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Record W1965346889 · doi:10.2118/1213-0094-jpt

Integrated-Asset-Modeling Approach for Reservoir Management on North Slope

2013· article· en· W1965346889 on OpenAlexaboutno aff
Adam Wilson

Bibliographic record

VenueJournal of Petroleum Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsAsset (computer security)Asset managementGeologyPetroleum engineeringComputer scienceOperations researchEnvironmental scienceEngineeringBusiness

Abstract

fetched live from OpenAlex

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 158497, ’Integrated Asset Modeling for Reservoir Management of a Miscible WAG Development on Alaska's Western North Slope,’ by R.D. Roadifer, SPE, ConocoPhillips Alaska; R. Sauve, Schlumberger; R. Torrens, SPE, Schlumberger Middle East; H.W. Mead, SPE, N.P. Pysz, SPE, and D.O. Uldrich, SPE, ConocoPhillips Alaska; and T. Eiben, ConocoPhillips Canada, prepared for the 2012 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 8-10 October. The paper has not been peer reviewed. An integrated-asset-model (IAM) approach has been implemented for the Alpine field and eight associated satellite fields on the western Alaskan North Slope (WNS) to maximize asset value and recovery. The IAM approach enables the investigation of reservoir- and facilities-management options under existing and future operating constraints. The technology used for managing the fields consists of fullfield compositional reservoir-simulation models for each reservoir integrated with a pipeline-surface-network model and a process facility model. Developing an IAM Reservoir-Management Needs. As with the construction of any reservoir or surface model, the first step in developing an IAM is to define with as much clarity as possible the objectives in undertaking such an endeavor. A detailed list of all possible objectives for creating an IAM covering all possible development and operational situations that might occur would be quite extensive. As is often the case, however, simply identifying the “big rocks” will allow the finer details of those leveraging aspects to be identified and planned for in the development of the IAM. The Alpine field anchors the westernmost oil-production and -processing facility on Alaska’s North Slope (Fig. 1). Discovered in 1994, the Alpine field is in the Colville River delta, 6 miles south of the Arctic Ocean and approximately 70 miles west of the Trans-Alaska Pipeline. The Alpine field began production in November 2000 and continues development today. Subsequently, satellite fields, including the Fiord-Nechelik, Fiord-Kuparuk, Nanuq-Kuparuk, Nanuq-Nanuq, Qannik, and Alpine-Kuparuk, have been brought on line and continue to be developed. Additionally, several fields in the National Petroleum Reserve have the potential to be developed. The common theme across all these developments is that they are or will be produced through the Alpine Central Facility (ACF). The ACF is a single-train processing facility. The only significant fluid that leaves the ACF is the sales oil. All other gas not used for fuel or lift must either be blended for injection as miscible injectant (enriched lean-gas injectant) or be injected as lean gas into two black-start gas-injection wells. All produced water must be reinjected and, for pipeline-integrity reasons, must be segregated from imported makeup seawater used for injection.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.292
Threshold uncertainty score0.606

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 integrity0.0000.001
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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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