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Record W2794887440 · doi:10.2118/190182-ms

Multi-Well Analytical Solution for Coning Under Simultaneous Steady-State Flow of Three Phases

2018· article· en· W2794887440 on OpenAlexfundno aff
Eunnam Ahn, Russell T. Johns, Chonpatin Phaiboonpalayoi

Bibliographic record

VenueSPE Improved Oil Recovery Conference · 2018
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersEnergi SimulationPennsylvania State University
KeywordsFlow (mathematics)Steady state (chemistry)MathematicsMechanicsThermodynamicsChemistryPhysics

Abstract

fetched live from OpenAlex

Abstract Unwanted water or gas production from upward or downward coning can significantly erode profits in oil recovery processes. Simulation estimates of the magnitude and timing of coning can be erroneous owing to unknown reservoir heterogeneity, large grid blocks near the wells, and inaccuracies in simulation well models, such as that from Peaceman. This paper presents a multi-well steady-state analytical solution for coning of three phases (water, oil, and gas) flowing simultaneously. The solution for multiple wells is developed using superposition with a coning potential function that includes capillary pressure and relative permeability. The assumption of vertical equilibrium (VE) is made, which gives maximum crossflow vertically and therefore the largest possible coning. Any model for relative permeability and capillary pressure can be used, although we used Stone 2 for relative permeability and Brooks-Corey for capillary pressure. We give dimensionless inflow performance windows (IPW) to show the allowable physical window of three-phase rates and the maximum oil rate as a function of the water and the total well flow rate. The new potential functions are also used to demonstrate superposition for several well patterns with no-flow boundaries. Besides estimating critical oil rates, the solution could be important to benchmark numerical solutions and improve the accuracy of Peaceman's well model.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.031
GPT teacher head0.275
Teacher spread0.244 · 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
GenreMethods

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

Citations1
Published2018
Admission routes1
Has abstractyes

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