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Abstract Multi-rock type cores can be characterized by complex higher order connectivity relationships within an agglomerated petrophysical system. A solution that relates multiphase flow simulation in cores to time-lapse seismic properties in order to examine closed-loop 4D integration is performed at a high level on a plug. While a 4D workflow is not explicitly examined in this work, the requisite petro-elastic modeling (PEM) method based on a simulation-driven interpretation of the Gassmann equation is described and a comparison is made with its empirically derived counterpart. This work illustrates that a simulation-driven petro-elastic modeling approach can be used to generate time-dependent saturated rock properties consistent with seismic attribute description at the plug and core scales. The results demonstrate the simulation-driven approach, of a petro-elastic model embedded in a reservoir simulator, as an alternative to relating pressure and saturation from reservoir simulator-to-seismic-derived properties using a priori empirically based correlations. The method discussed in this paper maintains appreciable continuity with the results of empirically based petro-elastic methods but demonstrates differences commensurate with principal fluid differentiation capability inherent to reservoir simulator-derived data and observed time-lapse seismic response. The significance of applied multi-porosity relationships is further realized upon examination of the time-dependent petro-elastic model results.
Abstract The petro-elastic model (PEM) represents an integral component in the closed-loop calibration of integrated four-dimensional (4D) solutions incorporating time-lapse seismic, elastic and petrophysical rock property modeling, and reservoir simulation. Calibration of the reservoir simulation model is needed so that it is not only consistent with production history but also with the contemporaneous subsurface description as characterized by time-lapse seismic. The PEM requires dry rock properties in its description, which are typically derived from mechanical rock tests. In the absence of those mechanical tests, a small data challenge is posed, whereby all necessary data is not available but the value of reconciling seismic attributes to simulated production remains. A seismic inversion-constrained n-dimensional metaheuristic optimization technique is employed directly on three-dimensional (3D) geocellular arrays to determine elastic and density properties for the PEM embedded in the commercial reservoir simulator. Ill-posed dry elastic and density property models are considered in a field case where the seismic inversion and petrophysical property model constrained by seismic inversion exist. An n-dimensional design optimization technique is implemented to determine the optimal solution of a multidimensional pseudo-objective function comprised of multidimensional design variables. This study investigates the execution of a modified particle swarm optimization (PSO) method combined with an exterior penalty function (EPF) with varied constraints. The proposed technique involves using n-dimensional design optimization to solve the pseudo-objective function comprised of the PSO and EPF given limited availability of constraints. In this work, an examination of heavily and reduced-order penalized metaheuristic optimization processes, where the design variables and optimal solution are derived from 3D arrays, is conducted so that constraint applicability is quantified. While the process is examined specifically for PEM, it can be applied to other data-limited modeling techniques.
Abstract Converted wave (Sv-wave) velocity analysis approach is always a difficult problem in 3C seismic data processing. Conventional 3C velocity and image are generally computed in different time scales; PP wave is processed with PP time scale and PS wave with PS time scale. PP and PS wave data are basically processed separately causing errors in horizon calibration between PP and PS waves. Joint inversion of PP and PS reflection data has been hindered by the difficult task of registration or correlation of PP and PS events. It can perhaps be achieved by registering the events during inversion but the resulting algorithm is generally computationally intensive. In this paper, we report on a converted wave velocity analysis approach from 3C data that can image P and Sv-waves in the same PP or PS wave time scale. In fact we carry out the velocity analysis in depth domain such that common conversion points are updated at each iteration of velocity analysis. Thus mapping to PP and PS time scales is trivial. This method circumvents the horizon calibration problem in the data interpretation between PP and PS waves and image them accurately. At the same time, this method provides PP and PS wave velocities suitable for pre-stack migration. Here we also propose a stochastic inversion of PP and PS data which have been registered to the same PP time scale using a new interval velocity analysis technique. The prestack PP and PS wave joint stochastic inversion is achieved by using the PP and PS wave angle gathers using a very fast simulated annealing (VFSA) algorithm. The objective function attempts to match both PP and PS data; the starting models are drawn from fractional Gaussian distribution constructed from interpolated well logs. The proposed method has been applied to synthetic and real data; the inverted results from synthetic data inversion compare very well with model data, and inverted results for real data inversion are consistent with seismic data and log data. These also show that the proposed method has a higher accuracy for estimating rock physics parameters while it circumvents the horizon registration problem in the data interpretation. We also estimate uncertainty in our estimated results from multiple VFSA derived models. Introduction Multicomponent seismic technology offers several advantages, including reservoir characterization using PP and PS waves. It is highly effective in lithology determination and, for fluid and fracture identification. However, we are faced with the difficulties of estimating converted shear wave velocities and joint PP and PS inversion. Conventional methods for processing the PS wave data assume a simple propagation path, namely, a down going P-wave and a reflected up going shear wave. The converted wave is considered a virtual or effective wave whose velocity neither the P wave velocity nor the shear wave velocity. The PP wave is processed with the PP time scale and the PS wave with the PS time scale. PP and PS wave data are basically processed separately. The final PP and PS wave velocity gather and stack data or migration data have different travel times at the same depth, which makes horizon calibration and registration very difficult during joint inversion.
