Abstract:
A method for determining SRV and EUR includes: monitoring an amount and a density of a hydrocarbon fluid produced from the production well; obtaining a cumulative amount of the fluid that has accumulated from a beginning of production; obtaining a relationship between the cumulative amount and a square root of the time; determining a deviation point where the relationship changes from linear to non-linear; determining a deviation amount of the fluid corresponding to the deviation point; determining a first density of the hydrocarbon fluid at the beginning of production, a second density at a pore pressure equal to a bottom hole pressure in the production well, a first porosity at the beginning of production, and a second porosity for a pore pressure equal to the bottom hole pressure; and determining SRV and the EUR based on the deviation amount, the first and second densities, and the first and second porosities.
Abstract:
A method for determining SRV and EUR includes: monitoring an amount and a density of a hydrocarbon fluid produced from the production well; obtaining a cumulative amount of the fluid that has accumulated from a beginning of production; obtaining a relationship between the cumulative amount and a square root of the time; determining a deviation point where the relationship changes from linear to non-linear; determining a deviation amount of the fluid corresponding to the deviation point; determining a first density of the hydrocarbon fluid at the beginning of production, a second density at a pore pressure equal to a bottom hole pressure in the production well, a first porosity at the beginning of production, and a second porosity for a pore pressure equal to the bottom hole pressure; and determining SRV and the EUR based on the deviation amount, the first and second densities, and the first and second porosities.
Abstract:
A method for predicting well production is disclosed. The method includes obtaining a training data set for a machine learning (ML) model that generates predicted well production data based on observed data of interest, generating multiple sets of initial guesses of model parameters of the ML model, using an ML algorithm applied to the training data set to generate multiple individually trained ML models based the multiple sets of initial model parameters, comparing a validation data set and respective predicted well production data of the individually trained ML models to generate a ranking, selecting top-ranked individually trained ML models based on the ranking, using the data of interest as input to the top-ranked individually trained ML models to generate a set of individual predicted well production data, and generating a final predicted well production data based on the set of individual predicted well production data.
Abstract:
The present invention relates to methods for analyzing and modeling natural gas flow in subterranean shale reservoirs. In preferred embodiments, methodologies and techniques for determining and modeling natural gas flow in shale formations using methodologies and techniques capable of determining natural gas properties related to dual-continuum flow, permeability and pressure within a subterranean shale reservoir. In some embodiments, the natural gas properties are determined by subjecting a subterranean shale reservoir sample to pulse-decay analysis. In certain embodiments, the methodologies and techniques described herein may be used in various reservoirs exhibiting macroporosity and/or microporosity, such as fractured reservoirs and carbonate reservoirs composed of reservoir fluids.
Abstract:
Methods and systems are disclosed. The methods may include determining a first sequence of permeabilities by subjecting a rock sample to a first sequence of confining stress, axial stress, pore pressure (CSASPP) triplets and determining a first rock parameter using the first sequence of permeabilities, the first sequence of CSASPP triplets, and a first permeability model. The methods may further include determining a second sequence of permeabilities by subjecting the rock sample to a second sequence of CSASPP triplets and determining a second rock parameter using the second sequence of permeabilities, the second sequence of CSASPP triplets, and the first permeability model. The method may still further include determining an in situ permeability for an in situ rock based on an initial permeability, a second stress sensitivity parameter, a first stress sensitivity parameter, a confining stress value, axial stress values, a pore pressure value, and a second permeability model.
Abstract:
A method for determining a matrix permeability of a subsurface formation, including the steps: acquiring a core from the subsurface formation, imposing a fluid to the core until the core is saturated with the fluid, conducting a pressure-pulse decay (PD) method on an upstream and a downstream side of the core by applying a pressure-pulse on the upstream and the downstream side of the core, and determining the matrix permeability from decays of the pressure-pulses on the upstream side and downstream side, respectively.
Abstract:
A method for determining stress-dependent permeability includes providing a core sample in a pressurized core container of a testing apparatus and generating a steady-state flow of gas from an upstream reservoir in the testing apparatus along an axial direction through the pressurized core container into a downstream reservoir in the testing apparatus. During the steady-state flow, an inlet pressure at an inlet to the core sample, an outlet pressure at an outlet of the core sample, and a midpoint pressure at a midpoint of the core sample are measured. The stress-dependent permeability is calculated from a flow rate of the gas through the core sample and the measurements of the inlet pressure, the outlet pressure, and the midpoint pressure.
Abstract:
The disclosure relates to methods for determining imbibition of hydraulic fracturing fluids into hydrocarbon-bearing formations. More specifically, the disclosure relates to laboratory methods for determining certain unconventional flow parameters to measure the imbibition over time of hydraulic fracturing fluids into a low-permeability hydrocarbon-bearing rock formation.
Abstract:
Methods and systems are disclosed. The methods may include obtaining, from a subterranean region of interest, a rock sample having a rock type and defining a sequence of pore pressure, confining stress (PPCS) pairs such that a sequence of effective stresses monotonically changes. The methods may further include determining a sequence of permeabilities by subjecting the rock sample to the sequence of PPCS pairs and determining a relationship between the sequence of PPCS pairs and the sequence of permeabilities. The methods may further still include determining a parameter using the relationship and a permeability model, where the permeability model includes the parameter and determining an in situ permeability for an in situ rock in the subterranean region of interest using, at least in part, the parameter and the permeability model, where the in situ rock is of the rock type.
Abstract:
A method may include obtaining reservoir data, hydraulic fracturing data, and static wellbore data for a geological region of interest. The method may further include obtaining temporal production data for the geological region of interest. The temporal production data may include a predetermined production rate with respect to a predetermined period of time. The method may further include determining various temporal features based on the temporal production data and an extraction process. The extraction process may include a deconvolution function that separates a portion of the temporal features from the predetermined production rate. The method may further include determining, using a machine-learning model, predicted hydrocarbon-in-place (HIP) data for the geological region of interest using the reservoir data, the hydraulic fracturing data, the static wellbore data, and the temporal features. The method may further include transmitting a command to a well control system based on the predicted HIP data.