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Recently, foam-assisted EOR has been widely used and drawn attention due to its effectiveness. However, the exploration method for detecting foam front in the subsurface has not been established yet. In this study, we examined the possibility of seismic AVO analysis to monitor the sweep front in foam-assisted EOR using numerical experiments. We assume 2 models of layered subsurface structure in the practice of foam-assisted EOR. Our numerical model includes oil and water in the reservoir, injected fluid (either water or CO2) and foam between them. We applied an AVO analysis to the synthetic data sets in which the sweep fronts advances with time. The difference between before and after the advancement of the foam front shows clear changes both in AVO intercepts and gradients. Our results indicate that the time lapse AVO analysis could detect the movement of foam fronts in the subsurface.
Presentation Date: Wednesday, October 17, 2018
Start Time: 9:20:00 AM
Location: Poster Station 11
Presentation Type: Poster
We proposed a two-step anisotropic AVO inversion method for the VTI media based on an improved form of Rüger's equation. The AVO intercept, anisotropic gradient and vertical P-wave velocity are firstly inverted simultaneously from the near-offset seismic data. Then, the Thomsen parameter
Presentation Date: Tuesday, September 26, 2017
Start Time: 11:25 AM
Location: Exhibit Hall C/D
Presentation Type: POSTER
Xie, Xiang (Bohai Oilfield Research Institute) | Fan, Jianhua (Bohai Oilfield Research Institute) | Zhang, Zhongqiao (Bohai Oilfield Research Institute) | Shen, Hongtao (Bohai Oilfield Research Institute) | Guo, Naichuan (Bohai Oilfield Research Institute)
Forward modeling and AVO analysis on wedge models are integrated in this paper to analyze the influence of thickness of a single thin bed on AVO responses. Study shows that tuning effect similar with post-stack amplitude can also be observed on pre-stack AVO intercept (P) and gradient (G) as well as their product P*G, which all reach their maximum at 1/4 wavelength and then gradually decrease to stable values with the increase of layer thickness. Quantitative analysis is subsequently conducted on AVO tuning effect through detailed comparison of AVO forward modeling on gas and brine saturated wedge models. It’s indicated that the AVO responses of both gas and brine saturated models are obviously strengthened at the tuning thickness without changing the original AVO types, namely the algebraic sign of intercept and gradient. That is to say, the differences of AVO responses between gas and brine saturated thin beds are strengthened due to tuning effect, thus making gas and brine saturated thin beds more distinguishable, which has great significances for fluid identification in thin beds.
Presentation Date: Monday, September 25, 2017
Start Time: 2:40 PM
Location: Exhibit Hall C, E-P Station 3
Presentation Type: EPOSTER
The objective of this work is to use AVO intercept and gradient, in conjunction with well-log petrophysics analysis, to discriminate and classify lithofacies in a shaly sand reservoir. Careful log and core analysis, and rock physics modeling was used to identify the important seismic litho-classes. Monte Carlo AVO simulations based on statistical rock physics were used to set up the class-conditioned probability distributions (pdfs) of intercept and gradient. The effect of thin-layer anisotropy on the probability distributions of AVO intercept and gradient was considered by simulating various realizations of sand-shale thin layers. Monte Carlo simulations, by taking into account distributions of values instead of single average values, help to avoid the flaw of averages (Mukerji and Mavko, 2005). Monte Carlo simulations also give us confidence intervals and other measures of uncertainty. Computations using averages and average trends alone do not give any indication of the uncertainty due to the variability in the properties. The pdfs were then used to classify the seismic AVO intercept and gradient cubes to estimate the most-likely facies and obtain lithofacies probability cubes.
It is becoming popular to extract fracture information from wide-azimuth P-P reflection seismic data. The extracted crack density is not influenced by the phase of the seismic data. The extracted fracture orientation is sensitive to the phase of seismic data and the nature of the rocks. Other information besides the amplitude and NMO velocity of seismic data is needed in order to uniquely determine the fracture orientation. This paper discusses the ambiguity of the fracture orientation and how it can be resolved. Todorovic-Marinic et al (2004) discussed the stabilization of crack density.
The support vector (SV) learning method can be used to classify seismic data patterns for exploration and reservoir characterization applications. The SV method is particularly good at classifying data with nonlinear characteristics. As an example the method is applied to AVO gas sand classification.
The purpose of this paper is to present a learning algorithm to classify data with nonlinear characteristics. The support vector (SV) algorithm is a novel type of learning machine based on statistical learning theory (Vapnik, 1998). The support vector (SV) machine implements the following idea: It maps the input vectors x into a high-dimensional feature space Z through some nonlinear mapping, chosen a priori. In this space, an optimal separating hyperplane is constructed to separate data groupings.
Landrø (2002) presented a deterministic The effects of pressure and fluid saturation can have the analysis of uncertainty in the estimation of pressure and same degree of impact on seismic data, thus they are often saturation changes from time-lapse AVOdata and inseparable by analysis of a single seismic data set. In such traveltime differences, assuming independent variables in cases the use of time lapse AVO analysis offers an the calculations.