Petroleum geostatistics for nongeostatisticians

Chambers, Richard L.; Yarus, Jeffrey M.; Hird, Kirk B.

doi:10.1190/1.1438630

Cited by 35 publications

(13 citation statements)

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“…where C 0 is the nugget, C 1 the sill, and α the range of influence. The nugget effect C 0 can be interpreted as random noise, as short-scale variability, or as the measurement error [28]. The semivariograms were interpreted by fitting a Gaussian model (Equation (2)) on experimental data to estimate the nugget effect C 0 and the range of influence α.…”

Section: Estimation Of the Effective Spatial Resolutionmentioning

confidence: 99%

A Comparison of Different Methods to Estimate the Effective Spatial Resolution of FO-DTS Measurements Achieved during Sandbox Experiments

Simon

Bour

Lavenant

et al. 2020

Sensors

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For many environmental applications, the interpretation of fiber-optic Raman distributed temperature sensing (FO-DTS) measurements is strongly dependent on the spatial resolution of measurements, especially when the objective is to detect temperature variations over small scales. Here, we propose to compare three different and complementary methods to estimate, in practice, the “effective” spatial resolution of DTS measurements: The classical “90% step change” method, the correlation length estimated from experimental semivariograms, and the derivative method. The three methods were applied using FO-DTS measurements achieved during sandbox experiments using two DTS units having different spatial resolutions. Results show that the value of the spatial resolution estimated using a step change depends on both the effective spatial resolution of the DTS unit and on heat conduction induced by the high thermal conductivity of the cable. The correlation length method provides an estimate much closer to the value provided by the manufacturers, representative of the effective spatial resolutions along cable sections where temperature gradients are small or negligible. Thirdly, the application of the derivative method allows for verifying the representativeness of DTS measurements all along the cable, by localizing sections where measurements are representative of the effective temperature. We finally show that DTS measurements could be validated in sandbox experiments, when using devices with finer spatial resolution.

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Section: Estimation Of the Effective Spatial Resolutionmentioning

confidence: 99%

A Comparison of Different Methods to Estimate the Effective Spatial Resolution of FO-DTS Measurements Achieved during Sandbox Experiments

Simon

Bour

Lavenant

et al. 2020

Sensors

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“…For the approximation of the values of a variable at locations that have not been sampled, the procedures, in the context of Earth Sciences, involve well-known interpolation techniques like linear regression, ordinary kriging and co-kriging [19][20][21][22]. Regionalized variable theory has been used to estimate soil data providing a summary of soil variability in the form of a semi-variogram and a predictive technique, kriging, for unobserved values [23,24].…”

Section: Spatial Predictive Modellingmentioning

confidence: 99%

Spatial variation of PGA using neural networks

2019

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This work is oriented to studying the learning capabilities of neural networks and their impact on the development of a method for the generation of spatial variation of peak ground accelerations PGAs. This spatial variation is based on a limited number of accelerograms registered in specific geotechnical zones, along with a map of soil periods on the Mexico City area. The continuous surfaces that resulted are compared with the most common methods of interpolation, aiming to evidence the substantial advantages of NNs on those of these methods. Based on the results presented, it can be argued that using multi-parameters approaches to define spatial interpolations of a variable as important as PGA, allows to make safer engineering decisions.

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“…As noticed by Mohaghegh et al (2006), this restriction of proxy models is mainly due to "curse of dimensionality" given the high number of parameters that define a reservoir geological model. Besides, single-value parameterizations do not preserve geological consistency (spatial covariance model) required in a thorough treatment of petrophysical uncertainty (Chambers et al, 2000). An attempt to solve the issue was proposed by Zabalza-Mezghani et al, (2004).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Emulation of reservoir production forecast considering variation in petrophysical properties

Moreno

Avansi

Schiozer

et al. 2018

Journal of Petroleum Science and Engineering

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Petroleum geostatistics for nongeostatisticians

Cited by 35 publications

References 0 publications

A Comparison of Different Methods to Estimate the Effective Spatial Resolution of FO-DTS Measurements Achieved during Sandbox Experiments

A Comparison of Different Methods to Estimate the Effective Spatial Resolution of FO-DTS Measurements Achieved during Sandbox Experiments

Spatial variation of PGA using neural networks

Emulation of reservoir production forecast considering variation in petrophysical properties

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