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Record W2002114542 · doi:10.2118/01-12-tn2

Well Test Analysis of Multi-Layered Naturally Fractured Reservoirs with Variable Thickness and Variable Fracture Spacing

2001· article· en· W2002114542 on OpenAlexaboutno aff
Roberto Aguilera, Maria Silvia Aguilera

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyWellboreOutcropFracture (geology)Permeability (electromagnetism)Petroleum engineeringSchematicMatrix (chemical analysis)Completion (oil and gas wells)PetrologyGeotechnical engineeringGeomorphologyEngineeringMaterials science

Abstract

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Introduction This technical note is a synopsis of paper 2001–091. The readers are referred to the full-length paper for additional details, nomenclature and illustrations. Outcrop information, cores, images, and well logs have shown that in some cases, naturally fractured reservoirs are composed of many layers(1). The thinner the layer, the smaller the fracture spacing (or distance between natural fractures). This is illustrated in Figure 1, which is a photo of the Cardium sandstone outcrop at Seebe Dam near Calgary, Alberta. For this type of reservoir, some fractures may be intersected by the wellbore and others may not, as is shown in the schematic of Figure 2. A production log would show only the fluid entrance points into the wellbore. The production log would not give an indication of net pay in this naturally fractured reservoir, only an indication of where the wellbore intersects the most important fractures. It is not unusual to see, from a production log, that out of 30 m perforated in a fractured reservoir, only 2 to 3 m contribute production into the wellbore, even if the 30 m are true net pay. This is the result of a typical situation that occurs in most naturally fractured reservoirs we are familiar with; i.e., the matrix has a very low permeability, which does not permit efficient fluid flow into the wellbore. This same tight matrix, however, can flow very efficiently into the natural fractures(1). Various papers have contributed to our understanding of transient behaviour in multi-layered reservoirs(2–6). Aguilera et al.(7) evaluated data from the naturally fractured Palm Valley gas field using analytical and numerical simulation techniques. Both approaches provided approximately the same results. In a subsequent paper, Aguilera(8) used a numerical simulator to examine the behaviour of a naturally fractured reservoir with ten layers and with fracture permeabilities ranging between 6.91 and 1,232.41 mD. The geometric mean permeability was 40 mD. The thickness of each layer (1.6 m) and the fracture spacing (3.35 m) were constant. From this analysis, it was concludedthat multi-layered behaviour could be recognized by a pressure derivative indicating partial penetration effects, even if the well was perforated in all layers. The partial penetration effects correspond to a derivative with a slope equal to about - 0.5. This was followed by an indication of linear flow; i.e., aslope of the pressure derivative equal to approximately +0.5. This technical note is a continuation of the research published by Aguilera(8). In the present paper, we are using the same numerical model, but in addition to variations in fracture permeability, we include variations in layer thickness, fracture spacing, and fracture porosity(9). FIGURE 1: Outcrop of the Cardium sandstone showing variations in layer thickness and fracture spacing (Seebe Dam, Alberta, Canada). Width of shown outcrop is approximately 25 m. Results Most reservoir parameters have been kept as in the previous study(8). Pay thickness for the reservoir is 16 m. Matrix porosity is 4.8% Matrix permeability is 0.023 mD. Gas saturation in the matrix is 42%, and gas saturation in the fractures is 99%.

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)0.001
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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.004
GPT teacher head0.191
Teacher spread0.187 · 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

Citations9
Published2001
Admission routes1
Has abstractyes

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Same venueJournal of Canadian Petroleum TechnologySame topicHydraulic Fracturing and Reservoir AnalysisFrench-language works237,207