Measuring relative permeabilities and capillary pressures on core samples in the laboratory is both a lengthy and costly process. Wireline formation testers (WFT) are routinely used in the field to measure pressures and collect fluid samples. Commonly available data from WFT operations carry information about the multiphase flow properties of the formation. By utilizing in-situ WFT measurements together with a flow model and an optimization engine, we propose in this paper a new methodology for estimation of relative permeability and capillary pressure (PC).
The proposed methodology consists of a numerical forward model describing the mud filtrate invasion and fluid sampling processes while accurately accounting for WFT tool geometry and internal tool storage and fluid segregation effects. The forward model is embedded in an optimization workflow where relative permeabilities, capillary pressures, damage skin, and depth of mud filtrate invasion are estimated by minimizing a misfit function between measured and modeled pressures and water-cuts. The relative permeability and PC curves are parameterized using industry accepted models. The optimization workflow uses a distribution function of response parameters where the entire parameter range is included in the numerical runs, thus ensuring that a global optimum is found. Initial parameter estimates are determined from open-hole logs, such as resistivity, dielectric, nuclear magnetic resonance, as well as from pressure transient analysis.
The methodology developed in this paper is validated by application to a synthetic dataset with a known solution, and it is subsequently demonstrated on actual field data from a WFT sampling operation. The results of this paper demonstrate that it is possible to reliably estimate multiphase flow properties from WFT sampling data. The key contributions of this study are to show the capability of estimating a variety of multiphase flow properties from in-situ WFT cleanup measurements and to establish an automated approach, including a novel inversion methodology, to reduce the turnaround time.