bestfit: Best-fitting of Arps decline curves
In aRpsDCA: Arps Decline Curve Analysis in R

Description Usage Arguments Details Value See Also Examples

Perform best-fits of Arps decline curves to rate or cumulative data.

best.exponential(q, t,
  lower=c( # lower bounds
    0, # qi > 0
    0), # D > 0
  upper=c( # upper bounds
    max(q) * 5, # qi < qmax * 5
    10) # = 0.99995 / [time] effective
  )

best.hyperbolic(q, t,
  lower=c( # lower bounds
    0,  # qi > 0
    0,  # Di > 0
    0), # b > 0
  upper=c( # upper bounds
    max(q) * 5, # qi < qmax * 5
    10, # = 0.99995 / [time] effective
    2)  # b <= 2.0
  )

best.hyp2exp(q, t,
  lower=c( # lower bounds
    0,  # qi > 0
    0.35,  # Di > 0
    0,  # b > 0
    0), # Df > 0
  upper=c( # upper bounds
    max(q) * 5, # qi < qmax * 5
    10, # = 0.99995 / [time] effective
    2,  # b <= 2.0
    0.35) # Df <= 0.35
  )

best.exponential.curtailed(q, t,
  lower=c( # lower bounds
    0, # qi > 0
    0, # D > 0
    0  # t.curtail > 0
  ),
  upper=c( # upper bounds
    max(q) * 5, # qi < qmax * 5
    10, # = 0.99995 / [time] effective
    t[length(t)])
  )

best.hyperbolic.curtailed(q, t,
  lower=c( # lower bounds
    0,  # qi > 0
    0,  # Di > 0
    0,  # b > 0
    0   # t.curtail > 0
  ),
  upper=c( # upper bounds
    max(q) * 5, # qi < qmax * 5
    10, # = 0.99995 / [time] effective
    2,  # b <= 2.0
    t[length(t)])
  )

best.hyp2exp.curtailed(q, t,
  lower=c( # lower bounds
    0,  # qi > 0
    0.35,  # Di > 0
    0,  # b > 0
    0,  # Df > 0
    0   # t.curtail > 0
  ),
  upper=c( # upper bounds
    max(q) * 5, # qi < qmax * 5
    10, # = 0.99995 / [time] effective
    2,  # b <= 2.0
    0.35, # Df <= 0.35
    t[length(t)])
  )

best.fit(q, t)

best.curtailed.fit(q, t)

best.exponential.from.Np(Np, t,
  lower=c( # lower bounds
    0, # qi > 0
    0), # D > 0
  upper=c( # upper bounds
    max(c(Np[1], diff(Np)) / diff(c(0, t))) * 5, # qi < max(rate) * 5
    10) # = 0.99995 / [time] effective)
  )

best.exponential.from.interval(volume, t, t.begin=0.0,
  lower=c( # lower bounds
    0, # qi > 0
    0), # D > 0
  upper=c( # upper bounds
    max(volume / diff(c(t.begin, t))) * 5, # qi < max(rate) * 5
    10) # = 0.99995 / [time] effective)
  )

best.hyperbolic.from.Np(Np, t,
  lower=c( # lower bounds
    0,  # qi > 0
    0,  # Di > 0
    0), # b > 0
  upper=c( # upper bounds
    max(c(Np[1], diff(Np)) / diff(c(0, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    2)  # b <= 2.0
  )

best.hyperbolic.from.interval(volume, t, t.begin=0.0,
  lower=c( # lower bounds
    0,  # qi > 0
    0,  # Di > 0
    0), # b > 0
  upper=c( # upper bounds
    max(volume / diff(c(t.begin, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    2)  # b <= 2.0
  )

best.hyp2exp.from.Np(Np, t,
  lower=c( # lower bounds
    0,  # qi > 0
    0.35,  # Di > 0
    0,  # b > 0
    0), # Df > 0
  upper=c( # upper bounds
    max(c(Np[1], diff(Np)) / diff(c(0, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    5,  # b <= 2.0
    0.35) # Df <= 0.35
  )

best.hyp2exp.from.interval(volume, t, t.begin=0.0,
  lower=c( # lower bounds
    0,  # qi > 0
    0.35,  # Di > 0
    0,  # b > 0
    0), # Df > 0
  upper=c( # upper bounds
    max(volume / diff(c(t.begin, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    5,  # b <= 2.0
    0.35) # Df <= 0.35
  )

best.exponential.curtailed.from.Np(Np, t,
  lower=c( # lower bounds
    0, # qi > 0
    0, # D > 0
    0  # t.curtail > 0
  ),
  upper=c( # upper bounds
    max(c(Np[1], diff(Np)) / diff(c(0, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    t[length(t)])
  )

best.exponential.curtailed.from.interval(volume, t, t.begin=0.0,
  lower=c( # lower bounds
    0, # qi > 0
    0, # D > 0
    0  # t.curtail > 0
  ),
  upper=c( # upper bounds
    max(volume / diff(c(t.begin, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    t[length(t)])
  )

