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Record W2974851450 · doi:10.2118/195882-ms

An Integrated Approach in Characterization of Triple Porosity Nisku Reefs Alberta: A Quest from Core and Borehole Images to 3D Earth Model

2019· article· en· W2974851450 on OpenAlexaffabout
Wade Zaluski, Dragan Andjelkovic, Cindy Xu, Jose A. Rivero, Majid M. Faskhoodi, Hakima Ali Lahmar, Herman Mukisa, Hanatu Kadir, Charles Ibelegbu, Warren Pearson, Raouf Ameuri, William S. Sawchuk

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2019
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsExxonMobil (Canada)Schlumberger (Canada)
Fundersnot available
KeywordsGeologyPetrophysicsBoreholeReefPorosityPermeability (electromagnetism)Fracture (geology)CarbonateReservoir modelingPetrologyPetroleum reservoirDiagenesisPetroleum engineeringGeotechnical engineeringMineralogyMaterials science

Abstract

fetched live from OpenAlex

Abstract Enhanced oil recovery (EOR) is an economic way of producing the remaining oil out of previously produced Devonian Pinnacle Reefs in the Nisku Formation within the Bigoray area of Alberta. To maximize the recovery factor of the remaining oil, it was necessary to first characterize the geological structure, matrix reservoir properties, vugular porosity and the natural fracture network of these two carbonate reefs. This characterization model was then used for reservoir simulation history matching and production forecasting further discussed by (Rivero, 2019). With the enhanced resolution of a reprocessed 3D seismic volume, more accurate seismic interpretation was completed to better delineate the internal and external structure of the reefs. The petrophysical analysis and core interpretation showed that the two reefs could be divided into two zones; the bottom zone has low porosity and the upper zone has high porosity that was targeted in previous well completion schemes. These zones were easily picked on well logs and when using Seismic Ant Tracking attributes, were accurately interpreted within the seismic volume. With the framework of the geomodel developed, rock type, porosity, permeability and water saturation were interpolated within the reservoir. Because natural fractures in these carbonate reservoirs are known to be an important part of fluid movement, it was important to characterize the discrete fracture network. In one well, a borehole image successfully quantified the properties of the natural fracture network. The observed fracture density (5 fractures/m) suggested discreate fracture zones throughout the well which was also confirmed with core fracture mapping. As part of the geomodel, a discrete fracture model (DFN) was generated; Seismic Ant Tracking was used to interpolate the fracture intensity within the reservoir. In these Devonian Pinnacle Reefs, and in other reservoirs, before investing in an EOR scheme, it is critical for the operator to understand the geologic structure and the petrophysical characteristics of the reservoir in as much detail as possible. This paper demonstrates how log and seismic data that is up to 40 years old can be converted to modern data types and be used to characterize a reservoir in a way not possible before.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.020
GPT teacher head0.263
Teacher spread0.243 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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