Real-Time Models for Drilling Process Automation: Equations and Applications

2010 ◽  
Author(s):  
Fred Florence ◽  
Fionn Petter Iversen
Keyword(s):  
Real Time ◽  
Drilling Process ◽  
Process Automation

Dynamic Measurement of Spatial Attitude at Bottom Rotating Drillstring: Simulation, Experimental, and Field Test

2015 ◽  
Vol 138 (2) ◽  
Author(s):  
Qilong Xue ◽  
Ruihe Wang ◽  
Baolin Liu ◽  
Leilei Huang
Keyword(s):  
Real Time ◽  
Measurement Errors ◽  
Time Window ◽  
Measurement Data ◽  
Movement Patterns ◽  
Dynamic Measurement ◽  
Drilling Process ◽  
Stick Slip ◽  
Actual Measurement ◽  
Gas Drilling

In the oil and gas drilling engineering, measurement-while-drilling (MWD) system is usually used to provide real-time monitoring of the position and orientation of the bottom hole. Particularly in the rotary steerable drilling technology and application, it is a challenge to measure the spatial attitude of the bottom drillstring accurately in real time while the drillstring is rotating. A set of “strap-down” measurement system was developed in this paper. The triaxial accelerometer and triaxial fluxgate were installed near the bit, and real-time inclination and azimuth can be measured while the drillstring is rotating. Furthermore, the mathematical model of the continuous measurement was established during drilling. The real-time signals of the accelerometer and the fluxgate sensors are processed and analyzed in a time window, and the movement patterns of the drilling bit will be observed, such as stationary, uniform rotation, and stick–slip. Different signal processing methods will be used for different movement patterns. Additionally, a scientific approach was put forward to improve the solver accuracy benefit from the use of stick–slip vibration phenomenon. We also developed the Kalman filter (KF) to improve the solver accuracy. The actual measurement data through drilling process verify that the algorithm proposed in this paper is reliable and effective and the dynamic measurement errors of inclination and azimuth are effectively reduced.

New LWD Image Improvement Method to Mitigate Interpretation Risks

2021 ◽  
Author(s):  
Tianhua Zhang ◽  
Shiduo Yang ◽  
Chandramani Shrivastava ◽  
Adrian A ◽  
Nadege Bize-Forest
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Best Practice ◽  
Synthetic Data ◽  
Image Resolution ◽  
Drilling Process ◽  
Drilling Tool ◽  
Stick Slip ◽  
Geological Interpretation ◽  
Wellbore Temperature

Abstract With the advancement of LWD (Logging While Drilling) hardware and acquisition, the imaging technology becomes not only an indispensable part of the drilling tool string, but also the image resolution increases to map layers and heterogeneity features down to less than 5mm scale. This shortens the geological interpretation turn-around time from wireline logging time (hours to days after drilling) to semi-real time (drilling time or hours after drilling). At the same time, drilling motion is complex. The depth tracking is on the surface referenced to the surface block movement. The imaging sensor located downhole can be thousands of feet away from the surface. Mechanical torque and drag, wellbore friction, wellbore temperature and weight on bit can make the downhole sensor movement motion not synchronized with surface pipe depth. This will cause time- depth conversion step generate image artifacts that either stop real-time interpretation of geological features or mis-interpret features on high resolution images. In this paper, we present several LWD images featuring distortion mechanism during the drilling process using synthetic data. We investigated how heave, depth reset and downhole sensor stick/slip caused image distortions. We provide solutions based on downhole sensor pseudo velocity computation to minimize the image distortion. The best practice in using Savitsky-Golay filter are presented in the discussion sections. Finally, some high-resolution LWD images distorted with drilling-related artifacts and processed ones are shown to demonstrate the importance of image post-processing. With the proper processed images, we can minimize interpretation risks and make drilling decisions with more confidence.

