Browsing by Subject "inversion"
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Item An AVO method toward direct detection of lithologies combining P-P and P-S reflection data(Texas A&M University, 2004-09-30) Carcuz Jerez, Juan Ramon de JesusI here present a combined AVO analysis of P-P and P-S reflection data whose objective is to improve the identification of lithology by estimating the specific values of Poisson's ratio, [sigma], for each rock formation in a given geological model, rather than a contrast between formations. Limited knowledge on the elastic parameters of a given rock formation and difficulty regarding the availability and processing of P-S data constitute hindrances of lithology identification. Considering that ocean bottom seismology (OBS) has aided in solving the problem of P-S data availability, limited information on elastic parameters is still a challenge, and the focus of this thesis. The present analysis is based on Zoeppritz' solution for the P-P and P-S reflection coefficients, RPP and RPS, with a slight modification. We used the normalized P-S reflection coefficient; i.e., R'PS = RPS / sin [theta] for [theta] > 0, instead of RPS, where [theta] is the incident angle. By normalizing RPS, we avoid dealing with the absence of converted S-waves at small incident angles and enhance the similar linear behavior of the P-P and normalized P-S reflection coefficients at small angles of incidence. We have used the linearity of RPP and R'PS at angles smaller than 35 degrees to simultaneously estimate the average VP/VS ratio, the contrasts of P- and S-wave velocities, and the contrast of density. Using this information, we solve for Poisson's ratio of each formation, which may enable lithology discrimination. The feasibility of this analysis was demonstrated using nonlinear synthetic data (i.e., finite-difference data). The results in estimating Poisson's ratio yielded less than 5 percent error. We generalize this new combined P-P and P-S AVO analysis for dipping interfaces. Similarly to the nondipping interface case, our derivations show that the amplitude variation with offset (AVO) of P-P and P-S for a dipping interface can be cast into intercepts and gradients. However, these intercepts and gradients depend on the angle of the dipping interface. Therefore, we further generalize our analysis by including a migration step that allows us to find the dipping angle. Because seismic data is not available in terms of RPP and R'PS, this process includes recovery of reflection coefficients after migrating the data and correcting for geometrical spreading, as done by Ikelle et al. (1986 and 1988). The combination of all of these steps, namely geometrical-spreading correction, migration, and AVO analysis, is another novelty of this thesis, which leads to finding the specific values of Poisson's ratio of each rock formation directly from the seismic data.Item Experimental time-domain controlled source electromagnetic induction for highly conductive targets detection and discrimination(Texas A&M University, 2007-09-17) Benavides Iglesias, AlfonsoThe response of geological materials at the scale of meters and the response of buried targets of different shapes and sizes using controlled-source electromagnetic induction (CSEM) is investigated. This dissertation focuses on three topics; i) frac- tal properties on electric conductivity data from near-surface geology and processing techniques for enhancing man-made target responses, ii) non-linear inversion of spa- tiotemporal data using continuation method, and iii) classification of CSEM transient and spatiotemporal data. In the first topic, apparent conductivity profiles and maps were studied to de- termine self-affine properties of the geological noise and the effects of man-made con- ductive metal targets. 2-D Fourier transform and omnidirectional variograms showed that variations in apparent conductivity exhibit self-affinity, corresponding to frac- tional Brownian motion. Self-affinity no longer holds when targets are buried in the near-surface, making feasible the use of spectral methods to determine their pres- ence. The difference between the geology and target responses can be exploited using wavelet decomposition. A series of experiments showed that wavelet filtering is able to separate target responses from the geological background. In the second topic, a continuation-based inversion method approach is adopted, based on path-tracking in model space, to solve the non-linear least squares prob- lem for unexploded ordnance (UXO) data. The model corresponds to a stretched- exponential decay of eddy currents induced in a magnetic spheroid. The fast inversion of actual field multi-receiver CSEM responses of inert, buried ordnance is also shown. Software based on the continuation method could be installed within a multi-receiver CSEM sensor and used for near-real-time UXO decision. In the third topic, unsupervised self-organizing maps (SOM) were adapted for data clustering and classification. The use of self-organizing maps (SOM) for central- loop CSEM transients shows potential capability to perform classification, discrimi- nating background and non-dangerous items (clutter) data from, for instance, unex- ploded ordnance. Implementation of a merge SOM algorithm showed that clustering and classification of spatiotemporal CSEM data is possible. The ability to extract tar- get signals from a background-contaminated pattern is desired to avoid dealing with forward models containing subsurface response or to implement processing algorithm to remove, to some degree, the effects of background response and the target-host interactions.Item Rapid assessment of infill drilling potential using a simulation-based inversion approach(Texas A&M University, 2006-08-16) Gao, HuiIt is often difficult to quantify the drilling and recompletion potential in producing gas fields, due to large variability in rock quality, well spacing, well completion practices, and the large number of wells involved. Given the marginal nature of many of these fields, it is often prohibitively expensive to conduct conventional reservoir characterization and simulation studies to determine infill potential. There is a need for rapid, cost-efficient technology to evaluate infill potential in gas reservoirs, particularly tight gas reservoirs. Some authors have used moving window statistical methods, which are useful screening tools for identifying potential areas or groups of wells for further study. But