explore the process of formulating drilling fluids for these HTHP drilling environments. From the laboratory to the field, this case study will carry through to results and lessons learned from the actual project execution. The drilling fluid systems deployed allowed the expansion of the drilling envelop in the GOM thus the access to new oil reserves.
Design and Analysis of a Comprehensive Database Containing 24,000 Polycrystalline Diamond Compact Cutter Scrape Tests
Authors: Panayirci H. Murat , Skoff Greg , Shen Yuelin
Published: March 2022

This paper presents the design and analysis of a database containing more than 24,000 polycrystalline diamond compact (PDC) cutter scrape tests performed using a pressurized experimental laboratory setup. This unique data set contains high-frequency (2 kHz) measurements of the three-axis forces acting on the cutter in various rocks, cutter types and sizes, cutter orientations, depths-of-cut (DOC), and confining pressures. The database can be accessed, visualized, sorted, classified, and analyzed in detail, offering substantial opportunities to increase the knowledge of the rock-cutting process by PDC cutters. Using this database, the authors initially analyzed the rock type, cutter orientation, and DOC effects on the forces acting on the cutter. By using signal processing techniques, algorithms were developed to extract features from the force data. For example, each test was scored based on the rock chipping that occurred during cutting, which manifests itself as sawtooth patterns in the force data. These data allowed for qualitative evaluation of the effects of confining pressure and DOC on the chipping. In addition, the rock types most prone to chipping were identified. This massive dataset allowed for statistical quantification of the forces acting on the cutters, which resulted in new ways for modeling the rock-cutting process, which is inherently stochastic. Furthermore, it is also possible to quantify the efficiency improvement with different cutter shapes when cutting different rocks, which assists in selecting the correct cutter shape for the targeted application to improve drilling performance. In summary, this extensive database provides novel understandings of the rock-cutting process, which are fundamental to the efficiency of the drilling operations, drilling dynamics modeling, and drilling tool development.
Cement Expansion in Cased Hole Environments: A Novel Laboratory Testing and Evaluation Method with Successful Field Implementation
Authors: Sukhachev Vasilii , Salehpour Amir , Akhmetzianov Ilshat , Sozonov Alexander , Bakhareva Elizaveta , Shabanova Sabina , Zinovyeva Anastasia , Olennikova Olesya , Droger Nicolas
Published: March 2022

Preventing bulk shrinkage of cement in cased hole wells is important when dealing with the risk of sustained casing pressure (SCP). Current studies based on membrane tests suggest that even with a high percentage content of expanding agents, cement systems show little or no expansion in dry media.

An alternative laboratory method, for measuring cement expansion followed by a successful field application, has been used to evaluate the total bulk volume change of the cement based on curing in a dry environment and in water.

A major operator in the Caspian region who experienced some occasions of SCP in the past, is adopting various measures to prevent and mitigate the SCP. This paper presents laboratory testing and successful field implementation of an expanding cement system capable of expanding in the absence of water. This expanding cement system is tested with the American Petroleum Institute (API) membrane and ring test methods, which resulted in proposing a comprehensive procedure modification. The expanding cement system behavior is presented in terms of rheological, filtration, and mechanical properties. The operator accepted the methodology and successfully implemented the recommended solution on their selected wells.

Test results using the API membrane and expansion ring methods are presented. Testing procedure modifications, including curing environment, placement time, shear history, and gel and compressive strength development for measuring bulk volume change of cement are proposed. Contrary to all expectations, a cement system with simple modifications provided measurable expansion in a dry environment while being stable and not over expanding in water.

Optimal cement slurry properties were obtained. The developed cement system, which was successfully implemented in the field, provided long-term zonal isolation with no indication of sustained casing pressure to date.

For the first time, an alternative method is being used to evaluate the bulk volume change of cement that has been exposed to a dry environment. The research results and field data indicate that the expanding cement systems perform well and can be used in intervals with no access to water. Results of this effort will undoubtedly assist operators deal with sustained casing pressures by adopting the methodology.
New Experimental Results Show the Application of Fiber Optic to Detect and to Track Gas Position in Marine Risers and Shed Lights on the Gas Migration Phenomenon Inside a Closed Well
Authors: Santos Otto , Almeida Mauricio , Sharma Jyotsna , Kunju Mahendra , Chen Yuanhang , Waltrich Paulo
Published: March 2022

The main objective of this manuscript is to present and to discuss the results and significant observations gathered during 13 experimental runs conducted in a full-scale test well at Louisiana State University (LSU). The other two objectives of this manuscript are to show the use of distributed fiber optic sensing and downhole pressure sensors data to detect and to track the gas position inside the test well during the experiments; and to discuss experimental and simulated data of the gas migration phenomenon in a closed well.

