Paul van den Hoek

Coupled Static / Dynamic Modeling For Improved Uncertainty Handling

In the petroleum industry history-matched reservoir models are used to aid the field development decision-making process. Traditionally, models have been history-matched by reservoir engineers in the dynamic domain only. Ideally, if any changes are required to static parameters as result of history matching the dynamic model, then these should be reflected directly in the static reservoir model. This permits consistency between the static and dynamic domain. In addition, static model uncertainties are often not evaluated in the dynamic domain, which could result in the detailed modeling of geological features that have no impact on the dynamic behavior and the resulting development decision. This paper demonstrates a workflow where the reservoir simulator and static modeling package are closely linked to promote a more integrated approach and to enhance the interaction between the subsurface disciplines. Using either the simulator or the static modeling package as the platform, the output of the workflow is a sensitivity analysis of the uncertainties related to structure, rock properties, fluids and rock-fluid interactions. Next, computer-assisted history matching methods (i.e. adjoint-based and Design of Experiments) are used to find the parameter values that result in a successful history match. The workflow will be demonstrated both on a synthetic model and on a reservoir model from a real field case. This methodology results in history-matched models and a better understanding of the static and dynamic subsurface uncertainties, leading to more informed decision-making. The method presented here can significantly enhance the awareness of the impact of both static and dynamic subsurface uncertainties on development decisions. In addition, it offers a platform where all subsurface professionals can more optimally combine their efforts to improve the integrated understanding of reservoirs.

Adjoint-based History-Matching of Production and Time-lapse Seismic Data

Time-lapse (4D) seismic attributes can provide valuable information on the fluid flow within subsurface reservoirs. This spatially-rich source of information complements the poor areal information obtainable from production well data. While fusion of information from the two sources holds great promise, in practice, this task is far from trivial. Joint Inversion is complex for many reasons, including different time and spatial scales, the fact that the coupling mechanisms between the various parameters are often not well established, the localized nature of the required model updates, and the necessity to integrate multiple data. These concerns limit the applicability of many data-assimilation techniques. Adjoint-based methods are free of these drawbacks but their implementation generally requires extensive programming effort. In this study we present a workflow that exploits the adjoint functionality that modern simulators offer for production data to consistently assimilate inverted 4D seismic attributes without the need for re-programming of the adjoint code. Here we discuss a novel workflow which we applied to assimilate production data and 4D seismic data from a synthetic reservoir model, which acts as the real yet unknown reservoir. Synthetic production data and 4D seismic data were created from this model to study the performance of the adjoint-based method. The seamless structure of the workflow allowed rapid setup of the data assimilation process, while execution of the process was reduced significantly. The resulting reservoir model updates displayed a considerable improvement in matching the saturation distribution in the field. This work was carried out as part of a joint Shell-IBM research project. Introduction In history matching, production measurements are assimilated to obtain a dynamical reservoir model that is consistent with historical data; see e.g. Oliver et al (2008). However, production measurements – although generally of a high temporal resolution – provide only very localized spatial information about the subsurface around the wells, especially in the early production phase when wateror gas-breakthrough has not yet occurred in the producers. After breakthrough, somewhat more insight can be gained into the reservoir model parameters that influence the mismatch between measured and simulated data. At that time however the benefits of using a pro-active reservoir management strategy have often diminished considerably. Interpreted time-lapse (4D) seismic data can provide information on the areal distribution of pressure and saturation changes due to fluid production or injection. The seismic data are generally more noisy and uncertain than production data, but due to the field-wide distribution of the data, very valuable additional information on the subsurface can be gathered; see e.g. Calvert (2005). In production data assimilation, the quality of the updated model is usually evaluated with a cost function defined as the summed squared error between the observations (measurements) and simulated production data, sometimes weighted by a measure of the accuracy of the observations. Ensemble Kalman filter (EnKF) methods (Naevdal et al. (2005); Evensen (2009); Aanonsen et al. (2009)), streamline-based methods (Vasco et al. (1999); Wang and Kovscek (2000).) and adjoint-based methods (Chen et al. (1974), Chavent et al. (1975); Li et al. (2003); Rodrigues (2006); Oliver et al. (2008)) are the most common data-assimilation techniques reported in literature to deal with the history matching problem. All these methods update the reservoir model using the sensitivities of a least-squares cost function with respect to model parameters, but differ in the considered measurement types, model parameters and derivation of the sensitivities. Of these three methods, the adjointbased method is the preferred method, because:

An Improved Inversion Workflow Jointly Assimilating 4D Seismic and Production Data

Description: Quantitative integration of 4D seismic data with production data into reservoir models is a challenging task. This paper tackles two key issues of the complex joint inversion workflow to improve its efficiency and accuracy. We applied two derivative free optimization (DFO) methods, namely particle swarm optimization (PSO) and Simultaneous Perturbation and Multivariate Interpolation (SPMI), and compared their performances. We tested different strategies of effectively mining information in both 4D seismic and production data. We proposed a method of choosing the different weights in data domain by utilizing sensitivity of inversion parameters to different types of data. We also tested the strategy of combining the inversion results from separate inversion runs using 4D seismic data or production data only. Application: We tested the workflow in a 3D synthetic model. Uncertain parameters for this model include relationship between porosity and permeability, and the ratios of kv to kh for different reservoir zones. The performance of PSO and SPMI are compared in terms of the evolution of objective function and estimation of uncertain parameters. We also provide recommendations about when to use which method. Different strategies of optimal use of 4D seismic and production data are also applied and compared using this model. The learning is also applied to a deepwater turbidite field. Results, Observations, Conclusions: Both PSO and SPMI are effective DFO methods and deliver good results for 4D seismic history matching problems. The complementary features of these two methods can ensure both applicability and efficiency of this joint inversion workflow. Choosing proper weights in either data or model domain can improve the accuracy of this workflow. Significance of Subject Matter: By solving the two key issues of jointly assimilating 4D seismic and production data, we deliver reliable workflow for reservoir model characterization and management.