This paper presents an ensemble-based computer Assisted History Matching (AHM) of a real life carbonate oil field. The field-level reservoir pressures were matched with a fine-scale Dual-Porosity DualPermeability (DPDP) model spanning a long production history under primarily peripheral water injection pressure support. The well-level AHM workflow presented was validated with a DPDP high-resolution sector model of a fracture dominated carbonate reservoir. This sector model was ~17 million active grid cells with no application of simulation grid upscaling. The AHM workflow integrates probabilistic Bayesian inference using Ensemble Smoother with Multiple Data Assimilation (ES-MDA), which simultaneously assimilates the data and generates maximum a-posteriori updates of reservoir model parameters in a variance- minimizing update scheme. A detailed uncertainty matrix was built with ensemble of sensitivity scenarios, based on varying free water level, corresponding matrix porosity and the initial water saturation combined with geostatistical realizations of dynamic permeability derived from dynamic PLT logs and fracture characterization, where the varied parameters were the variogram attributes in terms of correlation length and geometric anisotropy. Five data assimilation iterations with ES-MDA method were required to achieve acceptable convergence and minimization of objective function, defined as a joint misfit of well-level static pressures and watercut for the key producing wells. Practical DPDP model simulation times were achieved through utilization of Massive Parallel Processing technology. This study presents the first ensemble-based approach to integrated reservoir modeling for a mature oil field with the objective to deliver geologically-constrained history matched models with better predictive value for production optimization and forecasting.
One of the most challenging tasks in the oil industry is the production of reliable reservoir forecast models. Due to different sources of uncertainties in the numerical models and inputs, reservoir simulations are often only crude approximations of the reality. This problem is mitigated by conditioning the model with data through data assimilation, a process known in the oil industry as history matching. Several recent advances are being used to improve history matching reliability, notably the use of time-lapse data and advanced data assimilation techniques. One of the most promising data assimilation techniques employed in the industry is the ensemble Kalman filter (EnKF) because of its ability to deal with non-linear models at reasonable computational cost. In this paper we study the use of crosswell seismic data as an alternative to 4D seismic surveys in areas where it is not possible to re-shoot seismic. A synthetic reservoir model is used in a history matching study designed better estimate porosity and permeability distributions and improve the quality of the model to predict future field performance.This study is divided in three parts: First, the use of production data only is evaluated (baseline for benchmark). Second, the benefits of using production and 4D seismic data are assessed. Finally, a new conceptual idea is proposed to obtain timelapse information for history matching. The use of crosswell time-lapse seismic tomography to map velocities in the interwell region is demonstrated as a potential tool to ensure survey reproducibility and low acquisition cost when compared with fullscale surface surveys. Our numerical simulations show that the proposed method provides promising history matching results leading to similar estimation error reductions when compared with conventional history matched surface seismic data.
Assisted history matching (AHM) methodologies provide a systematic approach to history match reservoir models accounting for uncertainties. It also provides sensitivity of reservoir response within the uncertainty range of parameters. There are usually large degrees of uncertainties in a simulation model, and as the simulation model becomes very large, both engineering and computational complexities associated with AHM methodologies become massive. The performance an AHM algorithm depends on its ability to provide a solution with an acceptable level of accuracy and uncertainty tolerance and computational efficiency to reach that goal. This study provides performance evaluation guidelines for AHM studies and a cost benefit metrics for feasible history matching studies of giant simulation models. These metrics will take into consideration several criteria, such as the quality of the simulation model, the requirement for compute and storage resources, time to converge to an optimal or acceptable simulation model, user friendliness and ease of integration of the tool in an existing simulation environment. The goal of this evaluation metrics is to assist reservoir engineers to identify the best class of tools and algorithms, which will be appropriate for history matching studies of simulation models. The evaluation matrices were used to evaluate two stochastic tools. One is utilizing genetic/evolutionary algorithms and the other one is using different global statistical algorithms. The study is performed using an oil field in Saudi Arabia. This study identified key strengths and shortcomings of these two classes of algorithms for large scale history matching studies. The paper demonstrates that the current metrics can serve as a suitable screening tool to identify an appropriate methodology to be used in a history matching study.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.