Theory

Stretched Exponential Production Decline (SEPD)

Overview

The Stretched Exponential Production Decline (SEPD) model was introduced by ValkΓ³ and Lee (2010) as a statistically-grounded approach to decline curve analysis for unconventional reservoirs. Unlike other empirical models, SEPD has a foundation in statistical physics, specifically the theory of "fat-tailed" distributions, making it particularly suitable for analyzing large populations of wells.

Key Concepts

  • Statistical Physics Basis: Derived from stretched exponential (Kohlrausch) functions used in material science
  • Bounded EUR: Always produces finite cumulative production, avoiding the b > 1 problem
  • Population-Based: Originally designed for analyzing entire field populations, not individual wells
  • Three Parameters: Uses Qi, Ο„ (characteristic time), and n (stretching exponent)

When to Use SEPD

Reservoir TypeSuitabilityNotes
Shale gasβœ… ExcellentOriginal target application
Shale oilβœ… ExcellentWorks well for tight oil
Tight gasβœ… GoodCaptures transient behavior
Field-scale analysisβœ… ExcellentDesigned for population statistics
Conventional⚠️ LimitedMay not match BDF behavior well

Theory

Physical Basis

The SEPD model is based on the stretched exponential function (also called Kohlrausch function), which arises naturally in systems with a distribution of relaxation times. In reservoir context, this represents heterogeneity in drainage patterns and flow geometries.

The key insight is that production from unconventional wells can be viewed as a superposition of multiple exponential decays with varying time constants:

q(t)=∫0∞q0eβˆ’t/Ο„β€²f(Ο„β€²)dΟ„β€²q(t) = \int_0^\infty q_0 e^{-t/\tau'} f(\tau') d\tau'

When f(Ο„β€²)f(\tau') follows a specific "fat-tail" distribution, the result is the stretched exponential form.

Stretched Exponential Function

The stretched exponential differs from a standard exponential by the exponent n on the time term:

  • Standard exponential: eβˆ’t/Ο„e^{-t/\tau}
  • Stretched exponential: eβˆ’(t/Ο„)ne^{-(t/\tau)^n}

For n<1n < 1, the function decays more slowly at early times and faster at late times compared to a standard exponential, capturing the transition from high initial decline to slower long-term decline.


Equations

Rate Equation

q(t)=qiexp⁑[βˆ’(tΟ„)n]q(t) = q_i \exp\left[-\left(\frac{t}{\tau}\right)^n\right]

where:

  • qiq_i = Initial (peak) production rate (LΒ³/T)
  • Ο„\tau = Characteristic time parameter (T)
  • nn = Stretching exponent (dimensionless, typically 0.2-0.5)
  • tt = Time (T)

Cumulative Production

The cumulative production involves the incomplete gamma function:

Np(t)=qiΟ„n{Ξ“[1n]βˆ’Ξ“[1n,(tΟ„)n]}N_p(t) = \frac{q_i \tau}{n} \left\{\Gamma\left[\frac{1}{n}\right] - \Gamma\left[\frac{1}{n}, \left(\frac{t}{\tau}\right)^n\right]\right\}

where:

  • Ξ“[a]\Gamma[a] = Complete gamma function
  • Ξ“[a,x]\Gamma[a, x] = Upper incomplete gamma function

Ultimate Recovery (EUR)

As tβ†’βˆžt \to \infty, the incomplete gamma function approaches zero:

EUR=qiΟ„nΞ“[1n]EUR = \frac{q_i \tau}{n} \Gamma\left[\frac{1}{n}\right]

This closed-form EUR is a key advantage of the SEPD model.

Time to Economic Limit

Solving for time when q=qeconq = q_{econ}:

tecon=Ο„[ln⁑(qiqecon)]1/nt_{econ} = \tau \left[\ln\left(\frac{q_i}{q_{econ}}\right)\right]^{1/n}


Functions Covered

FunctionDescriptionReturns
StretchedExponentialDeclineRateRate at time tq(t), LΒ³/T
StretchedExponentialDeclineCumulativeCumulative production at time tNp(t), LΒ³
StretchedExponentialDeclineTimeTime to reach economic rate limitt, T
StretchedExponentialDeclineEUREUR to economic rate limitNp(tβ‚‘con), LΒ³
StretchedExponentialDeclineFitParametersFit [Qi, Ο„, n] to dataArray[3]
StretchedExponentialDeclineWeightedFitParametersWeighted fit [Qi, Ο„, n]Array[3]

See each function page for detailed parameter definitions, Excel syntax, and usage examples.


Applicability & Limitations

Applicability Ranges

ParameterRecommended RangeNotes
Exponent n0.2 - 0.5n = 1 reduces to standard exponential
Ο„ (tau)> 0Scales the decline timeline
Production history> 6 monthsShorter histories may work for fitting

Physical Constraints

  • n>0n > 0 (required for declining rate)
  • n≀1n \leq 1 (physically meaningful stretching)
  • Ο„>0\tau > 0 (positive time constant)
  • qi>0q_i > 0 (physical rate)

Limitations

  1. No Physical D∞ Term: Unlike PLE, SEPD does not explicitly model terminal decline rate
  2. Late-Time Behavior: May not accurately capture boundary-dominated flow behavior
  3. Statistical Nature: Best suited for population analysis rather than individual well prediction
  4. Gamma Function: Cumulative calculation requires special mathematical functions

Comparison with Other Models

AspectSEPDArps HyperbolicPLE
Theoretical basisStatistical physicsEmpiricalLoss-ratio
EUR formulaβœ… Closed-form⚠️ Only if b < 1❌ Numerical
Parameters334
BDF modeling❌ Implicit⚠️ Poorβœ… Explicit D∞
Population analysisβœ… Designed for❌ Individual wells⚠️ Possible

Relationship to PLE

When D∞=0D_\infty = 0 in the PLE model:

q=qiexp⁑(βˆ’Ditn)q = q_i \exp(-D_i t^n)

This is equivalent to SEPD with Ο„=Diβˆ’1/n\tau = D_i^{-1/n}. The key difference is that PLE explicitly includes a terminal decline term D∞D_\infty for boundary-dominated flow.


References

  1. ValkΓ³, P.P. and Lee, W.J. (2010). "A Better Way to Forecast Production from Unconventional Gas Wells." SPE Annual Technical Conference and Exhibition, Florence, Italy. SPE-134231-MS.

  2. ValkΓ³, P.P. (2009). "Assigning Value to Stimulation in the Barnett Shale: A Simultaneous Analysis of 7000 Plus Production Histories and Well Completion Records." SPE-119369-MS.

  3. Ali, T.A. and Sheng, J.J. (2015). "Production Decline Models: A Comparison Study." SPE-177300-MS.

  4. Abramowitz, M. and Stegun, I.A. (1972). Handbook of Mathematical Functions with Formulas, Graphs and Mathematical Tables. New York: Dover Publications.

Decline Models
unconventionalshalestatistical-physicsbounded-EURpopulation-analysis
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