Efficient reservoir characterization using dimensionless ensemble smoother and multiple data assimilation in damaged multilayer systems

The ES-MDA has been extensively applied to address inverse problems related to oil reservoirs, leveraging Bayesian statistics as its cornerstone. This ensemble-based methodology utilizes historical reservoir data to infer its properties such as permeability and skin zone properties. In a recent study , the ES-MDA was utilized to estimate individual skin zone properties using well pressure responses as observed data. However, owing to insufficient reservoir information and the inherent nonlinearity of the problem, their findings lacked precision. This study presents a novel approach to efficiently characterize reservoir skin zones by employing an enhanced ES-MDA implementation and augmenting the observed data vector with flow-rate data. We introduce an analytical method for determining the pressure and flow rate observed at the well during an injectivity test, specifically tailored for multilayer reservoirs with skin zones, utilizing Laplace Transform. To convert the computed data to the real field, we use Stehfest’s algorithm. The analytical model serves a dual purpose: generating artificial data to represent a real field and predicting properties when coupled to the ES-MDA. The new analytical model enables the extraction of flow rates in each layer, which are then integrated as new data into the ES-MDA, thereby bolstering the estimation accuracy of targeted parameters. Both flow rate and pressure are employed as input data and, to alleviate the impact of orders of magnitude disparities on estimates, the ES-MDA is implemented in a dimensionless form. We tested the proposed methodology in four cases to display how adding the flow-rate data could improve results from a previous work. Moreover, the dimensionless ES-MDA offered skin zone properties with lower RMSE compared to the ones obtained in the mentioned study.