Abstract: A coarse grid model with a plurality of grid cells in a plurality of layers is provided. Model data are provided for a reservoir region of interest, and a plurality of pressure values are determined for the grid cells corresponding to a wellbore and for those not corresponding to the wellbore. A flowrate is determined at the grid cells corresponding to the wellbore based on the pressure values and on a flowrate metric. The flowrate metric is a function of well index, a pressure quantity, and a mobility variable, where the mobility variable is a non-linear function of gas condensate saturation and pressure. Also determined is a subset of the grid cells not corresponding to the wellbore where a pressure value is less than dew pressure. A flowrate for the subset of the one or more grid cells is determined based on the pressure values and on the flowrate metric.

Abstract: A method to improve production of condensate is disclosed. The method includes obtaining a condensate fluids sample from a gas condensate reservoir, generating, from a laboratory pressure, volume and temperature (PVT) experiment of the condensate fluids sample, a liquid dropout curve, performing simulation of the laboratory PVT experiment based on Equations of State (EoS) of the condensate to generate a simulated liquid dropout curve, where the EoS is adjusted to match the simulated liquid dropout curve and the liquid dropout curve generated by the laboratory PVT experiment, performing, based on the adjusted EoS, a reservoir simulation of the gas condensate reservoir under injection of flue gas, where the reservoir simulation models a condensate banking phenomenon to generate an optimal flue gas ratio that maximizes a measure of condensate production, and facilitating, based on the optimal flue gas ratio, the production of the condensate in the gas condensate reservoir.

Abstract: A method includes obtaining a log of the reservoir, determining, by a computer processor, a log porosity-thickness for each segment of the log of the reservoir, determining a test permeability-thickness relating to each segment of the log of the reservoir using a dynamic pressure transient analysis, determining a relationship between the test permeability-thickness and the log porosity-thickness, determining a model flow capacity and a model storage capacity for the model, determining a calculated flow capacity using the model storage capacity and the relationship, determining a ratio of the calculated flow capacity to the model flow capacity, correcting the model by applying the ratio to each cell, and planning and executing a reservoir production plan based on the model.

Abstract: A method may obtain static reservoir data for a grid model. The method may further include determining, using the static reservoir data, dynamic reservoir data for the grid model. The method may further include determining various storage capacities and various flow capacities for various model layers within the grid model using the static reservoir data. The method may further include determining various upscaling groups among the model layers based on the flow capacities and the storage capacities. The method may further include generating up scaled static data using the upscaling groups, the static reservoir data, and the grid model. The method may further include generating upscaled dynamic data using the upscaling groups, the dynamic reservoir data, and the grid model. The method may further include performing a reservoir simulation using a coarsened grid model including the upscaled static data and the upscaled dynamic data.

Abstract: A method includes obtaining, using a computer processor, reservoir data and downtime factors for a well, determining, using the computer processor, an absolute potential flow rate for the well using the reservoir data, uniformly reducing, using the computer processor, an initial productivity index of the well to create a reduced productivity index having a productivity index multiplier, determining, using the computer processor, a relationship between the productivity index multiplier and each downtime factor for the well, and predicting production losses, using the relationship and the computer processor, of a field of wells as a result of the downtime factors.

Abstract: Provided in this disclosure are systems and methods for selection of petrophysical techniques to model reservoirs. Reservoir properties were calculated using two techniques—a deterministic technique and an optimizing petrophysics technique, and simulation models were developed. The technique that yielded a simulation model that aligned more accurately and consistently with the reservoir data was selected to further develop the reservoir model.

Abstract: Provided in this disclosure are systems and methods for selection of petrophysical techniques to model reservoirs. Reservoir properties were calculated using two techniques—a deterministic technique and an optimizing petrophysics technique, and simulation models were developed. The technique that yielded a simulation model that aligned more accurately and consistently with the reservoir data was selected to further develop the reservoir model.