best.hyperbolic.curtailed.from.Np(Np, t,
  lower=c( # lower bounds
    0,  # qi > 0
    0,  # Di > 0
    0,  # b > 0
    0   # t.curtail > 0
  ),
  upper=c( # upper bounds
    max(c(Np[1], diff(Np)) / diff(c(0, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    5,  # b <= 2.0
    t[length(t)])
  )

best.hyperbolic.curtailed.from.interval(volume, t, t.begin=0.0,
  lower=c( # lower bounds
    0,  # qi > 0
    0,  # Di > 0
    0,  # b > 0
    0   # t.curtail > 0
  ),
  upper=c( # upper bounds
    max(volume / diff(c(t.begin, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    5,  # b <= 2.0
    t[length(t)])
  )

best.hyp2exp.curtailed.from.Np(Np, t,
  lower=c( # lower bounds
    0,  # qi > 0
    0.35,  # Di > 0
    0,  # b > 0
    0,  # Df > 0
    0
  ),
  upper=c( # upper bounds
    max(c(Np[1], diff(Np)) / diff(c(0, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    5,  # b <= 2.0
    0.35, # Df <= 0.35
    t[length(t)])
  )

best.hyp2exp.curtailed.from.interval(volume, t, t.begin=0.0,
  lower=c( # lower bounds
    0,  # qi > 0
    0.35,  # Di > 0
    0,  # b > 0
    0,  # Df > 0
    0
  ),
  upper=c( # upper bounds
    max(volume / diff(c(t.begin, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    5,  # b <= 2.0
    0.35, # Df <= 0.35
    t[length(t)])
  )

best.fit.from.Np(Np, t)

best.fit.from.interval(volume, t, t.begin=0.0)

best.curtailed.fit.from.Np(Np, t)

best.curtailed.fit.from.interval(volume, t, t.begin=0.0)

best.exponential.with.buildup(q, t,
  lower=c( # lower bounds
    0,  # qi > 0
    0), # D > 0
  upper=c( # upper bounds
    max(q) * 5, # qi < qmax * 5
    10),        # = 0.99995 / [time] effective
  initial.rate=q[1], time.to.peak=t[which.max(q)])

best.hyperbolic.with.buildup(q, t,
  lower=c( # lower bounds
    0,  # qi > 0
    0,  # Di > 0
    0), # b > 0
  upper=c( # upper bounds
    max(q) * 5, # qi < qmax * 5
    10, # = 0.99995 / [time] effective
    2), # b <= 2.0
  initial.rate=q[1], time.to.peak=t[which.max(q)])

best.hyp2exp.with.buildup(q, t,
  lower=c( # lower bounds
    0,  # qi > 0
    0.35,  # Di > 0
    0,  # b > 0
    0), # Df > 0
  upper=c( # upper bounds
    max(q) * 5, # qi < qmax * 5
    10, # = 0.99995 / [time] effective
    2,  # b <= 2.0
    0.35), # Df <= 0.35
  initial.rate=q[1], time.to.peak=t[which.max(q)])

best.fit.with.buildup(q, t)

best.exponential.from.Np.with.buildup(Np, t,
  lower=c( # lower bounds
    0, # qi > 0
    0), # D > 0
  upper=c( # upper bounds
    max(c(Np[1], diff(Np)) / diff(c(0, t))) * 5, # qi < max(rate) * 5
    10), # = 0.99995 / [time] effective
  initial.rate=Np[1] / t[1],
  time.to.peak=(t[which.max(diff(Np))] + t[which.max(diff(Np)) + 1]) / 2.0)

best.exponential.from.interval.with.buildup(volume, t, t.begin=0.0,
  lower=c( # lower bounds
    0, # qi > 0
    0), # D > 0
  upper=c( # upper bounds
    max(volume / diff(c(t.begin, t))) * 5, # qi < max(rate) * 5
    10), # = 0.99995 / [time] effective
  initial.rate=volume[1] / (t[1] - t.begin),
  time.to.peak=(t - diff(c(t.begin, t)) / 2)[which.max(volume)])

best.hyperbolic.from.Np.with.buildup(Np, t,
  lower=c( # lower bounds
    0,  # qi > 0
    0,  # Di > 0
    0), # b > 0
  upper=c( # upper bounds
    max(c(Np[1], diff(Np)) / diff(c(0, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    2), # b <= 2.0
  initial.rate=Np[1] / t[1],
  time.to.peak=(t[which.max(diff(Np))] + t[which.max(diff(Np)) + 1]) / 2.0)

best.hyperbolic.from.interval.with.buildup(volume, t, t.begin=0.0,
  lower=c( # lower bounds
    0,  # qi > 0
    0,  # Di > 0
    0), # b > 0
  upper=c( # upper bounds
    max(volume / diff(c(t.begin, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    2), # b <= 2.0
  initial.rate=volume[1] / (t[1] - t.begin),
  time.to.peak=(t - diff(c(t.begin, t)) / 2)[which.max(volume)])

