An Improvement of Velocity Variation with Offset (VVO) Method in Estimating Anisotropic Parameters

Seismic anisotropy causes deviation of traveltimereflection from hyperbolic moveout. The deviation can be seen atfar offset and its deviation depends on anisotropic parameter andoffset. This paper discuss velocity variation with offset (VVO)method as a tool for estimating anisotropic parameters; ε and δ.Anisotropic parameter is one of important aspect in seismicanisotropy analysis. While other methods use non-hyperbolicmoveout for estimating anisotropic parameter, VVO method useshyperbolic assumption for moveout correction and leave reflectorunflat at far offset because anisotropy. The method calculatesresidual traveltime and then changes it into anisotropy velocity toobtain anisotropic parameter using linear inversion method. Thispaper provides an improvement and limitation of VVO methodin estimating anisotropic parameter. Comparison between VVOmethod and other established method is discussed theoretically inthis paper. To test the method, synthetic model is built and theresult show promising outcome in predicting ε. Meanwhileaccuracy for δ estimation depends on accuracy of moveoutvelocity. Advantage of VVO method is that ε and δ can beestimated separately using P-wave gather data without wellinformation

Travel Time Tomography to Delineate 3-D Regional Seismic Velocity Structure in the Banyumas Basin, Central Java, Indonesia, Using Dense Borehole Seismographic Stations

The Banyumas Basin is a tertiary sedimentary basin located in southern Central Java, Indonesia. Due to the presence of volcanic deposits, 2-D seismic reflection methods cannot provide a good estimation of the sediment thickness and the subsurface geology structure in this area. In this study, the passive seismic tomography (PST) method was applied to image the 3-D subsurface Vp, Vs, and Vp/Vs ratio. We used 70 seismograph borehole stations with a recording duration of 177 days. A total of 354 events with 9, 370 P and 9, 368 S phases were used as input for tomographic inversion. The checkshot data of a 4, 400-meter deep exploration well (Jati-1) located within the seismic network were used to constrain the shallow crustal layer of the initial 1-D velocity model. The model resolution of the tomographic inversions was assessed using the checkerboard resolution test (CRT), the diagonal resolution element (DRE), and the derivative weight sum (DWS). Using the obtained Vp, Vs, and Vp/Vs ratio, we were able to sharpen details of the geological structures within the basin from previous geological studies, and a fault could be well-imaged at a depth of 4 km. We interpreted this as the main dextral strike-slip fault that controls the pull apart process of the Banyumas Basin. The thickness of the sediment layers, as well as its layering, were also could be well determined. We found prominent features of the velocity contrast that aligned very well with the boundary between the Halang and Rambatan formations as observed in the Jati-1 well data. Furthermore, an anticline structure, which is a potential structural trap for the petroleum system in the Banyumas Basin, was also well imaged. This was made possible due to the dense borehole seismographic stations which were deployed in the study area

The Application of Support Vector Machine to Estimate Synthetic Shear Sonic Log

Abstract – Rock physics modelling is commonly applied to characterize the subsurface. Sonic log provides the elastic properties in advanced petrophysics modelling or rock physics modelling. Although it is very important, to obtain shear sonic measurement results is very expensive. Therefore, empirical and artificial intelligence allow some solutions to estimate synthetic shear sonic log. This study applicate PCA as feature selection and SVM as the regressor with TAF as the target interval for well NEGF1P. The results of feature selection are GR, DTC, and MSF log as selected features. GS optimizes the SVM kernel parameter using selected features. The best parameters for each kernel (linear and rbf) and selected feature are the input to estimate synthetic shear sonic log. The estimation result using linear kernel has R2 0.845 and root mean square error (RMSE) 15.132 and using rbf kernel has R2 0.886 and RMSE 12.989. The estimation results construe that rbf kernel estimates the synthetic sonic log with more precision than the linear kernel and indicates the linear relation between the estimated and origin log. The three other wells apply SMV with rbf kernel best parameters and selected features to estimation the synthetic shear sonic in similar interval and younger interval (GUF)

Dependence of critical porosity on pore geometry and pore structure and its use in estimating porosity and permeability

Abstract It is well recognized that the wave velocity is not only influenced by its constituent materials but also by the details of the rock bulk. This situation may bring about data points of P-wave velocity V p measured on a large number of rock samples against either porosity or permeability of the frequently scattered although certain trends may exist. This paper presents the results of a study by employing rock samples on which ϕ, k, and V p are measured in attempt to characterize critical porosity ϕ c and its relation to other rock properties. The approach used in this study is the use of Kozeny equation. The equation is believed to account for all parameters influencing absolute permeability of porous media. A mathematical manipulation done on the equation has resulted in a power law equation that relates pore geometry √(k/ϕ) to pore structure k/ϕ 3. Three different sets of sandstone amounting totally to as many as 716 samples were provided in this study. The properties measured are ϕ, k, and V p, and grain size. For each sandstone data set, at least there are nine groups of the rock samples obtained. When V p is plotted against ϕ, it is found that each group of each sandstone data set has both its own ϕ c and an excellent relation of ϕ, V p, and ϕ c. Furthermore, combining all the basic equation for V p, Kozeny equation, and the empirical relation for porosity results in a model equation to predict permeability. In conclusion, for the sandstones employed, ϕ c is a specific property of a group of rocks having a similar pore geometry