An existing test well at LSU research facilities was recompleted and instrumented with fiber optic sensors to continuously collect downhole data and with four pressure and temperature downhole gauges at four discrete depths within an annulus formed 9 5/8″ casing and 2-7/8″ to a depth of 5025′. A chemical line was attached to the tubing allowing the nitrogen injection at the bottom of the hole. The research facilities were also equipped with a surface data acquisition system. The experiments consisted in injecting nitrogen into the test well filled with water by two means: either injecting it down through the chemical line or down through the tubing to be subsequently bullheaded to the annulus. Afterwards, either the nitrogen was circulated out of the well with a backpressure being applied at surface to mimic an MPD operation or left to migrate to the surface with the test well closed.

During the runs, the three acquisition systems (fiber optic, downhole gauges, and surface data acquisition) recorded all relevant well control parameter for a variety of gas injected volumes (2.0-15.1 bbl), circulation rates (100-300 GPM) and applied backpressures (100-300 psi). The experimental results gathered by the acquisition systems were very consistent in measuring gas velocities inside the well. The numerical model predictions matched very close the pressure behavior observed in the experimental trials. In the gas migration experiments, it was observed that the bottomhole pressure is not carried to the surface and that this pressure is a function of the volume of gas injected in the well. These facts are supported by the numerical simulation results.

The manuscript shows the possibility of the use of fiber optic and downhole pressure sensors information to detect and to track the gas position inside a well or the marine riser during normal or MPD operations. Additionally, the vast amount of experimental data gathered during the experiments in which the nitrogen was left in the closed well to migrate to surface helped shed lights on the controversial issue concerning the surface pressure build-up while the gas migrates to surface in a closed well. Numerical simulations were all instrumental for supporting the findings.
Measuring of Carbon Footprint in Offshore Drilling
Authors: Allen Marcos
Published: March 2022

The Oil, Gas and Energy industry contributes to combined scope 1/2/3 emissions exceeding 42% of the global GHG emissions, with extraction and drilling accounting for 10% of scope 1 emissions (McKinsey & Company, 2020). With the demand for exploration and production predicted to increase by 2035, offshore drilling will not be replaced by renewable energy sources for the next 15 to 20 years (McKinsey & Company, 2019).

The commitment of NetZero by 2050 aligns with climate science to limit the temperature rise by 1.5 deg C pre-industrial levels. While low carbon energy sources will significantly reduce scope 1 emission, the transition will take time to introduce. In this time, the oil, gas and energy industry will provide a critical role in the technology and economic innovations to provide a cleaner future.

Modelling and carbon footprint of GHG emissions is a significant step in pursuing this goal, giving the vision of decarbonising all offshore drilling operations. The paper aims to solidify the concept of granular carbon footprint, allowing pre-operation decisions with accurate operational trending of CO2 emissions.
Methodology to Use the Cement Bond Quality Classification Matrix as Input for Leakage Rates Assessment
Authors: Ford Eric , Lohne Hans Petter , Beltrán-Jiménez Katherine , Delabroy Laurent
Published: March 2022

Verification of the quality of the cement barrier behind casing is often performed through acoustic or ultrasonic cement logs that probe the presence of cement and its bonding to the casing. In risk-based approaches to permanent well abandonment design, the probable conditions of the existing barriers in the well should be reflected in terms of reliability, or conversely, the risk of leakage through the barrier over time. Connecting the log response to a predictive model of leakage potential behind casings remains a key challenge for the industry.

We build on ultrasonic cement logs acquired in two sandwich joints recently retrieved from a North Sea production well as part of the permanent abandonment operation. The joints consist of 30 years old annulus cement sandwiched between production and intermediate casing joints that have been subjected to a comprehensive test regimen involving pressure and seepage testing. Combined with the log response, these measurements offer an unprecedented opportunity to compare log response and physical measurements of leakage potential. We have developed a methodology for converting a log response to a likely, local microannulus size that in turn reflects the seepage potential of the annular barrier.

Physical testing of the two sandwich joints used as basis for the study revealed significant local variations in seepage potential ranging from effective microannuli of tens of microns down to a very low permeable region with practically impermeable features over the timescale of measurement. Our methodology is based on a Monte Carlo approach for propagating uncertainty from log interpretation to annular seepage potential. We discretize the log response azimuthally and axially into volume elements of locally constant permeability and compare numerical log realizations to the physical measurements. We find generally good qualitative agreement with the cement log and demonstrate conceptually the possibility of utilizing a cement log response for a quantitative assessment of the barrier quality.

