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Anchored Targeted Projection Pursuit for Hypothesis-Driven Quality Control of Drill-Cuttings Geochemical Data
Research Article  ·  Published: 14 October 2026
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Journal of Geo-Energy and Environment
Volume 3, Issue 1, 2027: 48-71
Research Article Open Access

Anchored Targeted Projection Pursuit for Hypothesis-Driven Quality Control of Drill-Cuttings Geochemical Data

by
Yupeng Li Yupeng Li 1 * ORCID
Saeed S. Shahrani Saeed S. Shahrani 2
Zhaoxian Yuan Zhaoxian Yuan 3 ORCID
1 Beijing Research Center, Upstream Advanced Research Center, Saudi Aramco, Beijing 102200, China
2 Geological Operations Department, EXPEC, Saudi Aramco, Dhahran, Saudi Arabia
3 Hebei Key Laboratory of Strategic Critical Mineral Resources, Hebei GEO University, Shijiazhuang 050031, China
* Corresponding Author: Yupeng Li, [email protected]
Volume 3, Issue 1
Volume 3, Issue 1 2027
Received: 14 July 2026 Revised: 05 October 2026 Accepted: 06 October 2026 Published: 14 October 2026 CrossMark
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DOI: 10.62762/JGEE.2026.403029
ARK: ark:/57805/jgee.2026.403029
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Article Information

Published in Journal of Geo-Energy and Environment
Volume/Issue Volume 3, Issue 1, 2027
Pages 48-71

Abstract

Wellsite X-ray fluorescence (XRF) analysis of drill cuttings provides multi-element profiles for chemostratigraphy, well correlation and reservoir characterization, but the data are exposed to artefacts of the drilling workflow, such as drilling-fluid residue, carry-over between samples and detector drift. Quality control (QC) usually relies on principal component analysis (PCA), which ranks samples by variance rather than geological meaning, or on supervised classifiers, which need labels that do not exist at the QC stage. In practice, analysts often suspect a problem and can point to a few samples that show it. We introduce anchored targeted projection pursuit (anchored TPP), which builds its projection target from these anchors alone and leaves all other samples unconstrained. The fit reduces to a weighted orthogonal Procrustes problem with a closed-form solution and no tuning parameters. It remains well posed when variables outnumber samples, and its loadings name the elemental contrast behind each flag. We test it on a synthetic XRF benchmark and on a semi-synthetic experiment that plants a potassium chloride (KCl) brine mud signature into real cuttings data from an unconventional hydrocarbon well. Anchored TPP detects contamination that PCA and single-element thresholds miss, and log-ratio pre-treatment more than doubles its recovery. Positive--unlabelled learning recovers marginally more (0.55 of 12 hidden samples; 95% confidence interval 0.07-1.03), but anchored TPP is interpretable, tuning-free and fast enough for interactive use. Because all contamination labels are planted, field validation against laboratory-confirmed intervals remains necessary.

Graphical Abstract

Anchored Targeted Projection Pursuit for Hypothesis-Driven Quality Control of Drill-Cuttings Geochemical Data

Keywords

projection pursuit drill cuttings data quality control chemostratigraphy reservoir characterization X-ray fluorescence drilling-fluid contamination compositional data human-in-the-loop analysis

Data Availability Statement

The reference implementation of anchored TPP, the synthetic benchmark generator, and the scripts reproducing every figure and reported value in this paper are archived at Zenodo, DOI: https://doi.org/10.5281/zenodo.22668675, and developed at https://github.com/yupenglee2006-ui/anchored-tpp-reproducibility, where the revision statistics script (experiments/exp_revision_stats.py) reproduces every confidence interval and effect size reported here (MIT licence for code; CC-BY-4.0 for figures and result files). The semi-synthetic experiments use the published cuttings XRF dataset of B. Hall (“Geochemical facies analysis using unsupervised machine learning,” CSEG Recorder, pp. 26–30, 2018; https://cseg.ca/wp-content/uploads/2018-01-RECORDER-Geochemical_Facies_Analysis.pdf), which is third-party and is not redistributed in the archive; it must be obtained from its original source. A synthetic stand-in generator is provided so that the pipeline can be exercised without it.

Funding

This work was supported without any funding.

Conflicts of Interest

Yupeng Li is affiliated with the Beijing Research Center, Upstream Advanced Research Center, Saudi Aramco, Beijing 102200, China, and Saeed S. Shahrani is affiliated with the Geological Operations Department, EXPEC, Saudi Aramco, Dhahran, Saudi Arabia. The authors declare that these affiliations did not influence the study design, data collection, analysis, interpretation, or decision to publish. The authors declare that no other competing interests exist.

AI Use Statement

The authors declare that DeepSeek-V4.1-Flash was used for language polishing during drafting, and Claude Opus 5.0 (Anthropic) was used for language editing and formatting during revision. The authors have carefully reviewed, revised, and verified the AI-assisted output and take full responsibility for the content of the manuscript.

Ethical Approval and Consent to Participate

Not applicable. This study did not involve human participants, human data or animals; it uses synthetic data and a published, third-party geochemical dataset of rock samples.