best.hyp2exp.from.Np.with.buildup(Np, t,
  lower=c( # lower bounds
    0,  # qi > 0
    0.35,  # Di > 0
    0,  # b > 0
    0), # Df > 0
  upper=c( # upper bounds
    max(c(Np[1], diff(Np)) / diff(c(0, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    5,  # b <= 2.0
    0.35), # Df <= 0.35
  initial.rate=Np[1] / t[1],
  time.to.peak=(t[which.max(diff(Np))] + t[which.max(diff(Np)) + 1]) / 2.0)

best.hyp2exp.from.interval.with.buildup(volume, t, t.begin=0.0,
  lower=c( # lower bounds
    0,  # qi > 0
    0.35,  # Di > 0
    0,  # b > 0
    0), # Df > 0
  upper=c( # upper bounds
    max(volume / diff(c(t.begin, t))) * 5, # qi < max(rate) * 5
    10, # = 0.99995 / [time] effective
    5,  # b <= 2.0
    0.35), # Df <= 0.35
  initial.rate=volume[1] / (t[1] - t.begin),
  time.to.peak=(t - diff(c(t.begin, t)) / 2)[which.max(volume)])

best.fit.from.Np.with.buildup(Np, t)

best.fit.from.interval.with.buildup(volume, t, t.begin=0.0)

`q`	vector of rate data.
`Np`	vector of cumulative production data.
`volume`	vector of interval volume data.
`t`	vector of times at which `q`, `Np`, or `volume` is measured.
`t.begin`	initial time for interval volume data, if non-zero.
`lower`	lower bounds for decline parameters (sane defaults are provided).
`upper`	upper bounds for decline parameters (sane defaults are provided).
`initial.rate`	initial rate, for declines with buildup.
`time.to.peak`	time to peak rate, for declines with buildup.

Best-fitting is carried out by minimizing the sum of squared error in the rate or cumulative forecast, using nlminb as the optimizer.

Appropriate bounds are applied to decline-curve parameters by default, but may be altered using the lower and upper arguments to each specific function.

best.exponential, best.hyperbolic, and best.hyp2exp return objects of the appropriate class (as from arps.decline) representing best fits of the appropriate type against q and t, in the same units as q and t.

best.fit returns the best overall fit, considering results from each function above.

best.exponential.from.Np, best.hyperbolic.from.Np, and best.hyp2exp.from.Np return objects of the appropriate class (as from arps.decline) representing best fits of the appropriate type against Np and t, in the same units as Np and t.

best.fit.from.Np returns the best overall fit, considering results from each function above.

best.exponential.from.interval, best.hyperbolic.from.interval, and best.hyp2exp.from.interval return objects of the appropriate class (as from arps.decline) representing best fits of the appropriate type against volume and t, in the same units as volume and t.

For these functions, t is taken to represent the time at the end of each producing interval; the beginning time for the first interval may be specified as t.begin if it is non-zero.

best.fit.from.interval returns the best overall fit, considering results from each function above.

best.exponential.curtailed, best.hyperbolic.curtailed, best.hyp2exp.curtailed, best.curtailed.fit, best.exponential.curtailed.from.Np, best.hyperbolic.curtailed.from.Np, best.hyp2exp.curtailed.from.Np, best.curtailed.fit.from.Np, best.exponential.curtailed.from.interval, best.hyperbolic.curtailed.from.interval, best.hyp2exp.curtailed.from.interval, and best.curtailed.fit.from.interval work as the corresponding functions above, but may return curtailed declines (as from curtail).

best.exponential.with.buildup, best.hyperbolic.with.buildup, best.hyp2exp.with.buildup, best.fit.with.buildup, best.exponential.from.Np.with.buildup, best.hyperbolic.from.Np.with.buildup, best.hyp2exp.from.Np.with.buildup, best.fit.from.Np.with.buildup, best.exponential.from.interval.with.buildup, best.hyperbolic.from.interval.with.buildup, best.hyp2exp.from.interval.with.buildup, and best.fit.from.interval.with.buildup work as the corresponding functions above, but will return a fit including a linear buildup portion (as from arps.with.buildup).

arps, curtailed, arps.with.buildup, nlminb

fitme.hyp2exp.t <- seq(0, 5, 1 / 12) # 5 years
fitme.hyp2exp.q <- hyp2exp.q(
    1000, # Bbl/d
    as.nominal(0.70), # / year
    1.9,
    as.nominal(0.15), # / year
    fitme.hyp2exp.t
) * rnorm(n=length(fitme.hyp2exp.t), mean=1, sd=0.1) # perturb

hyp2exp.fit <- best.hyp2exp(fitme.hyp2exp.q, fitme.hyp2exp.t)
cat(paste("SSE:", hyp2exp.fit$sse))
dev.new()
plot(fitme.hyp2exp.q ~ fitme.hyp2exp.t, main="Hyperbolic-to-Exponential Fit",
     col="blue", log="y", xlab="Time", ylab="Rate")
lines(arps.q(hyp2exp.fit$decline, fitme.hyp2exp.t) ~ fitme.hyp2exp.t,
      col="red")
legend("topright", pch=c(1, NA), lty=c(NA, 1), col=c("blue", "red"), legend=c("Actual", "Fit"))