Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 292

The DOI matches its Crossref record.

Partitioning Tracer Test for Detection, Estimation, and Remediation Performance Assessment of Subsurface Nonaqueous Phase Liquids

Jin, Minquan, Delshad, Mojdeh, Dwarakanath, Varadarajan, McKinney, Daene C., Pope, Gary A., Sepehrnoori, Kamy, Tilburg, Charles E., Jackson, Richard E.·1995·Water Resources Research

Abstract

In this paper we present a partitioning interwell tracer test (PITT) technique for the detection, estimation, and remediation performance assessment of the subsurface contaminated by nonaqueous phase liquids (NAPLs). We demonstrate the effectiveness of this technique by examples of experimental and simulation results. The experimental results are from partitioning tracer experiments in columns packed with Ottawa sand. Both the method of moments and inverse modeling techniques for estimating NAPL saturation in the sand packs are demonstrated. In the simulation examples we use UTCHEM, a comprehensive three‐dimensional, chemical flood compositional simulator developed at the University of Texas, to simulate a hypothetical two‐dimensional aquifer with properties similar to the Borden site contaminated by tetrachloroethylene (PCE), and we show how partitioning interwell tracer tests can be used to estimate the amount of PCE contaminant before remedial action and as the remediation process proceeds. Tracer tests results from different stages of remediation are compared to determine the quantity of PCE removed and the amount remaining. Both the experimental (small‐scale) and simulation (large‐scale) results demonstrate that PITT can be used as an innovative and effective technique to detect and estimate the amount of residual NAPL and for remediation performance assessment in subsurface formations.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1029/95wr00174

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

Metadata

Authors
Jin, M., Delshad, M., Dwarakanath, V., McKinney, D. C., Pope, G. A., Sepehrnoori, K., Tilburg, C. E., & Jackson, R. E.
Venue
Water Resources Research
Published
1995-05
Volume
31
Issue
5
Pages
1201-1211
ISSN
  • 0043-1397 (ISSN-L)
  • 1944-7973
OpenAlex
W1995812185
Cited by
292
References
0
FWCI
13.09
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.