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Record W2044559513 · doi:10.2118/02-12-03

Determination of Production Operation Methods in Naturally Fractured Reservoirs

2002· article· en· W2044559513 on OpenAlexafffund
Daoyong Yang, Yongan Gu, Qinqin Zhang

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsPetroleum Technology Research CentreUniversity of Regina
FundersPetroleum Technology Research CentreUniversity of Regina
KeywordsPetroleum engineeringArtificial liftInflowGeologyCompactionReservoir engineeringOil fieldProduction (economics)Sucker rodReservoir modelingOil productionGeotechnical engineeringPetroleum

Abstract

fetched live from OpenAlex

Abstract There are many naturally fractured reservoirs in the world, but few of them are developed optimally. In fact, it is difficult to characterize naturally fractured reservoirs and predict their oil production, let alone determine their appropriate production operation methods (POMs). Although there exist some formulas for evaluating well performance, few are derived on the basis of production test data. In this paper, several general formulas are developed to evaluate inflow performance of both vertical and horizontal wells, based on the production test data of three naturally fractured reservoirs. The rock compaction and the inertialflow resistance in the naturally fractured reservoirs are taken into account in these equations. Furthermore, theoretical models are presented to consider reservoir engineering, production performance, and surface facility performance. These models are then applied to evaluate and determine the POMs for two naturally fractured reservoirs. These two field applications show that stable flowing performance can be predicted accurately, and that artificial lift methods, such as sucker-rod pumps, can be mployed efficiently under certain reservoir conditions. The detailed field application results indicate that most of the POMs, as suggested by the theoretical models, are technically feasible and economically viable. Introduction Naturally fractured reservoirs are found in all types of lithologies and throughout the geological stratigraphic columns. However, the initial high oil rates seen in these reservoirs have misled petroleum engineers, in many instances, to overestimate their future production performance. Thus, the development of naturally fractured reservoirs has resulted in numerous economic failures(1). Meanwhile, field practices show that the selection of appropriate production operation methods (POMs) is critical to the long-term profitability of most producing wells(2–8). An improper choice can not only substantially reduce oil production, but also greatly increase operating costs. Once a POM is employed in a producing well, usually this POM remains unchanged, regardless of whether it will still be the optimal choice under subsequent conditions. Therefore, both the accurate prediction of well inflow performance and the appropriate selection of POMs are of great benefit to the optimal development of naturally fractured reservoirs. In general, it is difficult to characterize naturally fractured reservoirs, predict their oil production, and further determine suitable POMs. The well inflow performance relationship (IPR), which represents the well deliverability to produce fluids, is the first component to be considered in the process of selecting POMs(9). In the literature, although there are some formulas available for evaluating well performance, few are derived on the basis of production test data. Gubkina(10) presented a formula for evaluating the vertical well inflow performance in naturally fractured reservoirs, which was later improved by Bacnev et al.(11) However, the effect of well completion on well inflow performance was neglected. Joshi(12) and Karcher et al.(13) developed models to evaluate the horizontal well inflow performance in naturally fractured reservoirs. Mullane et al.(14) improved Joshi's method to achieve better forecasts. Other formulas(15–17) were developed, in which several unknown quantities are difficult to obtain from oil fields.

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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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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

Citations3
Published2002
Admission routes2
Has abstractyes

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