bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data-Driven Optimization of Drilling Parameters
Authors: Asgharzadeh Shishavan Reza , Adam Derek , Banirazi Reza 
Published: February 2020

Abstract
A major challenge during drilling is to identify sub-formation change, enabling near-real-time adjustment of the drilling parameters to enhance performance. An algorithm that determines sub-formations and optimizes drilling parameters as formations change and then updates itself solves this problem. This paper presents case studies of wells in the New Mexico Delaware Basin with improved drilling performance, detailing the algorithm's design and ability to produce formation-specific drilling parameters.
The workflow leverages historical drilling and geological information from offset wells, statistical analysis, physics-based models, and 1 Hz drilling data to identify sub-formations and transition zones in active wells. Rock drillability is calculated and used to identify the formation change via the algorithm. Simultaneously, optimal drilling parameters for the formation are generated, using an approach that relies on mechanical specific energy (MSE).
The workflow was successfully applied to a significant population of wells in multiple fields. Analyzing the results, the main observations were as follows: reduced cumulative MSE and average drilling time, improved consistency, and expedited learning curve. These achievements to a large extent are due to improved downhole dynamics and the reduction of input variability due to the statistical analysis of the algorithm. In addition, self-learning capabilities are built into the algorithm, which can improve its own performance with the addition of offset data during field development.
In general, three variables have major effects on drilling performance: surface controls, downhole equipment, and geology. Among these variables, geology is the most uncertain and yet crucial to success, as changes in rock type can significantly affect drilling performance. The power of the algorithm comes from the fact that it quantifies geological effects along with the effects of surface controls and BHA to significantly improve performance. This enables the algorithm to determine the formations that affect drilling operations and suggest the optimum drilling parameters for that formation zone.
This work combines real-time and historical drilling and geological data to identify the sub-formation changes and proposes the optimized drilling parameters. As observed in the case studies, this can lead to a significant improvement in drilling performance, near-real-time optimization of the drilling parameters, and minimizing non-productive time due to sudden changes in formation type.
Dynamics Vibration Prediction and Comparison with Downhole Data Measurements in Unconventional Wells
Authors: Mahjoub Mohamed , Nguyen Khac-Long , Menand Stéphane , Tran Quang-Thinh , Andrianoely Marie-Ange , Manin Lionel , Dufour Régis , Isbell Matt 
Published: February 2020

Abstract
During drilling operations, high drillstring vibrations may originate from various sources, namely, the bit-rock interaction, drillstring-wellbore contacts, stick-slip phenomena, fluid-structure interaction, and mass unbalances. Excessive vibrations may induce drilling equipment failures (fatigue, cracking, washouts, and ruptures), which can be costly and catastrophic. Therefore, it is crucial to accurately model the drillstring dynamics in order to help drilling engineers make better decisions and avoid harmful vibrational effects.
This paper proposes a numerical model developed in the time domain to estimate the drillstring lateral, axial, and torsional dynamics. It is based on the finite element method coupled with the Craig-Bampton reduction method (Craig and Bampton 1968) to reduce computation time. The drillstring-wellbore contact forces are automatically determined and may occur on both tool-joints and drill pipe bodies, and the drilling fluid is accounted for by its inertial and frictional effects.
The efficiency, accuracy, and predictive capacities of the model are assessed by comparing the computational results to downhole measurements for a case study of a non-conventional well. A good agreement for both lateral and torsional vibrations is obtained. The numerical results are used for a more detailed analysis on the lateral and torsional vibrations of the system. The evolution of lateral acceleration, from a forward to backward whirling tendency on the system, can then be assessed with the use of a spectral analysis.
The novelty of this dynamics model is that it can portray a realistic geometry of a drilling assembly in 3D curved wells with the fluid presence and gives a complete study of the coupling phenomena between the axial, torsional, and lateral vibrations. The use of this novel dynamics model along with downhole data acquisition and wired pipe technology is one of the essential steps towards drilling automation to provide a better mitigation of drillstring vibrations.
The Development and Application of Real-Time Deep Learning Models to Drive Directional Drilling Efficiency
Authors: Cao Dingzhou , Hender Don , Ariabod Sam , James Chris , Ben Yuxing , Lee Micheal 
Published: February 2020

