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Record W2320588162 · doi:10.2118/171607-pa

Integrated Reservoir Characterization and 3D Modeling of the Monteith Formation: A Case Study of Tight Gas Sandstones in the Western Canada Sedimentary Basin, Alberta, Canada

2016· article· en· W2320588162 on OpenAlexaffabout
Liliana Zambrano, John Freddy Ramirez, Per Kent Pedersen, Roberto Aguilera

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGeologyFaciesReservoir modelingFluvialStructural basinPetroleum engineeringDrillingDrill cuttingsPermeability (electromagnetism)Stage (stratigraphy)PetrologyHydrology (agriculture)GeomorphologyPaleontologyGeotechnical engineeringEngineeringDrilling fluid

Abstract

fetched live from OpenAlex

Summary The Monteith Formation is an important tight gas reservoir in the Deep Basin, Alberta, and consists of a progradational succession of shallow marine sediments, nonmarine carbonaceous and coaly, coastal plain facies, and coarse-grained fluvial deposits, from base to top, respectively. This study is based on multiscale description and characterization techniques with cores and drill cuttings, including multimethods laboratory measurements of key reservoir parameters such as porosity and permeability. A second stage of the study involves the use of laboratory measurements obtained from cores and drill cuttings and their integration with well logs to construct a numerical 3D model of the study area. The 3D model is used to history match gas production, and forecast performance of new wells in those areas where the geologic model indicates potential for gas production. The ultimate goal is to provide a better understanding of the distribution of reservoir properties in the study area for developing drilling prospects and their production potential in areas where reliable data are scarce. The reservoir-modeling stage is carried out by implementing a recently developed methodology that integrates a variable shape distribution (VSD) model, capable of capturing different reservoir properties throughout the whole scale spectrum without any data truncation. Truncation is the excuse generally used for eliminating information that does not fit a given distribution. The claim is that the data are of poor quality, something that is not true in many cases. This new methodology eliminates the need for truncation, and introduces an extension of the VSD approach for reservoir-simulation purposes that reduces uncertainty in the generation of drilling prospects. Core analysis shows that the Monteith A member is composed of complex fluvial-dominated deposits with better rock quality than the shallow marine sandstones of the Monteith C member. This is most likely because of larger pore-throat apertures that range between 0.5 and 1 μm, and a relatively higher proportion of preserved intergranular pore space within these coarser-grained framework grains. Furthermore, the best production performance is from wells that are producing from the Monteith A. Variability of production rates also seems to be controlled by the presence of natural fractures. It is anticipated that the resulting 3D reservoir model will allow improving field-development strategies for this and other similar unconventional gas reservoirs in the Deep Basin of Alberta and elsewhere.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.220
Teacher spread0.205 · 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 designObservational
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

Citations1
Published2016
Admission routes2
Has abstractyes

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