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- amplitude (1)
- analysis (3)
- area (1)
- assessment (1)
- CSEM (1)
- data (2)
- Demonstration (1)
- demonstration area (1)
- distribution (2)
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- formation evaluation (2)
- geometry (1)
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- Scarborough (1)
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**uncertainty (3)**- value (2)
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Uncertainties in marine controlled source electromagnetic (CSEM) data consist of two independent parts: measurement noise and position uncertainties. Measurement noise can be readily determined using stacking statistics in the Fourier domain. The uncertainties due to errors in position can be estimated using perturbation analysis given estimates of the uncertainties in transmitter-receiver geometries. However, the various geometric parameters are not independent (e.g. change in antenna dip affects antenna altitude, etc.) so how uncertainties derived from perturbation analysis can be combined to derive error-bars on CSEM data is not obvious. In this study, we use data from the 2009 survey of the Scarborough gas field to demonstrate that (a) a repeat tow may be used to quantify uncertainties from geometry, (b) perturbation analysis also yields a good estimate of data uncertainties as a function of range and frequency so long as the components are added arithmetically rather than in quadrature, and (c) lack of a complex error structure in inversion yields model results which are unrealistic and leads to “over-selling” of the capabilities of CSEM at any particular prospect.

amplitude, analysis, CSEM, data, error, error structure, estimate, formation evaluation, geometry, inversion, line, model, perturbation, perturbation analysis, range, reservoir, resistivity, result, Scarborough, uncertainty

Oilfield Places: Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin > Scarborough field (0.98)

SPE Disciplines:

analysis, area, assessment, Demonstration, demonstration area, distribution, Earthquake, Engineering, factor, Haneberg, landslide, machine learning, Monte Carlo, probability, safety, slope, slope stability, stability, submarine, uncertainty, value

Industry:

- Government > Regional Government > North America Government > US Government (1.00)
- Energy > Oil & Gas > Upstream (1.00)

Oilfield Places: Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin (0.99)

SPE Disciplines:

Technology:

- IT > AI > Representation & Reasoning (0.68)
- IT > AI > Machine Learning > Statistical Learning (0.51)

The prediction of dynamic elastic constants of reservoir rocks is one of the most important aspects of petroleum engineering. In recent years, several studies have been performed for this purpose. Because of uncertainty and variability in natural materials, deterministic prediction of rock properties in the reservoir is not reasonable. The purpose of this study is to evaluate uncertainty in dynamic-elastic-constant prediction for reservoir rock. Dipole-shear-sonic-image (DSI) log data from one of the Saudi Arabian reservoirs are used to evaluate uncertainty in dynamic-elastic-property prediction. For this purpose, a multiple linear regression (MLR) is carried out to present an empirical equation for shear-wave (*S*-wave) velocity prediction. Then, probabilistic analysis using Monte Carlo simulation (MCS) is performed to evaluate the uncertainty and reliability in prediction of dynamic elastic constants (Young's modulus and Poisson's ratio). On the basis of the analysis, uncertainty and variability of rock elastic constants are considered, and the value of Young's modulus and Poisson's ratio in a special interval from the reservoir are determined with a certain probability. Finally, the impact of log-data parameters on the value of rock elastic constants in the reservoir interval is assessed.

analysis, data, distribution, DTS, formation evaluation, horizontal stress, interval, machine learning, parameter, Poisson, porosity, prediction, probability, relationship, reservoir simulation, rock, stress, uncertainty, value, velocity, well logging

Oilfield Places: Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin (0.99)

SPE Disciplines: