Processing of Seismic-While-Drilling Data from the DrillCAM System Acquired with Wireless Geophones, Top-Drive, and Downhole Vibrations Sensors

2021 ◽  
Author(s):  
Ilya Silvestrov ◽  
Emad Hemyari ◽  
Andrey Bakulin ◽  
Yi Luo ◽  
Ali Aldawood ◽  
...  
Keyword(s):  
Data Driven ◽  
Acceleration Sensor ◽  
Pilot Signal ◽  
Alignment Procedure ◽  
General Flow ◽  
Time Alignment ◽  
Vibration Sensors ◽  
Drilling Data ◽  
Final Data ◽  
Time Drift

Abstract We present processing details of seismic-while-drilling data recently acquired on one of the onshore wells by a prototype DrillCAM system with wireless geophones, top-drive, and downhole vibration sensors. The general flow follows an established practice and consists of correlation with a drillbit pilot signal, vertical stacking, and pilot deconvolution. This work's novelty is the usage of the memory-based near-bit sensor with a significant time drift reaching 30-40 minutes at the end of each drilling run. A data-driven automatic time alignment procedure is developed to accurately eliminate time drift error by utilizing the top-drive acceleration sensor as a reference. After the alignment, the processing flow can utilize the top-drive or the near-bit pilots similarly. We show each processing step's effect on the final data quality and discuss some implementation details.

scholarly journals Data-Driven Distributed Optical Vibration Sensors: A Review

2020 ◽  
Vol 20 (12) ◽  
pp. 6224-6239 ◽  
Author(s):  
Li-Yang Shao ◽  
Shuaiqi Liu ◽  
Sankhyabrata Bandyopadhyay ◽  
Feihong Yu ◽  
Weijie Xu ◽  
...  
Keyword(s):  
Data Driven ◽  
Vibration Sensors ◽  
Optical Vibration

Hybrid Physics-Based and Data-Driven Modeling for Improved Standpipe Pressure Prediction

2021 ◽  
Author(s):  
Oney Erge ◽  
Eric van Oort
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Learning Models ◽  
Drilling Data ◽  
Drilling Operations ◽  
Pump Pressure

Abstract During drilling operations, it is common to see pump pressure spikes when flow is initiated, including after a connection or after a prolonged break in drilling operations. It is important to be able to predict the magnitude of such pressure spikes to avoid compromising wellbore integrity. This study shows how a hybrid approach using data-driven machine learning coupled with physics-based modeling can be used to accurately predict the magnitude of pressure spikes. To model standpipe pressure behavior, machine learning techniques were combined with physics-based models via a rule-based, stochastic decision-making algorithm. To start, neural networks and deep learning models were trained using time-series drilling data. From there, physics-based equations that model the pressure required to break the mud's gel strength as well as the flow of non-Newtonian fluids through the entire circulation system were used to simulate standpipe pressure. Then, these two highly different methods for predicting/modeling standpipe pressure were combined by a hidden Markov model using a set of rules and transition probabilities. By combining machine learning and physics-based approaches, the best features of each model are leveraged by the hidden Markov model, yielding a more accurate and robust prediction of pressure. A similar result is not achievable with a purely data-driven black-box model, because it lacks a connection to the underlying physics. Our study highlights how drilling data analysis can be optimally leveraged. The overarching conclusion: hybrid modeling can more accurately predict pump pressure spikes and capture the transient events at flow initiation when compared to physics-based or machine learning models used in isolation. Moreover, the approach is not limited to pressure behavior but can be applied to a wide range of well construction operations. The proposed approach is easy to implement and the details of implementation are presented in this study. Being able to accurately model and manage the pressure response during drilling operations is essential, especially for wells drilled in narrow-margin environments. Pressure can be more accurately predicted through our proposed hybrid modeling, leading to safer, more optimized operations.

Machine Learning Application to Estimate Missing Rock Mechanical Logs and Reduce Uncertainty While Drilling

2021 ◽  
Author(s):  
Ardiansyah Negara ◽  
Arturo Magana-Mora ◽  
Khaqan Khan ◽  
Johannes Vossen ◽  
Guodong David Zhan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Models ◽  
Gamma Ray ◽  
Well Logging ◽  
Complex Model ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Time Data ◽  
Drilling Data

Abstract This study presents a data-driven approach using machine learning algorithms to provide predicted analogues in the absence of acoustic logs, especially while drilling. Acoustic logs are commonly used to derive rock mechanical properties; however, these data are not always available. Well logging data (wireline/logging while drilling - LWD), such as gamma ray, density, neutron porosity, and resistivity, are used as input parameters to develop the data-driven rock mechanical models. In addition to the logging data, real-time drilling data (i.e., weight-on-bit, rotation speed, torque, rate of penetration, flowrate, and standpipe pressure) are used to derive the model. In the data preprocessing stage, we labeled drilling and well logging data based on formation tops in the drilling plan and performed data cleansing to remove outliers. A set of field data from different wells across the same formation is used to build and train the predictive models. We computed feature importance to rank the data based on the relevance to predict acoustic logs and applied feature selection techniques to remove redundant features that may unnecessarily require a more complex model. An additional feature, mechanical specific energy, is also generated from drilling real-time data to improve the prediction accuracy. A number of scenarios showing a comparison of different predictive models were studied, and the results demonstrated that adding drilling data and/or feature engineering into the model could improve the accuracy of the models.

