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Record W2087053033 · doi:10.2118/2004-037

Well Testing Analysis for Variable Permeability Reservoirs

2004· article· en· W2087053033 on OpenAlexaff
Fanhua Zeng, Gang Zhao

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPermeability (electromagnetism)HomogeneousTransient analysisGeologyMathematicsMathematical analysisMechanicsPhysicsEngineeringChemistryTransient response

Abstract

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Abstract Stimulation of carbonate formations by acid dissolution of the rock has been an efficient and successful method of bimproving production in oil and gas wells. Hydrochloric acid is the normal fluid of choice. However, in high temperature applications corrosion issues limit usage, especially in chrome completions. Acetic acid has been used with some success and with adequate corrosion protection. But due to its low reactivity at higher temperatures, the efficiency with which a gallon of acid dissolves the formation is perceived as low. This perception comes from reaction efficiency of acetic acid reported in the literature ranging in values from 90% at 25 °C to 40% at 121 °C for 2 to 15 wt%, respectively. Acetic acid reaction on calcium carbonate is controlled by its small dissociation constant, 1.754E-05 at 25 °C (77 °F) and therefore is labeled a weak acid. Abstract The effects of permeability variable in formation on pressure derivative curve are studied in this paper. We view the general heterogeneous reservoir as a base homogeneous reservoir modified with multiple variable permeabilities at some locations. The sections where permeabilities are different from the base permeability are called as the variable permeability sections. A heterogeneous reservoir with only one variable permeability section can be well defined as long as the permeability, the size and the location of the section are specified. Its pressure derivative deviation from that of the base homogeneous reservoir has been thoroughly studied. Results show that the start time and the value and the occurred time of maximum magnitude of the pressure derivative deviation suggest the start position, the permeability and the end position of the variable permeability section, respectively. In order to analyze a heterogeneous reservoir with multiple variable permeability sections, we proposed that its pressure derivative difference with respect to the base homogeneous reservoir is the summation of the pressure derivative differences, with respect to the base homogeneous reservoir, of the single-section multiple heterogeneous reservoirs, each of them possesses only one variable permeability section. This method has been proved and verified in the reservoirs with radial and areal permeability distribution using both analytical and numerical methods. Applications show that this method provides a useful clue for heterogeneity reservoir well testing analysis. If the test noise can be ignored in pressure derivative curve, this method is very practicable for well testing analysis of variable permeability reservoirs. In the cases where pressure noise makes the pressure derivative zigzag, some de-noising methods, for example Schroeter's deconvolution method, wavelets and optimal model, can be used to de-noise pressure data in order to get smooth pressure derivative curve. Then, this de-noised test data can be diagnosed using the method proposed. Introduction Traditional well testing analysis tends to determine an overall permeability, which cannot reflect the variation of permeability in formation. In our experiences on practical well testing interpretation, we often encounter the situations that we can match the shape and the trend of the pressure derivative curve perfectly, but we cannot match the slight waves in the pressure derivative curve. Generally there are two sources that produced these kinds of waves, pressure measurement noise and the response of heterogeneity of the reservoir. The waves generated from the pressure measurement noise are random and discontinuous, while those from the heterogeneity of the reservoir behave continuously and smoot

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 score0.290
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.228
Teacher spread0.209 · 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

Citations4
Published2004
Admission routes1
Has abstractyes

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