Abstract Time-lapse seismic is an important subject in the Petroleum Industry. Going beyond the qualitative interpretation of the seismic data, including quantitative information in the flow simulation model updating process, is a highly desirable goal. 4D seismic data give information far away from the wells, potentially allowing much richer parameterizations of the reservoir model in the history matching process. Since these parameterizations tend to be described by a large number of parameters, efficient algorithms are needed to tackle these problems. This paper describes some efforts to integrate time-lapse seismic attributes into a derivative-based assisted history matching tool developed in a previous project 1. The implementation of the seismic attributes derivatives using the forward and the adjoint method into a commercial reservoir flow simulator is described. The calculated derivatives are used in an optimization algorithm based on a trust-region Quasi-Newton method to minimize the mismatch between observed and simulated data from production and seismic. Good results were obtained in several synthetic cases adjusting seismic and production data. Introduction The history matching process is usually one of the most complex parts of a reservoir simulation study. Basically, the history match process tries to modify the flow simulation model input data (permeability, porosity, fault transmissibility, etc.) in order to make the model fit observed production data 2. The current industry practice is still, in most cases, a manual procedure of trial and error that requires a lot of experience and knowledge from the geoscientists involved in the study. The process is also inherently non-unique and can have several solutions. Nevertheless, the history matching procedure is essential because it ensures some legitimacy to the flow simulation model. The development of computer hardware and software allowed the appearance of several assisted history matching tools 1,3,4,5,6. These tools are based on the minimization of a least-squares objective function (OF) that quantifies the misfit between the simulated and observed data. The classical approach to history matching a reservoir model uses the production data, oil, water and gas rates and pressure measured at wells, as observed data to be fitted by the simulator. With the improvement of seismic technology, a new kind of data emerged in the oil industry. This new data consists of a series of seismic images of the reservoir taken in different moments of the production life of a petroleum field 2, usually called as time-lapse or 4D seismic. Basically, the 4D seismic may help to identify areas of bypassed oil for infill-drilling wells or locate water flooded areas, avoiding the drilling of low-performance wells in swept regions, to map gas cap areas, to identify compartmented regions in the reservoir and flow barriers. In this sense, the 4D seismic data contains relevant information of the fluid and pressure changes in the region away from the wells. Therefore, this kind of data has started to be used into history matching problems, initially in a qualitative manner, for instance, by manipulating reservoir parameters to match observed flow paths. Recently quantitative information from 4D seismic data has started to be incorporated into the history matching process. Huang et al.7,8 have presented a history matching procedure that includes production data and 4D seismic amplitudes. An application to a dry-gas reservoir model in Gulf of Mexico has shown that this approach can improve the reliability of the model predictions. The process was based on a stochastic search procedure to minimize the misfit between the simulated and observed data. Waggoner et al.9 have applied a similar approach in a gas condensate reservoir using acoustic impedances with production data in the OF. Stephen et al.10,11 have presented a method to generate multiple history matched models based on production and seismic data. In this work the misfit between the observed and simulated data was used to update the model probabilities in a Bayesian framework.
Abstract It is well-understood that hydraulic rock types categorize flow unit regimes in porous media such that modeling of these regimes according to hydraulic conductivity is possible in a reservoir simulator. This hydraulic characterization is necessary in reservoir simulation to spatially dictate multiphase flow behavior throughout the modeled reservoir. The spatial delineation of these multiphase data then relies on a singular relationship between petrophysical properties, which can be used to assign the multiphase properties to the simulation grid; the properties and requisite relationship of choice are between absolute permeability and porosity. This paper illustrates that a simulation-to-seismic modeling approach can be used to reconcile large-scale flow disparities in the dynamic reservoir model, resultant of diminished spatial constraints during the creation of the static petrophysical model with seismic attribute array data. The results demonstrate that the assignment of multiphase hydraulic descriptions to petrofacies have considerable control on simulated rates and produced volumes. The examination of selected simulation scenarios leads to the conclusion that petrofacies, when derived considering petrophysical cutoffs and hydraulic rock types, provide an alternative to incorporating depositional facies constraints during the modeling of select shale reservoirs, wherein the understanding of a regionalized modeling constraint is limited. Additionally, it is shown that geologic facies are not flow units while they are considered regionalized variables. The uncertainty associated with unconstrained petrofacies is then recognized as broad; but, it is accepted that this modeled petrophysical system allows for the described petrofacies to be used as a proxy for hydraulic conductivity in the absence of a facies-based spatial constraint. A novel approach is introduced in this work to construct petrofacies from log curves and core data to relate hydraulic rock types and petrophysical properties to the simulation grid using a methodology that honors the available seismic. The mathematical formulation used to describe petrofacies can be modest or elaborate; however, they differ from geologic facies, which can be described as regionalized variables. Unlike geologic facies, petrofacies do not adhere to interpretable geometries and thus might not be considered as regionalized variables or functionally modeled with deterministic or stochastic algorithms.