Real-Time Software to Estimate Friction Coefficient and Downhole Weight on Bit During Drilling of Horizontal Wells

2014 ◽  
Author(s):  
Mazeda Tahmeen ◽  
Geir Hareland ◽  
Bernt S. Aadnoy
Keyword(s):  
Friction Coefficient ◽  
Real Time ◽  
Information Transfer ◽  
Horizontal Wells ◽  
Drill String ◽  
Surface Measurement ◽  
Drilling Process ◽  
Drilling Data ◽  
Weight On Bit ◽  
Drag Analysis

The increasing complexity and higher drilling cost of horizontal wells demand extensive research on software development for the analysis of drilling data in real-time. In extended reach drilling, the downhole weight on bit (WOB) differs from the surface seen WOB (obtained from on an off bottom hookload difference reading) due to the friction caused by drill string movement and rotation in the wellbore. The torque and drag analysis module of a user-friendly real-time software, Intelligent Drilling Advisory system (IDAs) can estimate friction coefficient and the effective downhole WOB while drilling. IDAs uses a 3-dimensional wellbore friction model for the analysis. Based on this model the forces applied on a drill string element are buoyed weight, axial tension, friction force and normal force perpendicular to the contact surface of the wellbore. The industry standard protocol, WITSML (Wellsite Information Transfer Standard Markup Language) is used to conduct transfer of drilling data between IDAs and the onsite or remote WITSML drilling data server. IDAs retrieves real-time drilling data such as surface hookload, pump pressure, rotary RPM and surface WOB from the data servers. The survey data measurement for azimuth and inclination versus depth along with the retrieved drilling data, are used to do the analysis in different drilling modes, such as lowering or tripping in and drilling. For extensive analysis the software can investigate the sensitivity of friction coefficient and downhole WOB on user-defined drill string element lengths. The torque and drag analysis module, as well as the real-time software, IDAs has been successfully tested and verified with field data from horizontal wells drilled in Western Canada. In the lowering mode of drilling process, the software estimates the overall friction coefficient when the drill bit is off bottom. The downhole WOB estimated by the software is less than the surface measurement that the drillers used during drilling. The study revealed verification of the software by comparing the estimated downhole WOB with the downhole WOB recorded using a downhole measuring tool.

scholarly journals Deep Robot-Human Interaction with Facial Emotion Recognition Using Gated Recurrent Units & Robotic Process Automation

2020 ◽  
Author(s):  
Suchitra Saxena ◽  
Shikha Tripathi ◽  
Sudarshan Tsb
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Research Work ◽  
Human Interaction ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Process Automation ◽  
Training Time ◽  
Robotic Process Automation ◽  
Gated Recurrent Units

This research work proposes a Facial Emotion Recognition (FER) system using deep learning algorithm Gated Recurrent Units (GRUs) and Robotic Process Automation (RPA) for real time robotic applications. GRUs have been used in the proposed architecture to reduce training time and to capture temporal information. Most work reported in literature uses Convolution Neural Networks (CNN), Hybrid architecture of CNN with Long Short Term Memory (LSTM) and GRUs. In this work, GRUs are used for feature extraction from raw images and dense layers are used for classification. The performance of CNN, GRUs and LSTM are compared in the context of facial emotion recognition. The proposed FER system is implemented on Raspberry pi3 B+ and on Robotic Process Automation (RPA) using UiPath RPA tool for robot human interaction achieving 94.66% average accuracy in real time.

scholarly journals Research on the measurement system of drilling while engineering parameters for intelligent drilling in coal mine

2020 ◽  
Vol 165 ◽  
pp. 03036
Author(s):  
Lian Jie
Keyword(s):  
Real Time ◽  
Drilling Process ◽  
Construction Process ◽  
Directional Drilling ◽  
Drilling Tool ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Drilling Technology ◽  
Engineering Parameters ◽  
Parameter Measuring

In order to ensure safe and efficient mining and improve the efficiency of drilling construction, intelligent drilling technology has been studied in China. This technology is another development on the basis of automation. In addition to the automatic execution of the construction process, it also has the characteristics of intelligent perception, intelligent decision-making and intelligent correction. This technology requires engineering parameter measuring equipment to obtain the engineering parameters such as torque, WOB, inner and outer annulus pressure, rotation speed, vibration, temperature, etc. near the drill bit at the bottom of the hole in real time, so as to realize the real-time monitoring of the drilling process parameters at the bottom of the hole and the stress state of the drilling tool in the process of directional drilling, and increase the effective extraction distance of the drilling hole.

