Abstract:
A computing system includes a processor that estimates a pattern of a flow of a mixture of particles and a fluid in a tubular structure as a stationary bed flow, a dispersed flow, or a transitional flow that is relative to the stationary bed and dispersed flows. The processor estimates a plurality of parameters based on the estimated pattern. The processor determines a plurality of dimensionless parameters, based on the estimated parameters. The dimensionless parameters include a first dimensionless parameter corresponding to an effect of turbulence on the flow and a second dimensionless parameter corresponding to an effect of gravity on the flow. The processor characterizes the pattern of the flow as the stationary bed flow, the dispersed flow, or the transitional flow, based on the dimensionless parameters. The processor models the flow based on the estimated pattern if it is determined that the characterized pattern matches the estimated pattern.
Abstract:
Methods and systems are presented in this disclosure for performing fast economic analysis of production by fracture-stimulated wells. A class of models can be defined by combining a simulated fracture geometry comprising a stimulated reservoir volume with accounting for fluid dynamics and phase transitions in the stimulated reservoir volume for modeling production in a plurality of reservoirs. An objective function related to the production in the plurality of reservoirs can be generated based on at least one model from the class of models. Parameters related to fracture stages of a fracture network can be then determined, based on the objective function, and communicated, via a computer network to a computing device, to be used for at least one of building or operating the fracture network in the reservoir.
Abstract:
Methods and systems are presented in this disclosure for accurate modeling of near-field formation in wellbore simulations. The approach presented herein is based on splitting a transient three-dimensional solution of finding heat and mass transfer parameters in a wellbore and a near-wellbore region into coupling modeling of a flow inside the wellbore with several transient two-dimensional solutions in the vicinity to the wellbore.
Abstract:
Methods and systems are presented in this disclosure for evaluation of formation properties (e.g., permeability, saturation) based on interpretation of data obtained by production logging tools (PLTs). Based on the PLT data, a production rate for a component (e.g., production fluid) produced by a wellbore can be determined, and a distribution of a property of the component can be initialized along a length of the wellbore. A simulated production rate for the component can be calculated, based on the distribution of the property using a simulator for the wellbore. The distribution of the property can be iteratively adjusted based on the production rate and the simulated production rate, until convergence of the distribution for two consecutive iterations is achieved. A reservoir formation model used for operating the wellbore can be updated based on the adjusted distribution of the property of the component.
Abstract:
A system includes a processor. The processor estimates a pattern of a flow of a mixture of drilling fluid and cuttings in an annulus of a wellbore. The flow is estimated as a stationary bed flow, a dispersed flow, or a transitional flow relative to the stationary bed and dispersed flows. The processor estimates parameters based on the estimated pattern of the flow, and determines a plurality of dimensionless parameters including a first dimensionless parameter corresponding to an effect of turbulence on the flow and a second dimensionless parameter corresponding to an effect of gravity on the flow, based on the estimated parameters. The processor characterizes the pattern of the flow as the stationary bed flow, the dispersed flow, or the transitional flow, based on the dimensionless parameters, and models the flow based on the estimated pattern if it is determined that the characterized pattern matches the estimated pattern.