Exploring the signature of distributed pressure measurements on non-slender delta wings during axial and vertical gusts

2020 ◽  
Vol 32 (11) ◽  
pp. 115110
Author(s):  
Louis A. Burelle ◽  
Wenchao Yang ◽  
Frieder Kaiser ◽  
David E. Rival
Keyword(s):  
Pressure Measurements ◽  
Delta Wings ◽  
Distributed Pressure

Data-Driven Method for Flow Sensing of Aerodynamic Parameters Using Distributed Pressure Measurements

AIAA Journal ◽  
2021 ◽  
pp. 1-13
Author(s):  
Kaiwen Zhou ◽  
Luanliang Zhou ◽  
Simeng Zhao ◽  
Xingyu Qiang ◽  
Yingzheng Liu ◽  
...  
Keyword(s):  
Data Driven ◽  
Pressure Measurements ◽  
Flow Sensing ◽  
Aerodynamic Parameters ◽  
Distributed Pressure

Estimating the Specific Productivity Index in Horizontal Wells From Distributed-Pressure Measurements Using an Adjoint-Based Minimization Algorithm

SPE Journal ◽  
2012 ◽  
Vol 17 (03) ◽  
pp. 742-751 ◽  
Author(s):  
F.. Farshbaf Zinati ◽  
J.D.. D. Jansen ◽  
S.M.. M. Luthi
Keyword(s):  
Horizontal Well ◽  
Adjoint Method ◽  
Horizontal Wells ◽  
Measurement Noise ◽  
Productivity Index ◽  
Specific Productivity ◽  
Inversion Method ◽  
Pressure Measurements ◽  
Sensor Resolution ◽  
Distributed Pressure

Summary Recent developments in the deployment of distributed-pressure-measurement devices in horizontal wells promise to lead to a new, low-cost, and reliable method of monitoring production and reservoir performance. Practical applicability of distributed-pressure sensing for quantitative-inflow detection will strongly depend on the specifications of the sensors, details of which were not publicly available at the time of publication. Therefore, we theoretically examined the possibility of identifying reservoir inflow from distributed-pressure measurements in the well. The wellbore and nearwellbore region were described by semianalytical steady-state models, and a gradient-based inversion method was applied to estimate the specific productivity index (SPI) as a function of along-well position. We employed the adjoint method to obtain the gradients, which resulted in a computationally efficient inversion scheme. With the aid of two numerical experiments (one of which was based on a real well and reservoir), we investigated the effects of well and reservoir parameters, sensor spacing, sensor resolution, and measurement noise on the quality of the inversion results. In both experiments, we generated synthetic measurements with the aid of a high-resolution reservoir-simulation model and used these to test the semianalytical inversion algorithm. In the first experiment, we considered a 2000-m horizontal well passing through two 300-m high-permeability streaks in a background with a permeability that was 10 times lower. The location of the streaks and the SPIs along the well were detected with fair accuracy using 20 unknown parameters (SPI values) and 20 pressure measurements. Decreasing the number of measurements resulted in a poorer detection of the streaks and their SPIs. The detection performance also decreased for increasing noise levels and deteriorated sensor resolution, though the negative effect of random measurement noise was cancelled out primarily by stacking multiple measurements. The detrimental effects of measurement noise and low sensor resolution were strongest in areas where the inflow was lowest (usually close to the toe). The second experiment concerned a high-rate near-horizontal well with slightly varying inclination that intersected a dipping package of formations with strongly variable permeabilities. Additionally, a satisfactory detection of SPIs was obtained even though the heterogeneities were no longer perpendicular to the well as in the first experiment. As a result of using the simple semianalytical forward model and the adjoint method, the inversions typically required less than 90 seconds on a standard laptop. This offered the opportunity to extend the algorithm to multiphase flow and dynamic applications (pressure-transient testing), while still maintaining sufficient computational speed to perform the inversion in real time.

Distributed Pressure Measurements Allow Early Quantification of Reservoir Dynamics in the Jene Field

1991 ◽  
Vol 6 (01) ◽  
pp. 55-62 ◽  
Author(s):  
G.F. Bunn ◽  
M.J. Wittmann ◽  
W.D. Morgan ◽  
R.C. Curnutt
Keyword(s):  
Pressure Measurements ◽  
Distributed Pressure

Determination of vortex burst location on delta wings from surface pressure measurements

AIAA Journal ◽  
1992 ◽  
Vol 30 (11) ◽  
pp. 2736-2739 ◽  
Author(s):  
Douglas I. Greenwell ◽  
Norman J. Wood
Keyword(s):  
Surface Pressure ◽  
Pressure Measurements ◽  
Delta Wings

Signal Interpretation Considerations When Estimating Subglottal Pressure From Oral Air Pressure

2019 ◽  
Vol 62 (5) ◽  
pp. 1326-1337 ◽  
Author(s):  
Brittany L. Perrine ◽  
Ronald C. Scherer ◽  
Jason A. Whitfield
Keyword(s):  
Standard Condition ◽  
Word Production ◽  
Air Pressure ◽  
Pressure Waveform ◽  
Pressure Measurements ◽  
Air Leak ◽  
Pressure Signal ◽  
Adult Participants ◽  
Syllable Repetition ◽  
Subglottal Pressure

Purpose Oral air pressure measurements during lip occlusion for /pVpV/ syllable strings are used to estimate subglottal pressure during the vowel. Accuracy of this method relies on smoothly produced syllable repetitions. The purpose of this study was to investigate the oral air pressure waveform during the /p/ lip occlusions and propose physiological explanations for nonflat shapes. Method Ten adult participants were trained to produce the “standard condition” and were instructed to produce nonstandard tasks. Results from 8 participants are included. The standard condition required participants to produce /pːiːpːiː.../ syllables smoothly at approximately 1.5 syllables/s. The nonstandard tasks included an air leak between the lips, faster syllable repetition rates, an initial voiced consonant, and 2-syllable word productions. Results Eleven oral air pressure waveform shapes were identified during the lip occlusions, and plausible physiological explanations for each shape are provided based on the tasks in which they occurred. Training the use of the standard condition, the initial voice consonant condition, and the 2-syllable word production increased the likelihood of rectangular oral air pressure waveform shapes. Increasing the rate beyond 1.5 syllables/s improved the probability of producing rectangular oral air pressure signal shapes in some participants. Conclusions Visual and verbal feedback improved the likelihood of producing rectangular oral air pressure signal shapes. The physiological explanations of variations in the oral air pressure waveform shape may provide direction to the clinician or researcher when providing feedback to increase the accuracy of estimating subglottal pressure from oral air pressure.

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