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Record W4236252564 · doi:10.2118/2001-012

Determination of Production Operation Methods in Naturally Fractured Reservoirs

2001· article· en· W4236252564 on OpenAlexafffund
Daoyong Yang, Q. Zhang, Yongan Gu

Bibliographic record

VenueCanadian International Petroleum Conference · 2001
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProduction (economics)Petroleum engineeringComputer scienceGeology

Abstract

fetched live from OpenAlex

Abstract There are many naturally fractured reservoirs in the world, but few of them are optimally developed. In fact, it is difficult to characterize the naturally fractured reservoirs and predict the oil production, needless to mention the determination of appropriate production operation methods (POMs). Although there have been some formulas for evaluating well performance, a few were derived on the basis of production test data. In this paper, several general formulas are developed for evaluating inflow performance of both vertical wells and horizontal wells, based on the production test data obtained from three naturally fractured reservoirs. The influence of rock compaction and the inertial flow resistance in naturally fractured reservoirs are considered in these equations. Furthermore, theoretical models are also presented, into which reservoir engineering, production performance and surface facility performance are incorporated. These models are then applied to evaluate and determine the oil well POMs for two naturally fractured reservoirs. It has been shown from these two field applications that stable flowing performance, including its ceasing conditions, can be predicted. And artificial lift methods such as sucker-rod pumping can be efficient under certain reservoir conditions. The detailed field application results indicate that most of POMs determined from 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, initial high oil rates have misled engineers in many instances to overestimate production forecasts of wells. Thus development of the naturally fractured reservoirs results in numerous economic failures(1). Meanwhile, field practices show that 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 production but also greatly increase operating costs. Once a type of POM has been determined to install on a producing well, usually the POM is unchanged, whether it was and still is the optimal choice under existing conditions. Therefore, It is essential that both accurate prediction of well inflow performance and appropriate selection of POMs be of great benefit to the optimal development of the naturally fractured reservoirs. In general, it is difficult to characterize the naturally fractured reservoirs, predict the oil production and further determine suitable POMs. The well inflow performance relationship (IPR), which represents the well's ability to produce fluids, is the first component to be considered in the process of selecting POMs(9). In the literature, although there have been some formulas for evaluating well performance, few were derived on the basis of production test data. Gubkina(10) presented a formula for evaluating vertical well inflow performance in the naturally fractured reservoirs, which was later improved by Bacnev et al.(11). However, the effect of well completeness on well inflow performance was not accounted for. To evaluate the horizontal well inflow performance in naturally fractured reservoirs, Joshi's formula(12) is modified to achieve better forecasts(13,14).

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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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.313
Teacher spread0.287 · 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".

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Citations1
Published2001
Admission routes2
Has abstractyes

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