Seismic-while-drilling applications from the first DrillCAM trial with wireless geophones and instrumented top drive

2020 ◽  
Vol 39 (6) ◽  
pp. 422-429
Author(s):  
Andrey Bakulin ◽  
Ali Aldawood ◽  
Ilya Silvestrov ◽  
Emad Hemyari ◽  
Flavio Poletto
Keyword(s):  
Real Time ◽  
Polycrystalline Diamond ◽  
Drill String ◽  
Sensor Data ◽  
Continuous Data ◽  
Near Surface ◽  
Initial Application ◽  
Look Ahead ◽  
Drilling Operations ◽  
Single Sensor

Advanced geophysical sensing while drilling is being driven by trends to automate and optimize drilling and the desire to better characterize complex near surface and overburden in desert environments. We introduce the DrillCAM system, which combines a set of geophysical techniques from seismic while drilling (SWD), drill-string vibration health, estimation of formation properties at the bit, and imaging ahead of and around the bit. We present data acquisition, processing, and initial application results from the first field trial on an onshore well in a desert environment. In this study, we focus on SWD applications. For the first time, wireless geophones installed around a rig were used to acquire continuous data while drilling. We demonstrate the feasibility of such a system to provide flexible acquisition geometries that are easily expandable with increasing bit depth without interference from drilling operations. Using a top-drive sensor as a pilot, we transform the drill-bit noise into meaningful and reliable seismic signals. The data were used to retrieve a check shot while drilling, make kinematic look-ahead predictions, and obtain a vertical seismic profiling corridor stack matching surface seismic. Robust near-offset check-shot signals were received from roller-cone and polycrystalline diamond compact (PDC) bits above 7200 ft after limited preprocessing of challenging single-sensor data with supergrouping. Detecting signals from deeper sections drilled with PDC bits may require more advanced processing by using an entire 2D spread of wireless geophones and downhole pilots. The real-time capabilities of the system make the data available for continuous data processing and interpretation that will facilitate drilling automation and improve real-time decision making.

Predicting Dysfunction Vibration Events while Drilling Using LSTM Recurrent Neural Networks

2021 ◽  
Author(s):  
Narendra Vishnumolakala ◽  
Dean Michael Murphy ◽  
Thu Nguyen ◽  
Enrique Zarate Losoya ◽  
Vivekvardhan Reddy Kesireddy ◽  
...  
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Warning System ◽  
Complex Problem ◽  
Drill String ◽  
Sequential Data ◽  
Look Ahead ◽  
Drilling Data ◽  
The Future ◽  
Drilling Operations

Abstract The objective of the study is to build a robust Recurrent Neural Network system using Long-Short-Term-Memory (LSTM) to predict future vibrations during drilling operations. This provides a reliable solution to the complex problem of modeling several forms of vibrations encountered downhole. This accurate prediction system can be readily integrated into advisory/warning systems giving drillers the potential to save time, improve safety, and increase efficiency in drilling operations. High-frequency downhole drilling data onshore fields, obtained from a major O&G service provider, was used to train and validate the models. First, multiple classification algorithms such as Logistic Regression, KNN, Decision Trees, Random Forest were utilized to identify the presence and severity of Stickslip, Whirl, and other drill-string vibrations. LSTM-RNN was then used instead of traditional RNN intended for sequential data, to resolve the vanishing gradient problem. LSTM-RNN architecture was built to predict vibrations a)10 seconds and b) 30 seconds into the future. Results of the traditional classification models confirmed the hypothesis that dysfunctions can be successfully identified based on real-time downhole drilling data. 98% accuracy was obtained in successfully identifying torsional vibrations during drilling. A total of 101 parameters including measured and derived variables are available in the dataset. Modeling was performed with 14 features and vibrations were predicted. The RNN model was trained on data from multiple wells that encountered vibrations during drilling. The models were able to predict vibrations 10 seconds into the future with an MSE of 0.02 and 30 seconds into the future with reasonable accuracy and MSE of 0.10. Avoiding excessive vibrations will result in fewer trips by increasing longevity and reducing malfunctions of downhole electronics, the drill-string, and the BHA. Reduced NPT means drilling complex wells efficiently in less time which in turn directly translates to lower costs for the company. In addition to significant cost benefits, automated technology predicting anomalies and reacting in real-time translates to improved safety because it would now require fewer operators at risk on the rig floor. The work opens up avenues for a sophisticated advisory/warning system and effective ‘look-ahead’ drilling processes in the future.

