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Record W2016853221 · doi:10.2118/2000-080

Impulse, Perforation, And Closed Chamber Testing: Simple, Quick, Cost-Effective Snapshots of Reservoir Inflow Characteristics

2000· article· en· W2016853221 on OpenAlexaboutno aff
Damien Leech, Robert Hawkes, Philip A. Storey, S. G. R. Brown

Bibliographic record

VenueCanadian International Petroleum Conference · 2000
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsInflowSimple (philosophy)PerforationImpulse (physics)Impulse responseComputer sciencePetroleum engineeringGeologyMechanicsMechanical engineeringEngineeringPhysicsMathematics

Abstract

fetched live from OpenAlex

Abstract Impulse tests, Perforation Inflow Diagnostic (PID) tests and Closed Chamber tests are all similar in methodology. By whatever name, they provide simple, quick, cost-effective snapshots of maximum reservoir inflow characteristics. In a matter of minutes a field Engineer can provide estimates of the maximum AOF or IPR capability, the skin effect and flow capacity of a zone. Liquid influx can also be quantified. Applications include pre-frac reservoir assessments, multiple-layer perforation inflow distribution diagnostics (PIDD), establishing initial reservoir pressures, evaluating moveable gas in shaley zones or very low permeability horizons (reserves additions), flowing or pumping oil well tests, closed chamber drillstem tests (CCDST), and surface casing vent tests. With some conventional flow &buildup tests and production history as background, PID response can be correlated to stabilized gas rates for instantaneous tie-in or frac decisions, a benefit to expedient shallow gas exploitation. For all of these tests, data has been recorded in realtime at surface. Rigorous testing utilizes modern electronic surface data acquisition equipment and various software. A proprietary hardware &software system is also available. In it's simplest, most cost-effective and exploitive form, measurements with a hand-held pressure gauge are known to be 'good enough'. Analytical theory for these tests is well documented. While the tests have been around for some time, the techniques are under-utilized in Western Canada. Introduction The primary purpose of this paper is to illustrate various applications and usefulness of impulse testing with working field examples. Over the past several years the authors have experienced growth in the number of requests for these tests and success in their application in Western Canadian Sedimentary Basin gas fields. Surging interest in impulse testing is due to technological advancements, present-day economics, modern business interests and changing strategies where exploitation has become a new Petroleum Engineering discipline. Impulse testing has been around for some time and analytical theory is well documented. A comprehensive analytical process, however, does not presently exist. Hence, a secondary purpose of this paper is to bring together the various test types and references to illustrate the practicality of developing impulse analysis software. Better analytical tools are required to fully appreciate and maximize benefits of these quick, simple cost-effective techniques. EXPANDING APPLICATIONS Over the past several years impulse testing applications have been expanding, concurrent with ever increasing gas development and primarily for by-passed pay evaluation. Technological advancements with electronic surface data acquisition equipment (surface pressure recorders) has made impulse testing more practical. Surface recorders are now becoming commonplace tools for companies to access. Advanced completion strategies and underbalanced perforating technology means that accurate and viable impulse data can be collected at surface. Introduction of coiled tubing frac's has allowed companies to specifically target very thin low permeability streaks where an impulse test may provide the only feasible pre-frac assessment tool. Lower and lower stabilized flow rates are a reality of present-day and future economics. As well, ultimate recoverable reserves are more important then ever to a companies net present value. Hence, companies are pursuing by-passed pay in very low permeability horizons.

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 categoriesInsufficient payload (model declined to judge)
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 score0.140
Threshold uncertainty score0.999

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)0.0020.000

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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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.

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

Citations2
Published2000
Admission routes1
Has abstractyes

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