Automated Drilling Variances Detection Through Smart Alarms System

2021 ◽  
Author(s):  
Lucian Toader ◽  
Paulinus Abhyudaya Bimastianto ◽  
Shreepad Purushottam Khambete ◽  
Suhail Mohammed Al Ameri ◽  
Erwan Couzigou ◽  
...  
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Detection Algorithm ◽  
Physical Models ◽  
Critical Parameters ◽  
Sensor Calibration ◽  
Residual Pressure ◽  
Drilling Process ◽  
Real Time Analysis ◽  
Deterministic Modelling

Abstract In a drive to enhance drilling operational awareness, the Real-Time Operations Center (RTOC) has developed a State-of-the-Art event detection algorithm that consistently highlights the deviations of critical parameters by actively comparing real-time values against comprehensive physical models and alerting the users through a dashboard. The process relies on different levels of frequency and severity in order to detect events at their onset and prevent developing into a situation that compromises the operations. The first pillar of the solution consists of deterministic modelling of the expected values for a series of parameters in order to provide the basis for comparison and diagnostics. The main parameters sought to be modelled consist of the Standpipe Pressure, the Rotary Torque and the Hook load, which respectively are generated through individual methods taking into consideration actual conditions as well as relevant contextual data to ensure accuracy. The second pillar of the solution consists of visual alerts, triggered and displayed on a dashboard based on frequency and severity levels, as percentage of deviation from accepted operational envelope. The solution has been initially implemented during drilling operations where different issues were expected to take place, finding that whenever such occurrences took place, the algorithms were able to signal potential events in most of the cases. Some challenges were observed mainly due to sensor calibration and behavior since the expected model values not necessarily match reality, including residual pressure when the pumps are off or when the string is set on slips but the hook load values still present some variance. Also, it has been observed during transient periods where flow and rotation are changed drastically, that the stabilization to a steady state present with high variance, which has demanded the introduction of further logics within the algorithms to account for these effects and avoid the generation of false indications of issues. The solution has given encouraging results thus far in signaling different dysfunctions on the drilling process without the need of immediate human interpretation of data, which has allowed to move forward in the digitalization of operations, not only by timely signaling the onset of issues, but as well by providing the basis to further develop real time diagnosis of the problems to accelerate their resolution. The conception of the event detection based on deterministic real time analysis of individual channels against robust physical models from the existing digital twin solution has proven an immediate asset for operations on its own. By providing clear signaling of issues, while providing a solid framework to ultimately develop a diagnostic solution to translate a potential event into a proactive approach to support decision making process.

Application of Artificial Intelligence and Machine Learning to Detect Drilling Anomalies Leading to Stuck Pipe Incidents

2021 ◽  
Author(s):  
Paulinus Abhyudaya Bimastianto ◽  
Shreepad Purushottam Khambete ◽  
Hamdan Mohamed Alsaadi ◽  
Suhail Mohammed Al Ameri ◽  
Erwan Couzigou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Science ◽  
Predictive Analytics ◽  
Drilling Process ◽  
Time Data ◽  
Surface Parameters ◽  
Normal Behavior ◽  
Real Time Data ◽  
Productive Time

Abstract This project used predictive analytics and machine learning-based modeling to detect drilling anomalies, namely stuck pipe events. Analysis focused on historical drilling data and real-time operational data to address the limitations of physics-based modeling. This project was designed to enable drilling crews to minimize downtime and non-productive time through real-time anomaly management. The solution used data science techniques to overcome data consistency/quality issues and flag drilling anomalies leading to a stuck pipe event. Predictive machine learning models were deployed across seven wells in different fields. The models analyzed both historical and real-time data across various data channels to identify anomalies (difficulties that impact non-productive time). The modeling approach mimicked the behavior of drillers using surface parameters. Small deviations from normal behavior were identified based on combinations of surface parameters, and automated machine learning was used to accelerate and optimize the modeling process. The output was a risk score that flags deviations in rig surface parameters. During the development phase, multiple data science approaches were attempted to monitor the overall health of the drilling process. They analyzed both historical and real-time data from torque, hole depth and deviation, standpipe pressure, and various other data channels. The models detected drilling anomalies with a harmonic model accuracy of 80% and produced valid alerts on 96% of stuck pipe and tight hole events. The average forewarning was two hours. This allowed personnel ample time to make corrections before stuck pipe events could occur. This also enabled the drilling operator to save the company upwards of millions of dollars in drilling costs and downtime. This project introduced novel data aggregation and deep learning-based normal behavior modeling methods. It demonstrates the benefits of adopting predictive analytics and machine learning in drilling operations. The approach enabled operators to mitigate data issues and demonstrate real-time, high-frequency and high-accuracy predictions. As a result, the operator was able to significantly reduce non-productive time.

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