Real-Time Prediction of Mud Motor Failure Using Surface Sensor Data Features and Trends

2021 ◽  
Author(s):  
Tesleem Lawal ◽  
Pradeepkumar Ashok ◽  
Eric van Oort ◽  
Dandan Zheng ◽  
Matthew Isbell
Keyword(s):  
Real Time ◽  
Cost Optimization ◽  
Damage Index ◽  
Sensor Data ◽  
Drilling Parameters ◽  
Wear And Tear ◽  
Drilling Operations ◽  
Downhole Measurements ◽  
The Impact ◽  
Motor Failure

AbstractMud motor failure is a significant contributor to non-productive time in lower-cost land drilling operations, e.g. in North America. Typically, motor failure prevention methodologies range from re-designing or performing sophisticated analytical modeling of the motor power section, to modeling motor performance using high-frequency downhole measurements. In this paper, we present data analytics methods to detect and predict motor failures ahead of time using primarily surface drilling measurements.We studied critical drilling and non-drilling events as applicable to motor failure. The impacts of mud motor stalls and drill-off times were investigated during on-bottom drilling. For the off-bottom analysis, the impact of variations in connection practices (pick up practices, time spent backreaming, and time spent exposing the tools to damaging vibrations) was investigated. The relative importance of the various features found to be relevant was calculated and incorporated into a real-time mud motor damage index.A historical drilling dataset, consisting of surface data collected from 45 motor runs in lateral hole sections of unconventional shale wells drilled in early to mid-2019, was used in this study. These motor runs contained a mix of failure and non-failure cases. The model was found to accurately predict motor failure due to motor wear and tear. Generally, the higher the magnitude of the impact stalls experienced by the mud motor, the greater the probability of eventual failure. Variations in connection practices were found not to be a major wear-and-tear factor. However, it was found that connection practices varied significantly and were often driller-dependent.The overall result shows that simple surface drilling parameters can be used to predict mud motor failure. Hence, the value derived from surface sensor information for mud motor management can be maximized without the need to run more costly downhole sensors. In addition to this cost optimization, drillers can now monitor motor degradation in real-time using the new mud motor index described here.

Improved Drilling Operations with Wired Drill Pipe and Along-String Measurements – Learnings and Highlights from Multiple North Sea Deployments

2021 ◽  
Author(s):  
Børge Engdal Nygård ◽  
Espen Andreassen ◽  
Jørn Andre Carlsen ◽  
Gunn Åshild Ulfsnes ◽  
Steinar Øksenvåg ◽  
...  
Keyword(s):  
Continental Shelf ◽  
Real Time ◽  
High Speed ◽  
Drill Pipe ◽  
Large Data ◽  
Drill String ◽  
Bottom Hole Assembly ◽  
Drilling Operations ◽  
Downhole Measurements ◽  
Remote Operations

Abstract Over the last few years, multiple wells have been drilled in the Norwegian Continental Shelf (NCS) and the United Kingdom Continental Shelf (UKCS) using wired drill pipe (WDP). This paper captures highlights from using real-time downhole measurements provided by WDP, for improved drilling operations. It presents learnings on how WDP measurements have been used in the operator's decision process. As part of WDP, along-string measurement subs (ASM) are equipped with temperature, annular/internal pressure, rotation and vibrations sensors. Data is transmitted to surface at high speed and is available in real-time, even when flow is off. The data provide great insight into the hole conditions along the drill string and at the bottom hole assembly (BHA). Based on this insight, drilling parameters at surface can be accurately adjusted, resulting in increased overall efficiency. Large data amounts can be communicated to and from surface with negligible time delay and independent from fluid circulation. Displaying the downhole measurements in real-time, both at the rig site and in remote operations centers has proven essential when optimising well construction activities. All parties need to access the same information in real-time. Moreover, the data need to be presented in an intuitive manner that enable improved operational decisions. To maximize WDP values, the Operator has learned that downhole data must be used to adjust drilling operations in real-time.