Improved interpretation of barrier quality is a key step toward new rig-less abandonment operations and more efficient risk-based abandonment design. Our study represents a first conceptual study that attempts to connect physical measurements of the cement quality behind production casing to its log response. More efforts are required to fully utilize the information contained in cement logs for quantitative comparison of different P&A designs.
Coupling Psychological Factors With Machine Learning to Improve Rig Technical Training
Authors: Curina Francesco , Abdo Elia , Asokan Ajith , Mustapha Hadi
Published: March 2022

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

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

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

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

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

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

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

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.
Towards a Practical, Generally-Applicable Condition-Based Maintenance CBM System for Mud Pumps
Authors: Yoon Dongyoung , Gul Sercan , Ashok Pradeepkumar , van Oort Eric
Published: March 2022

Unexpected failure of mud pumps during drilling operations can result in non-productive time (NPT) and increase well construction cost. Several prior studies and implementations of condition-based maintenance (CBM) systems for mud pumps have failed to provide a generalized solution for the variety of pump types encountered in the field, in particular by failing to detect damage early enough to mitigate NPT. Our research is aimed at improving upon this situation by developing a practical, generally-applicable CBM system for mud pumps.

In the study reported here, a laboratory test bed with a triplex mud pump was used to collect data to test a new approach to mud pump CBM. Artificial damage was introduced to the two most frequently replaced parts of the pump, i.e., the valve and piston. An accelerometer and an acoustic emission (AE) sensor were used to collect experimental data. Based on this data, an anomaly detection algorithm was constructed using a one-class support vector machine (OC-SVM) to pin-point the early onset of mud pump failure. The CBM methodology thus developed does not require prior knowledge (data) of the mud pump itself or of the failures of its components. This is key to it being more widely deployable.

The trained machine-learning algorithm in the test setup provided an accuracy greater than 90% in detecting the damaged state of the valve and piston. Only the characterization of the normal (i.e., non-damaged) state data was required to train the model. This is a very important result, because it implies that the sensors can be deployed directly onto mud pumps in the field – and additionally, that the first few hours of operation are sufficient to benchmark normal operating conditions. Also, it was observed that a multi-sensor approach improved the accuracy of detection of both the valve and piston damage. The system is able to detect early-stage damage by combining the cumulative sum control chart (CUSUM) with the damage index developed in this project.

This work is the first attempt at applying semi-supervised learning for CBM of mud pumps. The approach is applicable for field use with very little or no prior damage data, and in various working conditions. Additionally, the system can be universally deployed on any triplex pump and efficiently uses the data collected in the first few hours of operation as a baseline. Consequently, the practicality and scalability of the system are high. It is expected to enable the timely maintenance of critical rig equipment before catastrophic damage, failure and associated downtime occurs. The system has been deemed promising enough to be field-trialed, and is currently being trialed on rigs in North America.
How Deep Learning can Provide Consistent Improvement on ROP Through Different Drilling Environments
Authors: Junca Rivera Diego Alberto , Bohorquez Gutierrez Julian Ricardo , Dontsova Evgeniya , Estrada Giraldo John , Martinez Ferreira Jesus , Webb Jerry
Published: March 2022

Using multidimensional analysis of historical drilling parameters combined with deep learning (DL) techniques, consistent ROP improvement in different drilling environments can be achieved. This discussion is focused on how offset well data can be properly analyzed and modeled to generate valuable outputs that improve not only drilling performance but also sustainability across the entire upstream.

The workflow starts by history-matching comparable wells based on different variables such as well shape, hole size, wellbore design, downhole tools, etc. This first step is heavily based on the collaborative effect between subject matter experts working closely with data scientists to ensure only the right variables that are known to effect ROP are used and that a stable and scalable model can be aheived for different drilling environments. The process then moves to the data scientists where using multilayer perceptron models and random forest techniques allow determination of the ranking of features that affect ROP the most. The top tier features are then used to train a machine learning (ML) model to determine the average threshold of historic performance. Once the threshold is known, ML is again used to determine the optimal combination of drilling parameters that yield above-average ROP performance. This process is then repeated for each formation type and hole size. The performance range of the historical offset well data is then reviewed to determine the recommended threshold and fl score to output the highest modeling performance and the output parameter recommendations are then uploaded on a dashboard for real time guidance.
A Framework to Capture the Relationships in Drilling Data and the Propagation of Uncertainty
Authors: Cayeux Eric , Damski Carlos , Macpherson John , Laing Moray , Annaiyappa Pradeep , Harbidge Philip , Edwards Michael , Carney Jonathan
Published: March 2022

Drilling oil and gas wells is a complex process involving many disciplines and stakeholders. This process occurs in a context where some pieces of information are unknown, or are often incomplete, erroneous or at least uncertain. Yet, during drilling engineering and construction of a well, drilling data quality and uncertainty are barely addressed in an auditable and scientific way. Currently, there are few or no placeholders in engineering and operational databases to document uncertainty and its propagation.

