welltest_pta.Event

class welltest_pta.Event[source]

Bases: object

A single PTA event (drawdown or buildup) with its data slice and methods.

Parameters:

event_id (str)
event_type (str)
idx_start (int)
idx_end (int)
t_start (Timestamp)
t_end (Timestamp)
elapsed_start_hr (float)
elapsed_end_hr (float)
data (DataFrame)
rate (Optional[float])
preceding_dd_dur_hr (Optional[float])
parent_p_reservoir (Optional[float])
metadata (dict[str, Any])

__init__(event_id, event_type, idx_start, idx_end, t_start, t_end, elapsed_start_hr, elapsed_end_hr, data, rate=None, preceding_dd_dur_hr=None, parent_p_reservoir=None, metadata=<factory>)

Parameters:

event_id (str)
event_type (str)
idx_start (int)
idx_end (int)
t_start (Timestamp)
t_end (Timestamp)
elapsed_start_hr (float)
elapsed_end_hr (float)
data (DataFrame)
rate (Optional[float])
preceding_dd_dur_hr (Optional[float])
parent_p_reservoir (Optional[float])
metadata (dict[str, Any])

Return type:

None

Methods

`__init__`(event_id, event_type, idx_start, ...)
`bourdet`([L])	Return (Δt, Bourdet derivative) for this event.
`export`(path[, format])	Save this event's data slice to CSV / Excel / JSON.
`flow_regimes`([L, slope_tol])	Auto-identify flow regimes from the Bourdet derivative.
`horner`([fit_start_frac, fit_end_frac])	Horner P* extrapolation.
`mdh`([fit_start_frac, fit_end_frac])	MDH semi-log analysis.
`plot`([ax, raw])	Pressure vs time for this single event.
`plot_horner`([ax])	Horner plot (buildups only).
`plot_loglog`([ax, L])	Log–log diagnostic plot (\|ΔP\| and Bourdet derivative).
`plot_mdh`([ax])	MDH semi-log plot (buildups only).
`print`()	Pretty-print the event summary.
`reservoir_params`(q, mu, B, h, phi, ct, rw[, ...])	Compute \(k\), \(kh\), skin \(S\) (and \(C\) if early-storage slope detected) for this buildup.
`summary`()	Return a summary dictionary of this event's statistics.

Attributes

`delta_p`
`duration_hr`
`p_final`
`p_initial`
`parent_p_reservoir`
`preceding_dd_dur_hr`
`rate`
`rate_psi_per_hr`
`event_id`
`event_type`
`idx_start`
`idx_end`
`t_start`
`t_end`
`elapsed_start_hr`
`elapsed_end_hr`
`data`
`metadata`

event_id: str

event_type: str

idx_start: int

idx_end: int

t_start: Timestamp

t_end: Timestamp

elapsed_start_hr: float

elapsed_end_hr: float

data: DataFrame

rate: float | None = None

preceding_dd_dur_hr: float | None = None

parent_p_reservoir: float | None = None

metadata: dict[str, Any]

property duration_hr: float

property p_initial: float

property p_final: float

property delta_p: float

property rate_psi_per_hr: float

summary()[source]

Return a summary dictionary of this event’s statistics.

Return type:: dict[str, Any]

print()[source]

Pretty-print the event summary.

Return type:: None

export(path, format='auto')[source]

Save this event’s data slice to CSV / Excel / JSON.

Parameters:

path (str | Path)
format (str)

Return type:

None

bourdet(L=0.2)[source]

Return (Δt, Bourdet derivative) for this event.

Parameters:: L (float)
Return type:: tuple[ndarray, ndarray]

horner(fit_start_frac=0.30, fit_end_frac=0.85)[source]

Horner P* extrapolation. Buildup only.

Parameters:

fit_start_frac (float)
fit_end_frac (float)

Return type:

dict[str, float]

mdh(fit_start_frac=0.30, fit_end_frac=0.85)[source]

MDH semi-log analysis. Buildup only.

Parameters:

fit_start_frac (float)
fit_end_frac (float)

Return type:

dict[str, float]

flow_regimes(L=0.2, slope_tol=0.10)[source]

Auto-identify flow regimes from the Bourdet derivative.

Parameters:

L (float)
slope_tol (float)

Return type:

list[dict[str, Any]]

reservoir_params(q, mu, B, h, phi, ct, rw, method='horner')[source]

Compute \(k\), \(kh\), skin \(S\) (and \(C\) if early-storage slope detected) for this buildup.

Parameters:

q (float) – Fluid / reservoir / well properties (see welltest_pta.analysis.reservoir.reservoir_parameters()).
mu (float) – Fluid / reservoir / well properties (see welltest_pta.analysis.reservoir.reservoir_parameters()).
B (float) – Fluid / reservoir / well properties (see welltest_pta.analysis.reservoir.reservoir_parameters()).
h (float) – Fluid / reservoir / well properties (see welltest_pta.analysis.reservoir.reservoir_parameters()).
phi (float) – Fluid / reservoir / well properties (see welltest_pta.analysis.reservoir.reservoir_parameters()).
ct (float) – Fluid / reservoir / well properties (see welltest_pta.analysis.reservoir.reservoir_parameters()).
rw (float) – Fluid / reservoir / well properties (see welltest_pta.analysis.reservoir.reservoir_parameters()).
method (str) – "horner" (default) or "mdh".

Return type:

dict[str, float]

plot(ax=None, raw=True)[source]

Pressure vs time for this single event.

Parameters:

ax (Axes | None)
raw (bool)

plot_loglog(ax=None, L=0.2)[source]

Log–log diagnostic plot (|ΔP| and Bourdet derivative).

Parameters:

ax (Axes | None)
L (float)

plot_horner(ax=None)[source]

Horner plot (buildups only).

Parameters:: ax (Axes | None)

plot_mdh(ax=None)[source]

MDH semi-log plot (buildups only).

Parameters:: ax (Axes | None)

__init__(event_id, event_type, idx_start, idx_end, t_start, t_end, elapsed_start_hr, elapsed_end_hr, data, rate=None, preceding_dd_dur_hr=None, parent_p_reservoir=None, metadata=<factory>)

Parameters:

event_id (str)
event_type (str)
idx_start (int)
idx_end (int)
t_start (Timestamp)
t_end (Timestamp)
elapsed_start_hr (float)
elapsed_end_hr (float)
data (DataFrame)
rate (Optional[float])
preceding_dd_dur_hr (Optional[float])
parent_p_reservoir (Optional[float])
metadata (dict[str, Any])

Return type:

None