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Cite This Article

APA Style
Li, Y., Shahrani, S. S., & Yuan, Z. (2026). Anchored Targeted Projection Pursuit for Hypothesis-Driven Quality Control of Drill-Cuttings Geochemical Data. Journal of Geo-Energy and Environment, 3(1), 48-71. https://doi.org/10.62762/JGEE.2026.403029
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TY  - JOUR
AU  - Li, Yupeng
AU  - Shahrani, Saeed S.
AU  - Yuan, Zhaoxian
PY  - 2026
DA  - 2026/10/14
TI  - Anchored Targeted Projection Pursuit for Hypothesis-Driven Quality Control of Drill-Cuttings Geochemical Data
JO  - Journal of Geo-Energy and Environment
T2  - Journal of Geo-Energy and Environment
JF  - Journal of Geo-Energy and Environment
VL  - 3
IS  - 1
SP  - 48
EP  - 71
DO  - 10.62762/JGEE.2026.403029
UR  - https://www.icck.org/article/abs/JGEE.2026.403029
KW  - projection pursuit
KW  - drill cuttings
KW  - data quality control
KW  - chemostratigraphy
KW  - reservoir characterization
KW  - X-ray fluorescence
KW  - drilling-fluid contamination
KW  - compositional data
KW  - human-in-the-loop analysis
AB  - Wellsite X-ray fluorescence (XRF) analysis of drill cuttings provides multi-element profiles for chemostratigraphy, well correlation and reservoir characterization, but the data are exposed to artefacts of the drilling workflow, such as drilling-fluid residue, carry-over between samples and detector drift. Quality control (QC) usually relies on principal component analysis (PCA), which ranks samples by variance rather than geological meaning, or on supervised classifiers, which need labels that do not exist at the QC stage. In practice, analysts often suspect a problem and can point to a few samples that show it. We introduce anchored targeted projection pursuit (anchored TPP), which builds its projection target from these anchors alone and leaves all other samples unconstrained. The fit reduces to a weighted orthogonal Procrustes problem with a closed-form solution and no tuning parameters. It remains well posed when variables outnumber samples, and its loadings name the elemental contrast behind each flag. We test it on a synthetic XRF benchmark and on a semi-synthetic experiment that plants a potassium chloride (KCl) brine mud signature into real cuttings data from an unconventional hydrocarbon well. Anchored TPP detects contamination that PCA and single-element thresholds miss, and log-ratio pre-treatment more than doubles its recovery. Positive--unlabelled learning recovers marginally more (0.55 of 12 hidden samples; 95% confidence interval 0.07-1.03), but anchored TPP is interpretable, tuning-free and fast enough for interactive use. Because all contamination labels are planted, field validation against laboratory-confirmed intervals remains necessary.
SN  - 3069-3268
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
BibTeX Format
Compatible with LaTeX, BibTeX, and other reference managers
@article{Li2026Anchored,
  author = {Yupeng Li and Saeed S. Shahrani and Zhaoxian Yuan},
  title = {Anchored Targeted Projection Pursuit for Hypothesis-Driven Quality Control of Drill-Cuttings Geochemical Data},
  journal = {Journal of Geo-Energy and Environment},
  year = {2026},
  volume = {3},
  number = {1},
  pages = {48-71},
  doi = {10.62762/JGEE.2026.403029},
  url = {https://www.icck.org/article/abs/JGEE.2026.403029},
  abstract = {Wellsite X-ray fluorescence (XRF) analysis of drill cuttings provides multi-element profiles for chemostratigraphy, well correlation and reservoir characterization, but the data are exposed to artefacts of the drilling workflow, such as drilling-fluid residue, carry-over between samples and detector drift. Quality control (QC) usually relies on principal component analysis (PCA), which ranks samples by variance rather than geological meaning, or on supervised classifiers, which need labels that do not exist at the QC stage. In practice, analysts often suspect a problem and can point to a few samples that show it. We introduce anchored targeted projection pursuit (anchored TPP), which builds its projection target from these anchors alone and leaves all other samples unconstrained. The fit reduces to a weighted orthogonal Procrustes problem with a closed-form solution and no tuning parameters. It remains well posed when variables outnumber samples, and its loadings name the elemental contrast behind each flag. We test it on a synthetic XRF benchmark and on a semi-synthetic experiment that plants a potassium chloride (KCl) brine mud signature into real cuttings data from an unconventional hydrocarbon well. Anchored TPP detects contamination that PCA and single-element thresholds miss, and log-ratio pre-treatment more than doubles its recovery. Positive--unlabelled learning recovers marginally more (0.55 of 12 hidden samples; 95\% confidence interval 0.07-1.03), but anchored TPP is interpretable, tuning-free and fast enough for interactive use. Because all contamination labels are planted, field validation against laboratory-confirmed intervals remains necessary.},
  keywords = {projection pursuit, drill cuttings, data quality control, chemostratigraphy, reservoir characterization, X-ray fluorescence, drilling-fluid contamination, compositional data, human-in-the-loop analysis},
  issn = {3069-3268},
  publisher = {Institute of Central Computation and Knowledge}
}

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