In-situ measurement of Poisson's ratio for germanium nano-bridge in TEM

2016 ◽  
Author(s):  
Xiao Zhang ◽  
Fangfang Xu ◽  
Zhiqiang Mu ◽  
Zengfeng Di ◽  
Tie Li ◽  
...  
Keyword(s):  
Poisson’S Ratio ◽  
In Situ Measurement ◽  
Poisson's Ratio

Measurement of Cement in Situ Stresses and Mechanical Properties Without Cooling or Depressurization

2021 ◽  
Author(s):  
Meng Meng ◽  
Luke Frash ◽  
James Carey ◽  
Wenfeng Li ◽  
Nathan Welch ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Mechanical Property ◽  
Elastic Modulus ◽  
Poisson’S Ratio ◽  
Axial Stress ◽  
Triaxial Compression ◽  
In Situ Measurement ◽  
Poisson's Ratio ◽  
Ambient Conditions

Abstract Accurate characterization of oilwell cement mechanical properties is a prerequisite for maintaining long-term wellbore integrity. The drawback of the most widely used technique is unable to measure the mechanical property under in situ curing environment. We developed a high pressure and high temperature vessel that can hydrate cement under downhole conditions and directly measure its elastic modulus and Poisson's ratio at any interested time point without cooling or depressurization. The equipment has been validated by using water and a reasonable bulk modulus of 2.37 GPa was captured. Neat Class G cement was hydrated in this equipment for seven days under axial stress of 40 MPa, and an in situ measurement in the elastic range shows elastic modulus of 37.3 GPa and Poisson's ratio of 0.15. After that, the specimen was taken out from the vessel, and setted up in the triaxial compression platform. Under a similar confining pressure condition, elastic modulus was 23.6 GPa and Possion's ratio was 0.26. We also measured the properties of cement with the same batch of the slurry but cured under ambient conditions. The elastic modulus was 1.63 GPa, and Poisson's ratio was 0.085. Therefore, we found that the curing condition is significant to cement mechanical property, and the traditional cooling or depressurization method could provide mechanical properties that were quite different (50% difference) from the in situ measurement.

scholarly journals A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

2019 ◽  
Vol 11 (19) ◽  
pp. 5283 ◽  
Author(s):  
Gowida ◽  
Moussa ◽  
Elkatatny ◽  
Ali
Keyword(s):  
Poisson’S Ratio ◽  
Accurate Determination ◽  
Oil And Gas Industry ◽  
Poisson's Ratio ◽  
Elastic Behavior ◽  
Percentage Error ◽  
Ann Model ◽  
Field Development ◽  
Well Completion

Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model.

In Situ Measurement of Poisson’s Ratio of Steel Plates During Thermal Processes Using Resonant Modes

2021 ◽  
Author(s):  
Clemens Grünsteidl ◽  
Christian Kerschbaummayr ◽  
Edgar Scherleitner ◽  
Bernhard Reitinger ◽  
Georg Watzl ◽  
...  
Keyword(s):  
Poisson’S Ratio ◽  
Dual Phase Steel ◽  
Steel Plates ◽  
Poisson's Ratio ◽  
Austenitic Phase ◽  
Resonant Modes ◽  
Longitudinal Sound Velocity ◽  
Contact Free

Abstract We demonstrate the determination of the Poisson’s ratio of steel plates during thermal processing based on contact free laser ultrasound measurements. Our method utilizes resonant elastic waves sustained by the plate, provides high amplitudes, and requires only a moderate detection bandwidth. For the analysis, the thickness of the samples does not need to be known. The trend of the measured Poisson’s ratio reveals a phase transformation in dual-phase steel samples. While previous approaches based on the measurement of the longitudinal sound velocity cannot distinguish between the ferritic and austenitic phase above 770°C, the shown method can. If the thickness of the samples is known, the method also provides both sound velocities of the material. The gained complementary information could be used to analyze phase composition of steel from low temperatures up to its melting point.

