DOPPLER VS. SPINNER PLT SENSING FOR HYDROCARBON VELOCITY ESTIMATE BY DEEP-LEARNING APPROACH

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
◽  
Alberto Marsala ◽  
Virginie Schoepf ◽  
Linda Abbassi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Flow Velocity ◽  
Oil And Gas ◽  
Surface Flow ◽  
Sound Waves ◽  
Velocity Measurements ◽  
Data Set ◽  
Flowing Fluid ◽  
Flow Meters ◽  
Doppler Flow

Logging hydrocarbon production potential of wells has been at the forefront of enhancing oil and gas exploration and maximize productivity from oil and gas reservoirs. A major challenge is accurate downhole fluid phases flow velocity measurements in production logging (PLT) due to the criticality of mechanical spinner-based sensor devices. Ultrasonic Doppler-based sensors are more robust and deployable either in wireline or logging while drilling (LWD) conditions; however, due to the different sensing physics, the measurement results may vary. Ultrasonic Doppler flow meters utilize the Doppler effect that is a change in frequency of the sound waves that are reflected on a moving target. A common example is the change in pitch when a vehicle sounding a horn approaches and recedes from an observer. The frequency shift is in direct proportion of the relative velocity of the fluid with respect to the emitter-receiver and allows to infer the speed of the flowing fluid. Doppler flow meters offer many advantages over mechanical spinners such as the ability to measure without requiring calibration passes, the absence of mechanical moving parts, the sensors robustness to shocks and hits, easy installation and minimal affection by changes in temperature, density and viscosity of the fluid thus capability to work even in highly contaminated conditions such as tar, asphaltene deposits on equipment. Despite being widely used in surface flow metering, ultrasonic Doppler sensor applications to downhole environment have been so far very limited. We present in this work an innovative deep learning framework to estimate spinner phase velocities from Doppler based sensor velocities. Tests of the framework on a benchmark data set displayed strong estimation results, in particular outlining the ability to utilize Doppler-based sensors for downhole phase velocity measurements and allows the comparison of the estimates with previously recorded spinner velocity measurements. This allows for the real-time automated interpretative framework implementation and flow velocity estimations either in conventional wireline production logging technologies and potentially also in LWD conditions, when the well is flowing in underbalanced conditions.

Validity of Doppler Measurements of Anterior Cerebral Artery Blood Flow Velocity: Correlation with Brain Blood Flow in Piglets

PEDIATRICS ◽  
1983 ◽  
Vol 72 (4) ◽  
pp. 526-531
Author(s):  
Nancy B. Hansen ◽  
Barbara S. Stonestreet ◽  
Ted S. Rosenkrantz ◽  
William Oh
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Flow Velocity ◽  
Blood Flow Velocity ◽  
Continuous Wave ◽  
Anterior Cerebral Artery ◽  
Brain Blood Flow ◽  
Velocity Measurements ◽  
Doppler Flow ◽  
Continuous Wave Doppler

Continuous wave Doppler ultrasonography through the anterior fontanel has recently been used to assess changes in cerebral blood flow in human neonates. There has been controversy concerning whether measurements of Doppler blood flow velocity indeed correlate with brain blood flow. An in vivo correlation was performed between brain blood flow as measured by the microsphere method and Doppler flow velocity measurements of the cerebral arteries via an artificial fontanel in young piglets. The peak systolic velocity (r = .76, P < .001), end diastolic velocity (r = .72, P < .001) and area under the velocity curve (r = .86, P<.001) all showed significant positive correlations with brain blood flow. The pulsatility index did not correlate with brain blood flow. Although continuous wave Doppler flow velocity measurements of the anterior cerebral artery cannot quantitatively assess cerebral blood flow, this methodology can be used to correlate changes in cerebral blood flow and provide a meaningful trend analysis following physiologic or pharmacologic perturbation of the cerebral circulation.

scholarly journals From Surface Flow Velocity Measurements to Discharge Assessment by the Entropy Theory

Water ◽  
2017 ◽  
Vol 9 (2) ◽  
pp. 120 ◽  
Author(s):  
Tommaso Moramarco ◽  
Silvia Barbetta ◽  
Angelica Tarpanelli
Keyword(s):  
Flow Velocity ◽  
Surface Flow ◽  
Entropy Theory ◽  
Velocity Measurements