Abstract
This paper provides the technical details to develop a real-time deep learning model to detect and estimate the duration of downlinking sequences of Rotary Steerable Systems (RSS) based on a single measurement (standpipe pressure, SPP). Further analytics are derived based on the downlink recognition results together with other real-time log data (ROP, RPM, Torque, etc.) to drive directional drilling efficiency.
Real-time RSS downlink recognition is treated as an image segmentation problem. The Deep Learning (DL) models were created using the dynamic U-Net concept and materialized with a pre-trained ResNet-34 as the underlying architecture. Transfer learning was used due to the limited number of training samples (≪ 100 downlinks per onshore well) to help with speed and accuracy. The SPP time series data was segmented based on stand of pipe drilled (one image per stand). This "image" was then fed into the model for downlink recognition. To further increase the accuracy, a second opinion mechanism was applied when the models were tested and deployed into the Real-Time Drilling (RTD) system. Using a dual model approach greatly reduced the number of false positives due to non-downlink pressure fluctuations causing "noise". The patterns of SPP and its rate of change (delta SPP) are quite different. They both have pros and cons for identifying the downlink, thus two independent models were built based on these two signals. The DL model A is trained based on the original SPP signal and the DL model B is trained based on delta SPP. A downlink is confirmed only when both models show positive results.
Data of 10 onshore wells (2 rigs) drilled with RSS were segmented (8165 images in total) and labeled. There were 671 images with 795 downlinks and 7980 images without downlink. The five-fold cross-validation technique was used to identify the best model(s). The F1 score of blind test result was .991 (accuracy was ~99.82%, see Table 2). The relative error of duration estimation is 2.49%. The current rig fleet within the RTD system has a mix of drilling tool configurations - RSS and mud motors. To further validate the models’ robustness regarding drilling tools, additional tests were conducted using mud motor wells’ datasets from 21 rigs (25431 images without downlink). There were 3 false negatives from this extended test set, which resulted in a ~99.93% accuracy for the aggregated 31 wells dataset. These results suggest that the models are accurate, reliable and robust.
The real-time DL solution presented in this paper enables operators to analyze RSS performance during and between downlinking events. This would allow drilling engineers and rig supervisors to make faster, more reliable data-driven decisions to optimize performance and directional control of the well path.
Auto-Suggestive Real-Time Classification of Driller Memos into Activity Codes Using Natural Language Processing
Authors: Ucherek Jared , Lawal Tesleem , Prinz Matthew , Li Lisa , Ashok Pradeepkumar , van Oort Eric , Gobert Tatiana , Mejia Juan 
Published: February 2020

Abstract
Activity codes recorded by drillers are very useful for quantifying invisible lost time (ILT). However, classifying more than 100 activity codes accurately and consistently across various rig operations becomes infeasible for human operators. We propose an auto-suggestive system that guides the drillers to the correct codes based on memos they enter into the system. This aims to both eliminate manual classification errors and improve memo entry.
The method for extracting activity codes from memos can be broken into the following steps. The first step consists of filtering unnecessary text and vectorizing the memos. The vectors are then re-weighted using the term frequency-inverse document frequency (TFIDF) statistical measure. Next, data resampling helps to create a uniform set of labels for the training data, because there are quite a few important activity codes that appear infrequently with respect to others. Finally, a classifier is trained. It is shown that the finalized model can be used as a real-time auto-suggestive mechanism during the drillers’ data input process. Moreover, its use for cleaning up historical datasets is also explored.
This method was implemented on a large historical dataset consisting of 150 wells, and ILT analysis was performed with the original dataset and with the auto-classified dataset. Comparing these results clearly showed that performing analysis on a dataset that has not been properly classified can lead to incorrect and misleading conclusions. Also, this method did not require a manual re-labeling of the dataset for model training. This makes the algorithm readily applicable for any end-user, irrespective of the number of activity codes used. Various classifiers including logistic regression, support vector machine, random forests, naïve Bayes, and multi-layered perceptron were implemented and tested. Given comparable performances, we conclude that a simple and interpretable logistic regression model is best for real-time classification. Tests were also performed to see how many typed words in a memo would be needed before the correct activity code was identified. The results are detailed in this paper.
This is the first body of work that has taken drillers’ memos and converted them into activity codes, without the need for a human-classified training dataset. The real-time classifier is very powerful in ensuring clean data at the source and will be particularly useful when implemented on reporting systems for classifying rig activities by IADC activity codes. We further demonstrate the use of the classifier for cleansing historical datasets such that ILT analysis can be done more accurately.
Borehole Stability in Shale: Beyond Mud Weight
Authors: Araujo Ewerton , Kisinger Darren , Vidal Daniel , Cresap Barry 
Published: February 2020

