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Record W4238426186 · doi:10.2523/59735-ms

Diagnostic Techniques to Understand Hydraulic Fracturing: What? Why? and How?

2000· article· en· W4238426186 on OpenAlexaboutno aff
C. Cipolla, Cheryl S. Wright

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCitationHydraulic fracturingComputer scienceWrightPinnacleDownloadLibrary scienceWorld Wide WebGeologyPetroleum engineering

Abstract

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Diagnostic Techniques to Understand Hydraulic Fracturing: What? Why? and How? C.L. Cipolla; C.L. Cipolla Pinnacle Technologies Search for other works by this author on: This Site Google Scholar C.A. Wright C.A. Wright Pinnacle Technologies Search for other works by this author on: This Site Google Scholar Paper presented at the SPE/CERI Gas Technology Symposium, Calgary, Alberta, Canada, April 2000. Paper Number: SPE-59735-MS https://doi.org/10.2118/59735-MS Published: April 03 2000 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Cipolla, C.L., and C.A. Wright. "Diagnostic Techniques to Understand Hydraulic Fracturing: What? Why? and How?." Paper presented at the SPE/CERI Gas Technology Symposium, Calgary, Alberta, Canada, April 2000. doi: https://doi.org/10.2118/59735-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Unconventional Resources Conference / Gas Technology Symposium Search Advanced Search AbstractIn recent years there have been numerous advances in fracture mapping/diagnostic technologies. This paper details the state-of-the-art in applying both conventional and advanced technologies to better understand hydraulic fracturing and improve treatment designs. The initial portion of the paper describes the application and limitations of various diagnostic tools and methods, including well testing, net pressure analysis (fracture modeling), techniques that employ open-hole & cased-hole logs, surface & downhole tilt fracture mapping, microseismic fracture mapping, and production data analysis. The bulk of the paper is dedicated to case histories that illustrate the application of these various fracture diagnostic technologies. The case histories include examples of how several fracture diagnostics can be used in concert to provide more reliable estimates of fracture dimensions and allow better economic decisions.IntroductionThe process of hydraulic fracturing has always had a "black box" image. This has been partly because knowledge about fracture geometry is difficult to obtain with fractures growing thousands of feet below the surface, and partly because fracturing is proving to be vastly more complex than initially thought.1–3 While hydraulic fracture treatments continue to be designed using the best tools and techniques available, geometry estimates from fracture models have been difficult to verify. Numerous fracture diagnostic techniques have been developed to fill this knowledge gap, improving our understanding of hydraulic fracture behavior.4–10The main purpose of fracture diagnostics is to help the producer optimize field development and well economics. This can include optimizing individual fracture treatments to obtain the most economic design and optimum interval/height coverage or optimizing the entire field development in terms of well spacing and location. Fracture diagnostics can be beneficial in numerous stimulation settings. Settings range from propped fracture stimulation of a new pay zone in a newly developed field to infill-drilling development, and from field development using hydraulically fractured horizontal wells to the evaluation of fracturing during steam-flooding or water-flooding.When executing fracturing operations in one of these settings, several questions can be answered in the design/evaluation process using fracture diagnostics, including:Do fractures effectively cover the pay zone?Are fractures confined to the pay zone?Does the fracture grow into an unwanted gas bearing or water-bearing zone?What is the optimum number of fracture treatment stages and treatment size to cover thick pay zones?How much more length/height/production is obtained if treatment size is increased?Is the final fracture conductivity sufficient to achieve the desired production? What is the optimum proppant?Is the hydraulic fracture oriented in the same direction as the primary set of natural fractures?What direction should a horizontal well be drilled to complete it with transverse (or longitudinal) multi-stage fracture treatments?Is the well pattern appropriate to maximize sweep efficiency in steam/water-flood areas?Do the injected waste and drill cuttings remain within the selected zone?Numerous fracture diagnostics are available (see Figure 1), including techniques that directly image "big picture" far-field fracture growth, dimensions, and orientation; tools that provide a local measurement of the fracture at the wellbore; and lower-cost indirect (model-dependent) diagnostic methods. There are three main groups of commercially available fracture diagnostic techniques, each with their own set of capabilities and limitations. A summary of the techniques, limitations and the parameters each technique measures is provided in Table 1.11 Keywords: fracture mapping, fracture growth, hydraulic fracture, tiltmeter, mapping, conductivity, fracture geometry, fracture modeling, fracture diagnostic, geometry Subjects: Hydraulic Fracturing This content is only available via PDF. 2000. Society of Petroleum Engineers You can access this article if you purchase or spend a download.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.733

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)0.0010.000

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 head0.008
GPT teacher head0.209
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations43
Published2000
Admission routes1
Has abstractyes

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