Intracoronary Doppler flow velocity measurements in two patients with solitary coronary ostium

1996 ◽  
Vol 7 (10) ◽  
pp. 761-766 ◽  
Author(s):  
Matthias C. Schmidt ◽  
Heinrich G. Klues ◽  
Jürgen vom Dahl ◽  
Thomas Brenn ◽  
Werner Hügel ◽  
...  
Keyword(s):  
Flow Velocity ◽  
Coronary Ostium ◽  
Velocity Measurements ◽  
Doppler Flow ◽  
Intracoronary Doppler

Deep Learning Assisted Doppler Sensing for Hydrocarbon Downhole Flow Velocity Estimation

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Alberto Marsala ◽  
Virginie Schoepf ◽  
Linda Abbassi
Keyword(s):  
Deep Learning ◽  
Flow Velocity ◽  
Oil And Gas ◽  
Velocity Estimation ◽  
Gas Reservoirs ◽  
Hydrocarbon Production ◽  
Oil And Gas Exploration ◽  
Learning Framework ◽  
Measurement Results ◽  
Interpretative Framework

Abstract Logging hydrocarbon production potential of wells has been at the forefront of enhancing oil and gas exploration and maximize productivity from oil and gas reservoirs. A major challenge is accurate downhole fluid phases flow velocity measurements in production logging due to the criticality of mechanical spinner-based sensor devices. Ultrasonic Doppler based sensors are more robust and deployable either in wireline or logging while drilling (LWD) conditions; however, due to the different sensing physics, the measurement results may not be equivalent. We present in this work an innovative deep learning framework to estimate spinner phase velocities from Doppler based sensor velocities. Tests of the framework on a benchmark dataset displayed strong estimation results. This allows for the real-time automatic interpretative framework implementation and flow velocity estimations either in conventional wireline production logging technologies (PLTs) and potentially also in LWD conditions, when the well is flowing in underbalanced conditions.

scholarly journals Single-Beam Acoustic Doppler Profiler and Co-Located Acoustic Doppler Velocimeter Flow Velocity Data

Data ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 61
Author(s):  
Marilou Jourdain de Thieulloy ◽  
Mairi Dorward ◽  
Chris Old ◽  
Roman Gabl ◽  
Thomas Davey ◽  
...  
Keyword(s):  
Time Series ◽  
Flow Velocity ◽  
Numerical Models ◽  
Quality Data ◽  
Velocity Measurements ◽  
Ocean Energy ◽  
Data Set ◽  
Single Beam ◽  
Correlation Statistics ◽  
Acoustic Doppler Profiler

Acoustic Doppler Profilers (ADPs) are routinely used to measure flow velocity in the ocean, enabling multi-points measurement along a profile while Acoustic Doppler Velocimeters (ADVs) are laboratory instruments that provide very precise point velocity measurement. The experimental set-up allows laboratory comparison of measurement from these two instruments. Simultaneous multi-point measurements of velocity along the horizontal tank profile from Single-Beam Acoustic Doppler Profiler (SB-ADP) were compared against multiple co-located point measurements from an ADV. Measurements were performed in the FloWave Ocean Energy Research Facility at the University of Edinburgh at flow velocities between 0.6 ms − 1 and 1.2 ms − 1 . This paper describes the data; the analysis of the inter-instrument comparison is presented in an associated Sensors paper by the same authors. This data-set contains (a) time series of raw SB-ADP uni-directional velocity measurements along a 10 m tank profile binned into 54 measurements cells and (b) ADV point measurements of three-directional velocity time series recorded in beam coordinates at selected locations along the profile. Associated with the data are instrument generated quality data, metadata and user-derived quality flags. An analysis of the quality of SB-ADP data along the profile is presented. This data-set provides multiple contemporaneous velocity measurements along the tank profile, relevant for correlation statistics, length-scale calculations and validation of numerical models simulating flow hydrodynamics in circular test facilities.

scholarly journals Combining flow routing modelling and direct velocity measurement for optimal discharge estimation

2011 ◽  
Vol 8 (2) ◽  
pp. 2699-2738
Author(s):  
G. Corato ◽  
T. Moramarco ◽  
T. Tucciarelli
Keyword(s):  
Boundary Conditions ◽  
Flow Velocity ◽  
Hydraulic Model ◽  
Surface Flow ◽  
Water Levels ◽  
Surface Velocity ◽  
Rating Curve ◽  
Velocity Measurements ◽  
Flow Routing ◽  
Maximum Surface

