Showing papers by "Akhil Datta-Gupta published in 2008"

Adaptive Multiscale Streamline Simulation and Inversion for High-Resolution Geomodels

Abstract: The topic of this thesis is streamline-based integration of dynamic data for porous media systems, particularly in petroleum reservoirs. In the petroleum industry the integration of dynamic data is usually referred to as history matching. The thesis starts out by giving an introduction to streamline-based history-matching methods. Implementations and extensions of two existing methods for streamline-based history matching are then presented.The first method pursued is based on obtaining modifications for streamline-effective properties, which subsequently are propagated to the underlying simulation grid for further iterations. For this method, two improvements are proposed to the original existing method. First, the improved approach involves less approximations, enables matching of porosity, and can account for gravity. Second, a multiscale approach is applied for which the data integration is performed on a hierarchy of coarsened grids. The approach proved robust, and gave a faster and better match to the data.The second method pursued is the so-called generalized travel-time inversion (GTTI) method, which earlier has proven very robust and efficient for history matching. The key to the efficiency of this method is the quasilinear convergence properties and the use of analytic streamline-based sensitivity coefficients. GTTI is applied together with an efficient multiscale-streamline simulator, where the pressure solver is based on a multiscale mixed finite-element method (MsMFEM). To make the history matching more efficient, a selective work-reduction strategy, based on the sensitivities provided by the inversion method, is proposed for the pressure solver. In addition, a method for improved mass conservation in streamline simulation is applied, which requires much fewer streamlines to obtain accurate production-response curves. For a reservoir model with more than one million grid blocks, 69 producers and 32 injectors, the data integration took less than twenty minutes on a standard desktop computer. Finally, we propose an extension of GTTI to fully unstructured grids, where we in particular address issues regarding regularization and computation of sensitivities on unstructured grids with large differences in cell sizes.

Modeling Time Lapse Seismic Monitoring of CO2 Sequestration in Hydrocarbon Reservoirs Including Compositional and Geochemical Effects

Abstract: The sequestration of CO2 into geologic formations, specifically existing and depleted oil and gas reservoirs, is a promising solution for reducing environmental hazards from the release of greenhouse gases into the earth's atmosphere. A critical component of long-term sequestration will be our ability to adequately monitor the movement of CO2 fronts in the subsurface. In this article, we examine the viability of time-lapse seismic monitoring using an integrated modeling of fluid flow, including chemical reactions and seismic response. Modeling of CO2 injection is complicated by the various interactions between CO2, reservoir fluids, and the minerals in the formation. These interactions change fluid and bulk rock properties with time, which in turn impact the seismic signatures. We perform a comprehensive simulation of the gas injection process accounting for the phase behavior of CO2-reservoir fluids, the associated precipitation/dissolution reactions, and the accompanying changes in porosity and...

Modified Markov Chain Monte Carlo Method for Dynamic Data Integration Using Streamline Approach

Abstract: In this paper, the Markov Chain Monte Carlo (MCMC) approach is used for sampling of the permeability field conditioned on the dynamic data. The novelty of the approach consists of using an approximation of the dynamic data based on streamline computations. The simulations using the streamline approach allows us to obtain analytical approximations in the small neighborhood of the previously computed dynamic data. Using this approximation, we employ a two-stage MCMC approach. In the first stage, the approximation of the dynamic data is used to modify the instrumental proposal distribution. The obtained chain correctly samples from the posterior distribution; the modified Markov chain converges to a steady state corresponding to the posterior distribution. Moreover, this approximation increases the acceptance rate, and reduces the computational time required for MCMC sampling. Numerical results are presented.

Multiscale-Streamline Simulation and Dynamic Data Integration for High-Resolution Subsurface Models

Abstract: Multiscale-Streamline Simulation and Dynamic Data Integration for High-Resolution Subsurface Models

The Role of Streamline Models for Dynamic Data Assimilation in Petroleum Engineering and Hydrogeology

Abstract: Geological models derived from static data alone often fail to reproduce the dynamic response from the aquifers, for example transient pressure or tracer response or from petroleum reservoirs, for example multiphase production history such as water cut or gas-oil ratio. Reconciling geologic models to the dynamic response of the reservoir is critical for subsurface characterization and building reliable reservoir performance models. Available information on subsurface heterogeneity can be broadly categorized into two major types: static and dynamic. Static data are time-invariant direct or indirect measurements of reservoir properties, such as cores, well logs, and 3-D seismic data. With recent advances in reservoir characterization, these data can now be integrated efficiently into coherent 3-D reservoir descriptions (Dubrule, 1998). Dynamic data are the time dependent measurements of flow responses such as pressure, flow rate, fractional flow and, with the use of 4-D seismic, time-lapse saturation and pressure. Integration of dynamic data generally leads to an inverse problem and requires solution of the flow equations several times using an iterative procedure (Hyndman et al., 1994; Kitanidis, 1995; Mclaughlin and Townley, 1996; Medina and Carrera, 1996; Anderman and Hill, 1999; Vasco and Datta-Gupta, 1999; Yeh and Liu, 2000; Oliver et al., 2001). The process is commonly referred to as “history matching” and is usually the most tedious and time-consuming aspect of subsurface flow and transport simulation study. Streamline models offer some unique advantages in history matching (Vasco et al., 1999; Datta-Gupta et al., 2002; He et al., 2006). In this chapter we will explore the properties of streamline models that make them particularly attractive for the integration of production data into high resolution geologic models. The streamline approach has provided an extremely efficient means for computing parameter sensitivities which define the relationship between subsurface parameters and production response (Datta-Gupta et al., 2001). The streamline-based sensitivity computation has been extended to include gravity, changing field conditions and more recently to fractured