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Record W2077918217 · doi:10.2118/167185-ms

Pseudotime Calculation in Low Permeability Gas Reservoirs

2013· article· en· W2077918217 on OpenAlexaff
S. Hamed Tabatabaie, Louis Mattar, Mehran Pooladi‐Darvish

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCompressibilityNonlinear systemPermeability (electromagnetism)Partial differential equationPetroleum engineeringPressure gradientTight gasReservoir simulationReservoir engineeringBoundary value problemMechanicsDarcy's lawGeologyMathematicsGeotechnical engineeringMathematical analysisPorous mediumHydraulic fracturingPorosityChemistryPhysics

Abstract

fetched live from OpenAlex

Abstract Due to the recent advances in well design and production techniques, tight/shale gas reservoirs have received considerable attention. At the early phase of development of these reservoirs, fast analytical models are attractive since data are limited and a large number of sensitivity studies is required. This analytical solution is possible if the governing partial differential equation is linear. However, the pressure dependency of gas compressibility and viscosity makes the governing partial differential equation nonlinear. The use of pseudovariables (i.e. pseudopressure and pseudotime) significantly reduces this nonlinearity. Unlike pseudopressure, which is an exact mathematical transformation, pseudotime is an approximate transformation. For conventional gas reservoirs, the average reservoir pressure was utilized to evaluate pseudotime and worked very well during boundary dominated flow. However, in low permeability systems, when transient flow prevails, use of average reservoir pressure for pseudotime calculation is not valid and its use can create inconsistent results. Anderson and Mattar (2007) proposed that, during transient and transitional flow, the use of average pressure within the region of influence, rather than the average pressure of the whole reservoir, results in responses that are more consistent with those from numerical simulators. In this study, the idea of using average pressure within the region of influence is utilized to calculate pseudotime during constant rate production from a tight/shale gas reservoirs. In order to achieve this, the liquid type curve was employed to find the volume of investigation, and then the gas material balance equation was incorporated to evaluate the average pressure within the region of influence. The significant advantage of this method is that the volume of investigation is determined based on material balance principles by imposing a unit-slope line at each point in time, rather than depending on the radius of investigation formulation as was used by Anderson and Mattar (2007). In an irregularly shaped drainage area, different methods were investigated for evaluating the distance of investigation in the x- and y-directions. It was found that the distance of investigation in the x- and y- directions could not be represented by the same formula (i.e., yinv.=0.113kt/μgctϕ≠xinv. where all parameters are in field units). The method developed in this paper is applicable in modeling different flow regimes during transient, boundary affected and boundary dominated flow periods. This paper outlines the proposed approach and explains its usefulness by comparing the analytical and numerical results for different cases. It is shown that reliable forecasts of production can be obtained.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.208
Teacher spread0.201 · 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 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

Citations15
Published2013
Admission routes1
Has abstractyes

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