Abstract
Several wells in the Gulf of Mexico were analyzed to understand the source of shale instability during drilling and wireline operations. In spite of the mud weight being above the borehole collapse pressure, the wells still exhibited signs of shale instability. As such, it was evident that another mechanism was playing a role in the failure of the shales. This study investigated the drilling fluid properties that are known to affect shale stability. One fundamental property investigated was the water phase salinity, as shales can act as osmotic membranes, allowing fluid diffusion through the porous space while restricting the movement of salt ions. This establishment of an osmotic pressure can either preserve or destroy the mechanical integrity of the shale.
The analysis compared the water phase salinity (WPS) and mud weight (MW) of several wells, in the context of geomechanics, to understand their relationship with the actual borehole stability condition. In one particular case, the data revealed the impact of mud salinity on shale stability, where three different boreholes penetrated the same shale: the original hole (OH) and two sidetracks. In the OH, there was no indication of shale instability during the eight days the hole was exposed, no cavings were reported, and the caliper log showed an in-gauge hole. The first sidetrack (ST1) was completely different where the salinity level was reduced by 7% while the MW remained the same as the OH. During the first ten days of shale exposure in ST1, there was no significant indication of shale sloughing. However, after ten days, it was challenging to maintain a stable wellbore, which led to a stuck pipe event, and an unsuccessful attempt to regain wellbore stability by raising the MW. In the second sidetrack (ST2), the salinity concentration and MW were the same as in the OH, and after fourteen days of exposure to the mud system, there were no signs of instability. The caliper from ST2 confirmed that an in-gauge hole was maintained the entire time of drilling and wireline operations.
These observations were used to calibrate a borehole stability model, which was used to plan a subsequent infill well in the field drilled at a higher inclination and with a long open hole section through the same shales. The model successfully predicted the optimum combination of MW and WPS to maintain a stable wellbore throughout drilling and wireline operations. The results show that a scientifically-driven modeling approach based on offset wellbore observations and appropriate model selection and calibration can result in a significant improvement in drilling operational uptime by minimizing shale instability.
Building a General and Sustainable Machine Learning Solution in a Real-Time Drilling System
Authors: Ben Yuxing , Han Weilu , James Chris , Cao Dingzhou 
Published: February 2020

Abstract
One of the core components of a real-time drilling analytics system is online rig state classification. We have deployed a rig state classification algorithm which leverages machine learning on a real-time drilling system. With new data continuously flowing through the machine-learning-based algorithm, monitoring performance and accuracy is a time-consuming proposition. Traditionally, misclassified results have been reviewed and corrected offline, manually. This paper will present a general and sustainable machine learning solution to address this challenge.
A convolutional neural network (CNN) model was deployed to classify rig states in a real-time drilling analytics system. After observing wells with misclassified results, the time-series data was aggregated and corrected offline. With this new, corrected data set, the original model was retrained including both the new labeled data and the original data set used to develop the model. In parallel, an offline model based on semantic segmentation and batch data aggregation was developed to continuously monitor the performance of the online model. Finally, a post-processing procedure was implemented to improve the results which were identified as inaccurate.
After studying drilling time series data from over 40 wells with over 30 million timestamps across three US onshore basins, several problems were identified that led to lower than expected CNN model inference accuracy. Those include: data integrity, data quality (unit of measure), model uncertainty, and sample distribution shift. After these issues were addressed, a new ML model was developed which had an accuracy of over 99% and f1 score of 0.99-1. The offline model, based on semantic segmentation, had better accuracy than the online model, resulting from batch data processing across entire drilling intervals. The post-processing procedure, based on domain knowledge, was applied to the data mislabeled by the online CNN model. The combination of the three techniques has led to a highly-reliable prediction which forms the foundation of drilling analytics across the entire rig fleet.
This paper presents a multi-layered approach to developing and monitoring a ML based rig state inference system for real-time drilling analytics. With rig states forming the foundation of drilling analytics, a reliable, accurate, scalable solution is required to minimize manual effort required to clean and classify data prior to analysis. The authors are not aware of any real-time drilling applications of CNN-based inference or semantic-segmentation-based post-processing to classify and monitor time-series rig state prediction.
Achieving Automated Directional Drilling Across us Onshore Basins
Authors: Coffey Scott , Groover Austin 
Published: February 2020

Abstract
As automated directional drilling matures, it is increasingly accepted by operators as a technology to improve drilling performance. The maturation of one service provider's slide automation technology was driven largely by development and experience in one US onshore basin. This approach produced a system that was robust and configurable – but configured initially for basin-specific challenges. This paper describes how automated directional drilling practices from different basins are captured to drive performance improvements and collapse the learning curve in each subsequent deployment.
The implementation of this technology beyond the development basin was guided by an understanding of directional drilling best practices and challenges in each new area. Basin-specific factors like wellbore geometry and trajectory, formation tendency, and BHA objectives were assessed and codified to drive adjustments to the automation configuration. Engineering studies of offset wells preceded initial deployments in each new basin to identify necessary adjustments; changes were iteratively implemented and monitored via novel KPIs, such as pre-slide flat time, steering control precision, and performance consistency, alongside other industry-standard metrics.
Results from deployments in multiple US onshore basins areas show that because of variations in drilling practices, basin-specific configurations are a critical part of delivering high-performance automated directional drilling. A key early finding was the importance of identifying similar and dissimilar practices between basins so that adjustments to the system's logic were focused on high-impact activities, while minimizing startup time. Furthermore, as parameters and equipment change within a basin, it is necessary to continuously monitor and revise the slide automation system to accommodate these changes. One case study is presented to show how differences in manual slide performance between basins drive changes to the automated steering system's behavior. Another case study is presented that outlines the relationship between different BHA equipment and the automation configuration.
A Novel Intervention Tool to Operate Subsurface Barrier Valves
Authors: Balasubramanian Ganesh 
Published: February 2020

