CITATION.cff

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Three common statistical missteps we make in reservoir
  characterization
message: >-
  If these notebooks are useful for you, consider citing
  this paper
authors:
  - given-names: Frank
    family-names: Male
    email: frank.male@psu.edu
    affiliation: Penn State University
    orcid: 'https://orcid.org/0000-0002-3402-5578'
  - given-names: Jerry L.
    family-names: Jensen
    affiliation: University of Texas at Austin
identifiers:
  - type: doi
    value: 10.1306/07202120076
  - type: url
    value: 'https://doi.org/10.1306/07202120076'
repository-code: 'https://github.com/frank1010111/statistical_missteps'
abstract: >-
  Reservoir characterization analysis resulting from
  incorrect applications of statistics can be found in the
  literature, particularly in applications where integration
  of various disciplines is needed. Here, we look at three
  misapplications of ordinary least squares linear
  regression (LSLR), show how they can lead to poor results,
  and offer better alternatives, where available. The issues
  are Application of algebra to an LSLR-derived model to
  reverse the roles of explanatory and response variables
  that may give biased predictions. In particular, we
  examine pore-throat size equations (e.g., Winland’s and
  Pittman’s equations) and find that claims of overpredicted
  permeability may in part be because of statistical
  mistakes.Using a log-transformed variable in an LSLR
  model, detransforming without accounting for the role of
  noise. This gives an equation that underpredicts the mean
  value. Several approaches exist to address this
  problem.Misapplication of the coefficient of determination
  (R2) in three cases that lead to misleading results. For
  example, model fitting in decline curve analysis gives
  optimistic R2 values, as is also the case where a
  multimodal explanatory variable is present. Using actual
  and synthetic data sets, we illustrate the effects that
  these errors have on analysis and some implications for
  using machine learning results.
date-released: '2022-11-01'