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Record W2890151451 · doi:10.1007/s13202-018-0546-0

Analysis of horizontal well productivity in tight gas formations and its sensitivity to reservoir properties

2018· article· en· W2890151451 on OpenAlexaff
Rasa Soleimani, Yaser Jahanpeyma, Mohammad Salehian

Bibliographic record

VenueJournal of Petroleum Exploration and Production Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsProductivityPetroleum engineeringPermeability (electromagnetism)Well test (oil and gas)Pressure gradientPorosityTight gasOffshore geotechnical engineeringAnisotropySensitivity (control systems)GeologyGeotechnical engineeringEngineeringChemistryElectronic engineeringOpticsHydraulic fracturing

Abstract

fetched live from OpenAlex

Horizontal wells are frequently used in gas reservoirs to enhance the production performance by increasing the contact surface of the well with the formation. Although some methods have been presented to evaluate vertical well productivity in tight gas reservoirs, the detailed information regarding horizontal wells has not yet been deeply addressed. This study uses a modified backpressure test method based on the isochronal test to determine the correct value of the productivity of horizontal wells in a pressure-dependent viscosity anisotropic tight gas formation. To use the isochronal test calculations in the modified backpressure test, the "process conversion-flowing pressure correction" approach was used to convert the modified backpressure test process into the isochronal test. The comparison between productivity values before and after correction with the simulation results validates that how this method can successfully estimate the productivity in horizontal wells. We perform a sensitivity analysis on the error between the pseudo-pressure before and after correction and show its variation with porosity, permeability, skin, formation thickness, and temperature. This can be helpful to understand the importance of correcting pseudo-pressure under different conditions. Results validate that the proposed method of productivity analysis is needed for horizontal wells and should be applied especially in low permeable and thin (tight gas) formations to acquire the precise value of productivity.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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 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".

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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