The SPE has formed a cross-disciplinary technical sub-committee to investigate how to describe and propagate drilling data quality and uncertainty. The sub-committee is a cooperation between the Drilling System Automation, Wellbore Positioning, and Drilling Uncertainty Prediction Technical Sections. As the topic is vast and complex, a systematic method was adopted, where multiple user stories or pain points were generated, and ranked with the most compelling user story analyzed in detail. From this approach, a series of multi-disciplinary workflow - drilling data generators - can now be captured and described in terms of data quality and propagation of uncertainty.

The paper presents details of one "user story" focused on capturing the description of the quality and uncertainty of depths. Multiple "use cases" have been extracted from this single "user story" exemplifying how multiple stakeholders and disciplines manage, communicate, and understand the notion of wellbore depth and its relative uncertainty. Current data stores have the main objective of recording the results of processes but have very limited capabilities to store how the interdisciplinary processes generated and cross-related these results. The study explores the use of semantic graphs to capture those multidisciplinary data relationships. A minimum vocabulary has been created using just a few tens of concepts that has sufficient expressiveness to describe all the extracted "use cases", showing that the semantic graph method has the potential to describe a broad range of complex drilling related processes. The study also demonstrates that use of a parallel graph, employing other notions that do not expressly refer to the processes that generated the data can capture the description of how uncertainty propagates between each of those concepts.

This paper describes the development of an initial reference implementation of semantic graph manipulation, the associated vocabulary and the description of uncertainty and quality notions and their linkage in terms of uncertainty propagation. This reference implementation will be available as open source to the industry drilling community allowing software solutions that capture and describe the generation of drilling data through multi-disciplinary workflows, and how they relate in terms of uncertainty propagation.
Unconventional Bradenhead Cementing in the Delaware Basin – Case History
Authors: Pelton Ben , Adam Derek , Granier Garrett , Turner William , Tellez Muradas Diego , Willis John
Published: March 2022

This Delaware Basin case history describes a bradenhead cementing method that improves zonal isolation and top of cement (TOC). Common solutions for cementing depleted formations include stage tools, low-density slurries and reverse circulation. For this bradenhead method, cement is pumped through the wellhead valves and down the annulus to a target loss zone. Field data, lab data and the engineering model support the improvement bradenhead cementing provides across depleted formations.

The depleted Brushy Canyon formation serves as an injection interval for a two-stage bradenhead cement job. This approach isolates formations below the Brushy Canyon with a conventional first stage, while achieving TOC with the second stage pumped down the annulus. Fracture gradients of the injection interval and casing shoe constrain the cement design. To achieve adequate cement placement, the design phase includes the pressure calculations required to initiate flow and injection for the bradenhead stage. Reactive spacers are utilized to prevent annulus fluid level drop in extreme cases. A benchmarked cement bond log (CBL) quantifies the results and validates this unconventional cementing method.

More than 50 two-stage bradenhead cement jobs were executed with a TOC success rate of 100% in intermediate and production hole applications. CBL data obtained after cementing the intermediate casing confirms the two-stage bradenhead as a placement method. In areas constrained by casing shoe pressures, CBL data identified potential areas of shallow injection. These data resulted in a modification of the fluids pumped. Use of these re-engineered fluids reduced injection initiation pressures and improved cement bonding across salt and anhydrite formations. Cementing costs were reduced by eliminating stage tools, annular casing packers, and low-density cement slurries. Pumping the bradenhead job offline (off the rig's critical path) reduces cost further. These features support bradenhead cementing as an effective solution for wellbore sections with a potential injection zone.

The regulatory requirement to place cement above depleted formations resulted in many innovative cementing methods. Stage tools, low-density slurries, and reverse circulation are attempts to avoid formation breakdown, but these methods increase cost and have limited reliability and integrity. Bradenhead cementing takes advantage of the depleted interval to aid in cement placement. Although counterintuitive, bradenhead cementing has proved to be a cost-effective solution that improves zonal isolation and eliminates integrity risks associated with stage tool failures.