the accuracy of the moving window method in very heterogeneous reservoirs is limited, based on the analysis of some authors. This study presents a new simulation-based inversion approach for rapid assessment of infill well potential. It differs from typical simulation inversion applications in that, instead of focusing on small-scale, high-resolution problems, it focuses on large-scale, coarse-resolution studies consisting of hundreds or, potentially thousands, of wells. In an initial application, the method employs well locations, production data, an approximate reservoir description and, accordingly, is able to identify potential areas or groups of wells for infill development quickly and inexpensively. Prediction accuracy can be increased commensurate with reservoir characterization effort, time and costs. Thus, the method provides a consistent basis for transition from screening studies to conventional reservoir studies.The proposed approach is demonstrated to be more accurate than moving window statistical methods in synthetic cases, with comparable analysis times and costs. In a bind validation study of a field case with 40 years of production history, the method was able to accurately predict performance for a group of 19 infill wells.Item Stochastic and Deterministic Inversion Methods for History Matching of Production and Time-Lapse Seismic Data(2013-08-26) Watanabe, ShingoAutomatic history matching methods utilize various kinds of inverse modeling techniques. In this dissertation, we examine ensemble Kalman filter as a stochastic approach for assimilating different types of production data and streamline-based inversion methods as a deterministic approach for integrating both production and time-lapse seismic data into high resolution reservoir models. For the ensemble Kalman filter, we develope a physically motivated phase streamline-based covariance localization method to improve data assimilation performance while capturing geologic continuities that affect the flow dynamics and preserving model variability among the ensemble of models. For the streamline-based inversion method, we derived saturation and pressure drop sensitivities with respect to reservoir properties along streamline trajectories and integrated time-lapse seismic derived saturation and pressure changes along with production data using a synthetic model and the Brugge field model. Our results show the importance of accounting for both saturation and pressure changes in the reservoir responses in order to constrain the history matching solutions. Finally we demonstrated the practical feasibility of a proposed structured work- flow for time-lapse seismic and production data integration through the Norne field application. Our proposed method follows a two-step approach: global and local model calibrations. In the global step, we reparameterize the field permeability het- erogeneity with a Grid Connectivity-based Transformation with the basis coefficient as parameters and use a Pareto-based multi-objective evolutionary algorithm to integrate field cumulative production and time-lapse seismic derived acoustic impedance change data. The method generates a suite of trade-off solutions while fitting production and seismic data. In the local step, first the time-lapse seismic data is integrated using the streamline-derived sensitivities of acoustic impedance with respect to reservoir permeability incorporating pressure and saturation effects in-between time-lapse seismic surveys. Next, well production data is integrated by using a generalized travel time inversion method to resolve fine-scale permeability variations between well locations. After model calibration, we use the ensemble of history matched models in an optimal rate control strategy to maximize sweep and injection efficiency by equalizing flood front arrival times at all producers while accounting for geologic uncertainty. Our results show incremental improvement of ultimate recovery and NPV values.Item Streamline-based production data integration in naturally fractured reservoirs(Texas A&M University, 2005-08-29) Al Harbi, Mishal H.Streamline-based models have shown great potential in reconciling high resolution geologic models to production data. In this work we extend the streamline-based production data integration technique to naturally fractured reservoirs. We use a dualporosity streamline model for fracture flow simulation by treating the fracture and matrix as separate continua that are connected through a transfer function. Next, we analytically compute the sensitivities that define the relationship between the reservoir properties and the production response in fractured reservoirs. Finally, production data integration is carried out via the Generalized Travel Time inversion (GTT). We also apply the streamline-derived sensitivities in conjunction with a dual porosity finite difference simulator to combine the efficiency of the streamline approach with the versatility of the finite difference approach. This significantly broadens the applicability of the streamlinebased approach in terms of incorporating compressibility effects and complex physics. The number of reservoir parameters to be estimated is commonly orders of magnitude larger than the observation data, leading to non-uniqueness and uncertainty in reservoir parameter estimate. Such uncertainty is passed to reservoir response forecast which needs to be quantified in economic and operational risk analysis. In this work we sample parameter uncertainty using a new two-stage Markov Chain Monte Carlo (MCMC) that is very fast and overcomes much of its current limitations. The computational efficiency comes through a substantial increase in the acceptance rate during MCMC by using a fast linearized approximation to the flow simulation and the likelihood function, the critical link between the reservoir model and production data. The Gradual Deformation Method (GDM) provides a useful framework to preserve geologic structure. Current dynamic data integration methods using GDM are inefficient due to the use of numerical sensitivity calculations which limits the method to deforming two or three models at a time. In this work, we derived streamline-based analytical sensitivities for the GDM that can be obtained from a single simulation run for any number of basis models. The new Generalized Travel Time GDM (GTT-GDM) is highly efficient and achieved a performance close to regular GTT inversion while preserving the geologic structure.