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Record W2052236172 · doi:10.2523/iptc-17360-ms

NMR Measurements for Pore Size Mapping at Fine Scale

2014· article· en· W2052236172 on OpenAlexaff
Andrea Valori, Farhan Ali, Ahmad AlZoukani, Reza Taherian

Bibliographic record

VenueInternational Petroleum Technology Conference · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsSchlumberger (Canada)
Fundersnot available
KeywordsCapillary actionCapillary pressureDisplacement (psychology)Fluid dynamicsFlow (mathematics)GeologyPore water pressureMechanicsEnhanced oil recoveryPetroleum engineeringMaterials sciencePorous mediumMineralogyGeotechnical engineeringPorosityComposite materialPhysics

Abstract

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Abstract As fluids move through a rock their flow path is controlled by the capillary forces from the local pore size distribution. The pore structure causes the fluid not to follow a simple path which is a familiar challenge in reservoir production and recovery. In this paper we examine this effect at a small, more manageable scale of a core plug in laboratory. Fully water saturated plugs were centrifuged in air using small capillary pressure steps. At each step the T2 distribution of the core was measured. The capillary pressure steps were incremented at one psi steps for careful mapping of the evolution of fluid distribution. In this experiment the water was replaced by air which has no NMR signal, thus the results clearly showed gradual removal of free water from the larger pores with no reduction of bound water signal. Comparing T2 distributions from different capillary pressure steps, we were able to pinpoint the pores contributing to fluid displacement at each pressure. These results, for the first time, reveal more detailed pore information that is apparent from normal T2 distribution alone. The new approach enables deeper understanding of rock pore structure and how the fluid distribution is influenced by the pore sizes involved in conducting the fluid. These results, once up-scaled to reservoir level, will help optimize and improve oil recovery. The technical contribution of this paper includes pore size study at a finer scale by NMR than previously reported. Introduction Rock pores are important in reservoir production and planning. The pores parameters such as size distribution and volume are used in estimating important reservoir properties. The pore size distribution is usually determined using mercury injection capillary pressure (MICP) by injecting mercury and measuring the volume as a function of applied pore pressure. However, this method is not applicable to in situ measurements; further, there are no methods of estimating pore size distribution in situ. Correlations between MICP and NMR T2 distribution have been used to estimate the pore size distribution, but the results are often not satisfactory. A clear correlation between NMR T2 and pore size is not as yet available. This is partly due to the fact that NMR T2 is related to pore radius while MICP results are mostly controlled by the throat size. More research focused on understanding the relation between these two approaches is needed to advance our ability to relate the two in a quantitative way. In this paper we attempt to do that by measuring the specific NMR T2 peaks for each pore pressure. Experimental Core plugs from Carbonate outcrops in Saudi Arabia were used in this study. The plugs were cleaned and saturated with 100kppm NaCl brine. The plugs were dried and evacuated for 8 hours before brine was introduced into the vacuum chamber. To ensure complete saturation, the plugs were subjected to a pressure of 2000 psi for 16 hours. The porosities were calculated from the weight of the plugs before and after water saturation using standard approach. Helium porosity was 17.1% and Nitrogen permeability 27mD. One of the cores, M7V, was selected for detailed studies; the remaining three were used as counterbalance weights in the centrifuge. Before subjecting the cores to centrifugal force, NMR T2 measurements were performed on fully water saturated M7V plug (SW=1). An Oxford Instrument NMR spectrometer, operating at 2MHz frequency was used for making these measurements. In addition, we measured the 1-D NMR porosity image of the M7V core plug. These measurements require a linear gradient coil which is a built-in feature of Oxford Instrument spectrometer.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.307
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations5
Published2014
Admission routes1
Has abstractyes

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