bluebonnet.forecast.forecast_pressure
=====================================

.. py:module:: bluebonnet.forecast.forecast_pressure

.. autoapi-nested-parse::

   Forecast when bottomhole/fracface pressure is known and varying.



Functions
---------

.. autoapisummary::

   bluebonnet.forecast.forecast_pressure.fit_production_pressure
   bluebonnet.forecast.forecast_pressure.plot_production_comparison


Module Contents
---------------

.. py:function:: fit_production_pressure(prod_data: pandas.DataFrame, pvt_table: pandas.DataFrame, pressure_initial: float, filter_window_size: int | None = None, pressure_imax: float = 15000, inplace_max: float = 100000, filter_zero_prod_days: bool = True, n_iter: int = 100, params: lmfit.Parameters | None = None) -> lmfit.Parameters

   Fit cumulative production given fracface pressure.

   :param prod_data: contains columns 'Days', 'Gas', and 'Pressure'
   :type prod_data: pd.DataFrame
   :param pvt_table: information on equation of state, for example from build_pvt_gas
   :type pvt_table: pd.DataFrame
   :param pressure_initial: guess for initial reservoir pressure
   :type pressure_initial: float
   :param filter_window_size: If not None, boxcar filter size to average pressure data
   :type filter_window_size: int or None
   :param pressure_imax: maximum allowed initial reservoir pressure. pvt had better include this pressure
   :type pressure_imax: float, Optional
   :param inplace_max: Maximum allowed resource in place
   :type inplace_max: float
   :param filter_zero_prod_days: Filter out days without gas production or pressure value. Also
                                 shortens days on production to only include productive days.
   :type filter_zero_prod_days: bool
   :param n_iter: number of times to iterate until stabilizes
   :type n_iter: integer, default to 100
   :param params: Initial guesses for tau, M, and p_initial. You can pass in results from previous fit.
   :type params: Parameters

   :returns: **params** -- Best fits for tau, M, and p_initial
   :rtype: Parameters


.. py:function:: plot_production_comparison(prod_data: pandas.DataFrame, pvt_table: pandas.DataFrame, params: lmfit.Parameters, filter_window_size: int | None = None, filter_zero_prod_days: bool = True, well_name: str = 'Well Name') -> Any

   Compare production to match.

   :param prod_data: contains columns 'Days', 'Gas', and 'Pressure'
   :type prod_data: pd.DataFrame
   :param pvt_table: information on equation of state, for example from build_pvt_gas
   :type pvt_table: pd.DataFrame
   :param params: fit result parameters
   :type params: Parameters
   :param filter_window_size: If not None, boxcar filter size to average pressure data
   :type filter_window_size: int or None
   :param filter_zero_prod_days: Filter out days without gas production or pressure value. Also
                                 shortens days on production to only include productive days.
   :type filter_zero_prod_days: bool
   :param params: Best fits for tau, M, and p_initial
   :type params: Parameters
   :param well_name: name to label production with
   :type well_name: str

   :returns: **fig, (ax1, ax2)** -- matplotlib figure and tuple of axes with cumulative production and
             pressure over time (scaled by tau)
   :rtype: Any