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Record W4250979908 · doi:10.2118/2009-102

Estimation of Vertical Permeability in the McMurray Formation

2009· article· en· W4250979908 on OpenAlexaff
C.V. Deutsch

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPermeability (electromagnetism)GeologyRelative permeabilityComputer sciencePetroleum engineeringGeotechnical engineeringChemistryPorosity

Abstract

fetched live from OpenAlex

Abstract Predicting the performance of in-situ recovery processes in the McMurray Formation is required to optimize development planning and resource management. These performance predictions are sensitive to many parameters; however, vertical permeability is perhaps the most critical geological parameter. There are many challenges associated with the estimation of vertical permeability:it is difficult to collect representative core measurements,the high viscosity of the bitumen makes it impossible to perform well testing,statistical approaches and the notion of representative elementary volumes (REVs) are challenged by heterogeneities at all scales, andthe nature of the heterogeneities is variable within different depositional environments. This paper summarizes these challenges, then presents a consistent numerical modeling framework based on core data, core photographs, conventional well logs, high resolution image logs and detailed geological interpretation. The framework includes: dividing the stratigraphic column into facies with similar spatial arrangement of sand/shale, constructing high resolution models of sand/shale, assigning porosity and permeability to sand/shale, calibrating the models to direct measurements, solving for effective horizontal and vertical permeability at the appropriate scale and transferring the results to geomodeling. This framework is described in detail and demonstrated with illustrative examples. Considerations for even better results are discussed. Introduction The McMurray Formation contains a vast resource of heavy oil. The economic production of this heavy oil often makes use of thermal processes to reduce viscosity and horizontal wells that have a large contact area with the formation. Steam is often injected to introduce thermal energy. The rates of steam rise and water/oil drainage are predicted by flow simulation. A critical input parameter in that flow simulation is the vertical permeability. Accurate prediction of fluid flow would permit optimization of the recovery process and operating parameters; thus, accurate estimation of vertical permeability is of great interest in the McMurray formation. The ability of the reservoir formation to transmit fluids (permeability) has a large affect on the reservoir response for given operating conditions. Permeability is a constant that relates the flow rate through a porous medium to an imposed pressure gradient. Small scale variations in the clastic deposits of the McMurray cause permeability to be variable and direction dependent. Permeability in the vertical direction is of primary concern because operators are concerned with (1) the rise of steam through the formation, (2) the possible escape of steam and thermal energy to overlying formations, and (3) the rates at which condensed water and oil will drain to horizontal production wells. This paper is concerned with absolute vertical permeability. There are important confounding effects that are not considered such as changes to permeability because of multiple fluids present in the formation and changes to permeability because of time varying geomechanical effects. This paper is primarily concerned with the influence of small scale geological heterogeneities on the estimation of vertical permeability. The challenges that make the reliable estimation of vertical permeability will be reviewed. Historical approaches to permeability estimation will be summarized.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.985

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 head0.015
GPT teacher head0.234
Teacher spread0.219 · 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
Published2009
Admission routes1
Has abstractyes

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