A method for determining water-filled porosity and water salinity in a well includes obtaining complex dielectric permittivity of earth formations, either from dielectric measurements representative of well cores, or from dielectric well logs; selecting a dielectric mixing law for the index number m; plotting a m-th root of complex dielectric permittivity at a specified frequency in the complex domain, wherein m is an index number; determining a matrix permittivity, a water salinity, and a water-filled porosity based on the complex dielectric permittivity and the dielectric mixing law; and displaying the water salinity and the water-filled porosity.

The present application introduces a membrane fouling warning methodology grounded in machine learning. It utilizes a machine learning-based membrane fouling prediction model to automatically forecast and generate electrochemical information values, which characterize the extent of membrane fouling at various time points, based on influent water quality parameters. It then acquires the electrochemical information values Z.sub.t at a moment t and Z.sub.++t at a moment t+t. Subsequently, it computes and assesses the respective fouling levels using the electrochemical information values derived from the membrane fouling prediction model. Finally, it issues an early warning signal contingent upon the determined warning level. This methodology facilitates proactive understanding and management of membrane fouling, thereby sustaining the normal operation of the membrane fouling treatment system, mitigating the propensity for membrane assembly fouling, and prolonging the operational lifespan of the membrane assembly.

The present application introduces a membrane fouling warning methodology grounded in machine learning. It utilizes a machine learning-based membrane fouling prediction model to automatically forecast and generate electrochemical information values, which characterize the extent of membrane fouling at various time points, based on influent water quality parameters. It then acquires the electrochemical information values Z.sub.t at a moment t and Z.sub.t+t at a moment t+t. Subsequently, it computes and assesses the respective fouling levels using the electrochemical information values derived from the membrane fouling prediction model. Finally, it issues an early warning signal contingent upon the determined warning level. This methodology facilitates proactive understanding and management of membrane fouling, thereby sustaining the normal operation of the membrane fouling treatment system, mitigating the propensity for membrane assembly fouling, and prolonging the operational lifespan of the membrane assembly.

Systems and methods for obtaining a calibrated permittivity dispersion measurements of a subsurface formation by measuring an impedance of the subsurface formation using a borehole imager at a first one or more frequencies; measuring a permittivity of the subsurface formation using a reference tool at a second one or more frequencies; calculating a first dispersion curve of the permittivity of the subsurface formation based at least in part on the measured impedance of the subsurface formation at the first one or more frequencies; extrapolating the permittivity of the subsurface formation to the second one or more frequencies using the calculated first dispersion curve of the permittivity of the subsurface formation; calibrating the permittivity of the subsurface formation based at least in part on the extrapolated permittivity of the subsurface formation and the measured permittivity of the subsurface formation; and generating a second dispersion curve of the permittivity of the subsurface formation based at least in part on one or more of the calibrated permittivity of the subsurface formation at the first one or more frequencies and the measured permittivity of the subsurface formation at the second one or more frequencies.

The present disclosure describes a system that can enable the prediction of coronary flow without invasive medical procedure. The system can generate physical models that can provide an accurate assessment of coronary mechanics and enable realistic simulation of coronary procedures. The models can enable the hemodynamic measurement of flow through the model and the study of flow dynamics through the model and the biomechanics of the model.

The present technology generally relates to a porous medium parameter measurement device comprising one or more component selected from: a porous conductive component; a porous non-conductive component; and a selective component. The one or more component is in operative communication with each one of the one or more component and with a porous medium through a plurality of pores allowing a porous medium solution to reach diffusion equilibrium between the porous medium and each of the one or more component. The one or more component allows direct measurement of a multiplicity of parameters of the porous medium solution.