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Record W4387309893 · doi:10.30632/pjv64n5-2023a6

The Potash Identification (PID) Plot: A Rapid Screening Crossplot for Discrimination of Commercial Potash

2023· article· en· W4387309893 on OpenAlexaboutno aff
Donald G. Hill, E.R. Crain, Lawrence W. Teufel

Bibliographic record

VenuePetrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description · 2023
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsPotashMineralogyGeologyEvaporitePotassiumMining engineeringGeochemistryChemistrySedimentary rock

Abstract

fetched live from OpenAlex

Potash minerals are the primary source of potassium (K), which is used for the manufacture of gunpowder, fertilizer, and as a sodium-seasoning substitute. Commercial potash minerals are all evaporites. Because potassium-40 (40K) is radioactive (decaying to argon-40 (40Ar) and releasing a gamma ray (GR) in the process), commercial potash mineralization is often discovered when GR (γ ray) logs in petroleum wells drilled through evaporite sequences “go off scale.” However, not all potash minerals may be commercial sources of potassium via underground mining techniques, and potassium is not the only radioactive element. For example, the mineralogy of the McNutt “potash” Member of the Salado Formation in southeast (SE) New Mexico is extremely complex, consisting of multiple thin (i.e., less than 10 ft thick) beds of six low-grade (radioactive) potash minerals, only two of which are commercial for underground mining. There are also four nonradioactive evaporite minerals, one of which may interfere with potash milling chemistry and numerous claystones and marker beds (shales and/or volcanics), with GR count rates comparable to the low-grade potash mineralization in this sequence. Because of this complexity, traditional borehole wireline (WL) and logging-while-drilling (LWD) potash assay techniques, such as GR log-to-core assay transforms, may not be sufficient to identify potentially commercial potash mineralization for underground mining (Teufel, 2008) in SE New Mexico. Crain and Anderson (1966) and Hill (2019) developed linear programming and multimineral analyses, respectively, to estimate potash mineralogy and grades from multiple borehole geophysical measurements. However, both of these approaches require large sets of multiple log measurements. In SE New Mexico, petroleum wells are drilled through the Salado Formation evaporite (including the McNutt “potash” Member) with air, then cased and cemented in place without running WL measurements. Then, the wells are drilled out to total depth (TD) in the underlying sediments with water-based mud. Complete log suites are run from TD to the casing shoe, with only the GR and neutron logs recorded through the cased evaporite sequence for stratigraphic and structural correlation. As a result, essentially all recent oil and gas wells in SE New Mexico have casedhole gamma ray and neutron logs through the Salado evaporite. Hill and Crain (2020) developed a simple crossplot involving only GR and neutron log data, which could discriminate between anhydrous and hydrated potassium evaporite minerals. Logs from these wells could provide a rapid potash screening database if used properly. This technique can be used with both openhole and casedhole petroleum well logs, as well as corehole WL logs, and provides discrimination of commercial potash mineralization from noncommercial (potash and non-potash) radioactive mineralization. Case histories of the use of PID crossplots in evaporite basins of Michigan, Nova Scotia, Saskatchewan, and SE New Mexico are described. This technique may also be useful in screening potential potash deposits elsewhere in the world.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.073
GPT teacher head0.309
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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