Abstract
Subsurface barrier valves such as a formation isolation valves (FIVs) isolate the formation from the production bore after the first perforation and the lower completion has been installed. This enables installation of the upper completion and in some cases multiple perforation runs before the well starts producing. During this time, they protect the formation from fluid loss and pressures by acting as a bi-directional fluid barrier. In addition to reduced rig time, a key advantage of FIVs is reduced intervention costs. FIVs can be openend and closed multiple times as required, mechanically with an intervention tool such as a primary shifting tool (PST) (Fig. 1). The conveyance method for PSTs can be on coiled tubing, work string, perforating string, or wireline (using a tractor and stroker) to open and close FIVs. PSTs then must reliably engage and release from shifting profiles in the FIVs to operate the valve and release from the profile after successful valve actuation.
Changing the Game with Real-Time Downhole and along String Data During Managed Pressure Liner Running and Cementing
Authors: Hawthorn Andy , Groves Duncan , Brian Tom 
Published: February 2020

Abstract
Managed Pressure Drilling is one of several techniques gaining traction on critical wells with very tight pore and fracture pressure windows. In order to stay within these narrow mud weight windows it is critical to understand the bottom hole pressures. Some efforts have been made to combine surface modeling with downhole measurements during the actual drilling phase utilizing data sent to surface by mud pulse telemetry. However, during liner running and cementing operations this data is generally not available. Due to the narrow annular clearances around the liner and the heavier slurries used during cementing the chances of exceeding the fracture gradient are greatly magnified. To compound this during most cementing operations in managed pressure situations the surface measurements, and therefore the associated models based off them may bear no relationship to what is happening downhole, particularly as in certain circumstances the cement can be effectively in freefall inside the pipe. Measurements can be, and are, compared both before and after the actual job, but during the critical time of the actual cement job and displacement there is often no indication of what is actually happening downhole.
As stated above the telemetry system most utilized during drilling is mud pulse telemetry. However, as these systems occupy the pipe bore, and require a full mud column inside the pipe with a flow rate above a minimum threshold, then they are not used during cementing and liner running operations. To overcome these limitations a distributed measurement and acoustic telemetry network was run incorporated in the drillstring. This network sends data including annular and bore pressures, both from directly above the liner-running tool, but also distributed back along the string to surface. Acoustic telemetry transmits through the steel wall of the drillstring and therefore is independent of flow or fluid and can send data whilst tripping as well. Additionally the tools are fully through bore allowing the passage of cement darts and slurries with no real restriction. As the network also supplies interval measurements, then the passage of the different stages of the cement job can be seen as they move both down the pipe and back along the annulus.
This paper will show, through actual field results, what is really happening downhole in the critical time of the managed pressure cement job. Results will be compared with traditional surface measurements. We will describe how we used downhole and interval calculations of friction factors and equivalent circulating densities to model what is happening in and around the liner during the cement job. We will discuss the application of the data, and the resultant algorithms and calculations derived from this to affect subsequent jobs and to improve decision-making and therefore the safety and efficiency of these critical cement jobs.
Importance of the Pin-End Effect on Leak Assessment in Threaded Connections
Authors: Goodman Malcolm , Mitchell Robert , Kalil Issa 
Published: February 2020

Abstract
Because pin and box are offset, applied axial tension (or compression) creates axial shear across engaged threads that opens a connection and reduces leak resistance. Less understood is the pin-end effect which does the same thing. Internal pressure acting on the pin nose pushes the pin out of the box, creating an additive axial force that contributes to the thread shear and decreases leak resistance. This paper addresses these axial loads for threads with non-symmetric flank angles.
Multiple new concepts are presented. First, the thread shear equation, which depends on the pin-end effect, is developed for Buttress type threads in terms of the two flank angles. Second, the leak ratings for specific Buttress connections are presented in terms of a new leak criterion with dependence on backup pressure and axial force. Third, the leak dependence of Buttress type threads on backup pressure is shown to be significant and suggests that qualification testing of any connection for leak, without backup pressure, is not adequate.