Crystalline lithology across the Kapuskasing Uplift determined using in situ Poisson's ratio from seismic tomography

1992 ◽  
Vol 97 (B13) ◽  
pp. 19993 ◽  
Author(s):  
D. J. White ◽  
B. Milkereit ◽  
M. H. Salisbury ◽  
J. A. Percival
Keyword(s):  
Seismic Tomography ◽  
Poisson’S Ratio ◽  
Poisson's Ratio

Can gas sand have a large Poisson’s ratio?

Geophysics ◽  
2008 ◽  
Vol 73 (2) ◽  
pp. E51-E57 ◽  
Author(s):  
Jack P. Dvorkin
Keyword(s):  
Poisson’S Ratio ◽  
Elastic Anisotropy ◽  
Laboratory Data ◽  
Poor Quality ◽  
Poisson's Ratio ◽  
Quality Data ◽  
Poor Quality Data ◽  
Well Data ◽  
Seismic Scale

Laboratory data supported by granular-medium and inclusion theories indicate that Poisson’s ratio in gas-saturated sand lies within a range of 0–0.25, with typical values of approximately 0.15. However, some well log measurements, especially in slow gas formations, persistently produce a Poisson’s ratio as large as 0.3. If this measurement is not caused by poor-quality data, three in situ situations — patchy saturation, subresolution thin layering, and elastic anisotropy — provide a plausible explanation. In the patchy saturation situation, the well data must be corrected to produce realistic synthetic seismic traces. In the second and third cases, the effect observed in a well is likely to persist at the seismic scale.

Poisson’s ratio of eTPU molded bead foams in compression via in situ synchrotron X-ray microtomography

2021 ◽  
Author(s):  
Arun Sundar S. Singaravelu ◽  
Jason J. Williams ◽  
Pavel Shevchenko ◽  
Jasmin Ruppert ◽  
Francesco De Carlo ◽  
...  
Keyword(s):  
Poisson’S Ratio ◽  
Poisson's Ratio ◽  
X Ray

In-Situ Poisson's Ratio Determination From Interference Transient Well Test

SPE Journal ◽  
2015 ◽  
Vol 20 (05) ◽  
pp. 1041-1052 ◽  
Author(s):  
Mojtaba P. Shahri ◽  
Stefan Z. Miska
Keyword(s):  
Fluid Flow ◽  
Stress Distribution ◽  
Poisson’S Ratio ◽  
Poisson's Ratio ◽  
Well Testing ◽  
Well Test ◽  
Induced Stress ◽  
Stress Changes ◽  
Diffusivity Equation

Summary Poisson's ratio is usually determined with well logging, fracturing data, and core samples. However, these methods provide us with a Poisson's ratio that is representative of only near-wellbore regions. In this paper, a technique is proposed by extending currently used pressure-transient-testing concepts to include reservoir stresses. More specifically, the interference well test is generalized to find not only conventional flow parameters such as reservoir transmissivity and storage capacity, but also the average in-situ Poisson's ratio. This is accomplished with the generalized diffusivity equation, which takes into account flow-induced stress changes. First, a generalized diffusivity equation is formulated by considering a deformable porous medium. The main goal of the generalized diffusivity equation is to extend current well-testing methods to include both fluid-flow and rock-mechanics aspects, and to present a way to determine the rock-mechanics-related property, Poisson's ratio, from the interference-well test. The line-source solution to the diffusivity equation is used to modify the current interference well-test technique. A synthetic example is presented to show the main steps of the proposed transient well-testing analysis technique. In addition, application of the proposed method is illustrated with interference-well-test field data. With a Monte Carlo simulation, effects of uncertainty in the input data on the prediction of Poisson's ratio are investigated, as well. In addition, a coupled fluid-flow/geomechanical simulation is performed to show the validity of the proposed formulation and corresponding improvement over the current analytical approach. One can put in practice an average in-situ value in different applications requiring accurate value of Poisson's ratio on the reservoir scale. Some examples of these include in-situ-stress-field determination, stress distribution and rock-mass deformation, and the next generation of coupled fluid-flow/geomechanical simulators. By use of Poisson's ratio that could capture flow-induced stress changes, we would be able to find the stress distribution caused by production/injection within the reservoir more precisely as well.

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