Abstract. A new procedure is proposed for estimating river discharge hydrographs during flood events, using only water level data measured at a gauged site, as well as 1-D shallow water modelling and sporadic maximum surface flow velocity measurements. During flood, the piezometric level is surmised constant in the vertical plane of the river section, where the top of the banks is always above the river level, and is well represented by the recorded stage hydrograph. The river is modelled along the reach directly located downstream the upstream gauged section, where discharge hydrograph is sought after. For the stability with respect to the topographic error, as well as for the simplicity of the data required to satisfy the boundary conditions, a diffusive hydraulic model is adopted for flow routing. Assigned boundary conditions are: (1) the recorded stage hydrograph at the upstream river site and (2) the zero diffusion condition at the downstream end of the reach. The MAST algorithm is used for the numerical solution of the flow routing problem, which is embedded in the Brent algorithm used for the computation of the optimum Manning coefficient. Based on synthetic tests concerning a broad prismatic channel, the optimal reach length is chosen so that the approximated downstream boundary condition effects on discharge hydrograph assessment at upstream end are negligible. The roughness Manning coefficient is calibrated by using sporadic instantaneous surface velocity measurements during the rising limb of flood that are turned into instantaneous discharges through the solid of velocity estimated by a two-dimensional entropic model. Several historical events, occurring in three gauged sites along the upper Tiber River wherein a reliable rating curve is available, have been used for the validation. The analysis outcomes can be so summarized: (1) criteria adopted for selecting the optimal channel length and based on synthetic tests have been proved reliable by using field data of three gauged river sites. Indeed, for each of them a downstream reach, long not more than 500 m, is turned out fair for achieving good performances of the diffusive hydraulic model, thus allowing to drastically reducing the topographical data of river cross-sections; (2) the procedure for Manning's coefficient calibration allowed to get high performance of the hydraulic model just considering the observed water levels and sporadic measurements of maximum surface flow velocity during the rising limb of flood. Indeed, in terms of errors in magnitude on peak discharge, for the optimal calibration, they were found, in average, not exceeding 5% for all events observed in the three investigated gauged sections, while the Nash-Sutcliff efficiency was, in average, greater than 0.95. Therefore, the proposed procedure, apart from to have turned out reliable for the rating curve assessment at ungauged sites, can be applied in realtime for whatever flood conditions and this is of great interest for the practice hydrology seeing that, looking at new monitoring technologies, it will be possible to carry out velocity measurements by hand-held radar sensors in different river sites and for the same flood.

scholarly journals Deep Learning Applications in Geosciences: Insights into Ichnological Analysis

2021 ◽  
Vol 11 (16) ◽  
pp. 7736
Author(s):  
Korhan Ayranci ◽  
Isa E. Yildirim ◽  
Umair bin Waheed ◽  
James A. MacEachern
Keyword(s):  
Deep Learning ◽  
Oil And Gas ◽  
Full Range ◽  
Large Data ◽  
Training Data ◽  
Critical Parameters ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Robust Solution ◽  
Automated Technique

Ichnological analysis, particularly assessing bioturbation index, provides critical parameters for characterizing many oil and gas reservoirs. It provides information on reservoir quality, paleodepositional conditions, redox conditions, and more. However, accurately characterizing ichnological characteristics requires long hours of training and practice, and many marine or marginal marine reservoirs require these specialized expertise. This adds more load to geoscientists and may cause distraction, errors, and bias, particularly when continuously logging long sedimentary successions. In order to alleviate this issue, we propose an automated technique to determine the bioturbation index in cores and outcrops by harnessing the capabilities of deep convolutional neural networks (DCNNs) as image classifiers. In order to find a fast and robust solution, we utilize ideas from deep learning. We compiled and labeled a large data set (1303 images) composed of images spanning the full range (BI 0–6) of bioturbation indices. We divided these images into groups based on their bioturbation indices in order to prepare training data for the DCNN. Finally, we analyzed the trained DCNN model on images and obtained high classification accuracies. This is a pioneering work in the field of ichnological analysis, as the current practice is to perform classification tasks manually by experts in the field.

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