Sh. Maleki; H. R. Ramazi; M. J. Ameri Shahrabi
Abstract
Shear wave velocity (Vs) is considered as a key parameter in determination of the subsurface geomechanical properties in any hydrocarbon-bearing reservoir. During a well logging operation, the magnitude of Vs can be directly measured through the dipole shear sonic imager (DSI) logs. On a negative note, ...
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Shear wave velocity (Vs) is considered as a key parameter in determination of the subsurface geomechanical properties in any hydrocarbon-bearing reservoir. During a well logging operation, the magnitude of Vs can be directly measured through the dipole shear sonic imager (DSI) logs. On a negative note, this method not only is limited to one dimensional (1D) interpretation, it also appears to be relatively costly. In this research work, the magnitude of Vs is calculated using one set of controversial petrophysical logs (compressional wave velocity) for an oil reservoir situated in the south part of Iran. To do this, initially, the pertinent empirical correlations between the compressional (Vp) and shear wave velocities are extracted for DSI logs. Then those empirical correlations are deployed in order to calculate the values of Vs within a series of thirty wells, in which their Vp values are already recorded. Afterwards, the Kriging estimator along with the Back Propagation Neural Network (BPNN) technique are utilized to calculate the values of Vs throughout the whole reservoir. Eventually, the results obtained from the two aforementioned techniques are compared with each other. Comparing those results, it turns out that the Kriging estimation technique presents more accurate values of Vs than the BPNN technique. Hence, the supremacy of the Kriging estimation technique over the BPNN technique must be regarded to achieve a further reliable magnitude of Vs in the subjected oil field. This application can also be considered in any other oil field with similar geomechanical and geological circumstances.
R. Gholami; A. Moradzadeh
Abstract
Reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. In fact, determination of permeability is a crucial task in reserve estimation, production and development. Traditional methods for permeability prediction are well log and core data analysis which are ...
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Reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. In fact, determination of permeability is a crucial task in reserve estimation, production and development. Traditional methods for permeability prediction are well log and core data analysis which are very expensive and time-consuming. Well log data is an alternative approach for prediction of permeability because they are usually available for all of the wells. Hence, attempts have been made to utilize well log data to predict permeability. However, because of complicate and non-linear relationship of well log and core permeability data, usual statistical and artificial methods are not completely able to provide meaningful results. In this regard, recent works on artificial intelligence have led to the introduction of a robust method generally called support vector machine (SVM). The term “SVM” is divided into two subcategories: support vector classifier (SVC) and support vector regression (SVR). The aim of this paper is to use SVR for predicting the permeability of three gas wells in South Pars filed, Iran. The results show that the overall correlation coefficient (R) between predicted and measured permeability of SVR is 0.97 compared to 0.71 of a developed general regression neural network. In addition, the strength and efficiency of SVR was proved by less time-consuming and better root mean square error in training and testing dataset.