scholarly journals Interferometric ground-roll removal: Attenuation of scattered surface waves in single-sensor data

Geophysics ◽  
2010 ◽  
Vol 75 (2) ◽  
pp. SA15-SA25 ◽  
Author(s):  
David F. Halliday ◽  
Andrew Curtis ◽  
Peter Vermeer ◽  
Claudio Strobbia ◽  
Anna Glushchenko ◽  
...  
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Seismic Data ◽  
Sensor Data ◽  
Seismic Survey ◽  
Seismic Interferometry ◽  
Near Surface ◽  
Reflection Data ◽  
Ground Roll ◽  
Single Sensor

Land seismic data are contaminated by surface waves (or ground roll). These surface waves are a form of source-generated noise and can be strongly scattered by near-surface heterogeneities. The resulting scattered ground roll can be particularly difficult to separate from the desired reflection data, especially when this scattered ground roll propagates in the crossline direction. We have used seismic interferometry to estimate scattered surface waves, recorded during an exploration seismic survey, between pairs of receiver locations. Where sources and receivers coincide, these interreceiver surface-wave estimates were adaptively subtracted from the data. This predictive-subtraction process can successfully attenuate scattered surface waves while preserving the valuable reflected arrivals, forming a new method of scattered ground-roll attenuation. We refer to this as interferometric ground-roll removal.

scholarly journals The spatial data-adaptive minimum-variance distortionless-response beamformer on seismic single-sensor data

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. Q29-Q42 ◽  
Author(s):  
Ionelia Panea ◽  
Guy Drijkoningen
Keyword(s):  
Spatial Data ◽  
Seismic Data ◽  
Minimum Variance ◽  
Sensor Data ◽  
Near Surface ◽  
Total Signal ◽  
Optimal Weights ◽  
Minimum Variance Distortionless Response ◽  
Single Sensor ◽  
Data Adaptive

Coherent noise generated by surface waves or ground roll within a heterogeneous near surface is a major problem in land seismic data. Array forming based on single-sensor recordings might reduce such noise more robustly than conventional hardwired arrays. We use the minimum-variance distortionless-response (MVDR) beamformer to remove (aliased) surface-wave energy from single-sensor data. This beamformer is data adaptive and robust when the presumed and actual desired signals are mismatched. We compute the intertrace covariance for the desired signal, and then for the total signal (desired [Formula: see text]) to obtain optimal weights. We use the raw data of only one array for the covariance of the total signal, and the wavenumber-filtered version of a full seismic single-sensor record for the covariance of the desired signal. In the determination of optimal weights, a parameter that controls the robustness of the beamformer against an arbitrary desired signal mismatch has to be chosen so that the results are optimal. This is similar to stabilization in deconvolution problems. This parameter needs to be smaller than the largest eigenvalue provided by the singular value decomposition of the presumed desired signal covariance. We compare results of MVDR beamforming with standard array forming on single-sensor synthetic and field seismic data. We apply 2D and 3D beamforming and show prestack and poststack results. MVDR beamformers are superior to conventional hardwired arrays for all examples.

scholarly journals Adaptive Segmentation of Streaming Sensor Data on Edge Devices

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6884
Author(s):  
Roman Dębski ◽  
Rafał Dreżewski
Keyword(s):  
Real Time ◽  
Data Streams ◽  
State Of The Art ◽  
Sensor Data ◽  
Approximation Accuracy ◽  
Look Ahead ◽  
Approximation Errors ◽  
Segmentation Quality ◽  
Streaming Algorithm ◽  
Signal Approximation

Sensor data streams often represent signals/trajectories which are twice differentiable (e.g., to give a continuous velocity and acceleration), and this property must be reflected in their segmentation. An adaptive streaming algorithm for this problem is presented. It is based on the greedy look-ahead strategy and is built on the concept of a cubic splinelet. A characteristic feature of the proposed algorithm is the real-time simultaneous segmentation, smoothing, and compression of data streams. The segmentation quality is measured in terms of the signal approximation accuracy and the corresponding compression ratio. The numerical results show the relatively high compression ratios (from 135 to 208, i.e., compressed stream sizes up to 208 times smaller) combined with the approximation errors comparable to those obtained from the state-of-the-art global reference algorithm. The proposed algorithm can be applied to various domains, including online compression and/or smoothing of data streams coming from sensors, real-time IoT analytics, and embedded time-series databases.

Nonlinear beamforming for enhancement of 3D prestack land seismic data

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V283-V296 ◽  
Author(s):  
Andrey Bakulin ◽  
Ilya Silvestrov ◽  
Maxim Dmitriev ◽  
Dmitry Neklyudov ◽  
Maxim Protasov ◽  
...  
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Sensor Data ◽  
Reservoir Properties ◽  
Near Surface ◽  
3D Data ◽  
Common Reflection Surface ◽  
Single Sensor ◽  
The Common

We have developed nonlinear beamforming (NLBF), a method for enhancing modern 3D prestack seismic data acquired onshore with small field arrays or single sensors in which weak reflected signals are buried beneath the strong scattered noise induced by a complex near surface. The method is based on the ideas of multidimensional stacking techniques, such as the common-reflection-surface stack and multifocusing, but it is designed specifically to improve the prestack signal-to-noise ratio of modern 3D land seismic data. Essentially, NLBF searches for coherent local events in the prestack data and then performs beamforming along the estimated surfaces. Comparing different gathers that can be extracted from modern 3D data acquired with orthogonal acquisition geometries, we determine that the cross-spread domain (CSD) is typically the most convenient and efficient. Conventional noise removal applied to modern data from small arrays or single sensors does not adequately reveal the underlying reflection signal. Instead, NLBF supplements these conventional tools and performs final aggregation of weak and still broken reflection signals, where the strength is controlled by the summation aperture. We have developed the details of the NLBF algorithm in CSD and determined the capabilities of the method on real 3D land data with the focus on enhancing reflections and early arrivals. We expect NLBF to help streamline seismic processing of modern high-channel-count and single-sensor data, leading to improved images as well as better prestack data for estimation of reservoir properties.

A Tool for Derivation of Real Time Lithological Information from Drill Bit Sound

2021 ◽  
Author(s):  
Yunlai Yang ◽  
Wei Li ◽  
Fahd A. Almalki ◽  
Maher I. Almarhoon
Keyword(s):  
Real Time ◽  
Sound Transmission ◽  
Field Trials ◽  
Drill String ◽  
Drive Shaft ◽  
Drill Bit ◽  
Acoustic Sensors ◽  
Preliminary Results ◽  
Drilling Operations ◽  
Real Time Applications

Abstract Real time lithological information at the drill bit is required for some important drilling operations, such as geo-steering and casing shoe positioning. This paper presents a novel tool "Petro-phone" for recording and processing drill bit sounds, which are generated by the drill bit cutting the rock, in order to provide real time lithological information for the rock at the drill bit. A prototype and a preliminary professional version of Petro-phone have been developed and field trialed. Petro-phone is a surface tool with its acoustic sensors attached to the top drive of a drill rig at some strategical locations for maximally picking up drill bit sounds. The drill bit sounds generated at the drill bit transmit along drill string and drive shaft to reach to the acoustic sensors. Since all the parts along the drill bit sound transmission pathway are made of steel, the drill bit sounds transmit efficiently from the source (drill bit) to the sensors. Preliminary results from two field trials show that drill bit sound patterns correlate with lithologies. The results also indicate that a parameter "Apparent Power" of drill bit sounds negatively correlates with gamma log. Due to its true real time nature, Petro-phone potentially has some real time applications, such as geo-steering, casing shoes positioning. Recorded drill bit sound can also potentially be used to derive lithological information, such as lithology type.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

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