Data driven time alignment for TOF-PET

2017 ◽  
Author(s):  
Ahmadreza Rezaei ◽  
Georg Schramm ◽  
Koenraad Van Laere ◽  
Johan Nuyts
Keyword(s):  
Data Driven ◽  
Time Alignment ◽  
Tof Pet

Performance Improvement of Wells Augmented Stuck Pipe Indicator via Model Evaluations

2021 ◽  
Author(s):  
Meor M. Meor Hashim ◽  
M. Hazwan Yusoff ◽  
M. Faris Arriffin ◽  
Azlan Mohamad ◽  
Tengku Ezharuddin Tengku Bidin ◽  
...  
Keyword(s):  
Real Time ◽  
Confusion Matrix ◽  
Operation Time ◽  
Oil And Gas Industry ◽  
Data Driven ◽  
Trend Detection ◽  
Real Time Monitoring ◽  
Operational Costs ◽  
Drilling Parameters ◽  
Drilling Data

Abstract The advancement of technology in this era has long profited the oil and gas industry by means of shrinking non-productive time (NPT) events and reducing drilling operational costs via real-time monitoring and intervention. Nevertheless, stuck pipe incidents have been a big concern and pain point for any drilling operations. Real-time monitoring with the aid of dynamic roadmaps of drilling parameters is useful in recognizing potential downhole issues but the initial stuck pipe symptoms are often minuscule in a short time frame hence it is a challenge to identify it in time. Wells Augmented Stuck Pipe Indicator (WASP) is a data-driven method leveraging historical drilling data and auxiliary engineering information to provide an impartial trend detection of impending stuck pipe incidents. WASP is a solution set to tackle the challenge. The solution is anchored on Machine Learning (ML) models which assess real-time drilling data and compute the risk of potential stuck pipe based on drilling activities, probable stuck pipe mechanisms, and operation time. The output of the analysis is built on a warning and alarm system that can be utilized by the engineers to refine and optimize the well construction activities; tackling the stuck pipe issue before it manifests. This solution is evaluated by comparing historical and real-time drilling parameters with the prediction data to generate an error analysis. On top of that, a confusion matrix is tabulated based on the analysis of warnings and alarms raised by the solution to rule out Type 1 and Type 2 errors. The WASP solution has demonstrated tolerably accurate predictions of drilling parameters with minimal warnings and alarms error. With the solution, the stuck pipe issue can be identified hours earlier before the actual stuck pipe was reported in the historical well. It is a powerful tool with the capability to pinpoint possible stuck pipe mechanisms for engineer's immediate analysis and intervention. Value creation from the WASP solution has been massive with a reduction in manhours of analysis, potential NPT events, and unexpected operational costs. Data-driven techniques are effective in preventing stuck pipe incidents and will be scalable to tackle other downhole issues such as loss of circulation, well control, and borehole instability.

On-line alignment and astigmatism correction using a TV and personal computer

1993 ◽  
Vol 51 ◽  
pp. 202-203
Author(s):  
F. Hosokawa ◽  
Y. Kondo ◽  
T. Honda ◽  
Y. Ishida ◽  
M. Kersker
Keyword(s):  
High Resolution ◽  
Personal Computer ◽  
Block Diagram ◽  
Incident Beam ◽  
Ccd Camera ◽  
Alignment Procedure ◽  
Astigmatism Correction ◽  
Transmission Electron ◽  
Alignment System ◽  
On Line

High-resolution transmission electron microscopy must attain utmost accuracy in the alignment of incident beam direction and in astigmatism correction, and that, in the shortest possible time. As a method to eliminate this troublesome work, an automatic alignment system using the Slow-Scan CCD camera has been introduced recently. In this method, diffractograms of amorphous images are calculated and analyzed to detect misalignment and astigmatism automatically. In the present study, we also examined diffractogram analysis using a personal computer and digitized TV images, and found that TV images provided enough quality for the on-line alignment procedure of high-resolution work in TEM. Fig. 1 shows a block diagram of our system. The averaged image is digitized by a TV board and is transported to a computer memory, then a diffractogram is calculated using an FFT board, and the feedback parameters which are determined by diffractogram analysis are sent to the microscope(JEM- 2010) through the RS232C interface. The on-line correction system has the following three modes.

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