MétaCan
Menu
Back to cohort
Record W4245121131 · doi:10.2523/77671-ms

A Case Study: Using Modern Reservoir Characterization to Optimize Future Development of a Mature Asset

2002· article· en· W4245121131 on OpenAlexaff
John Campanella, Mark D. Sonnenfeld, Laura C. Zahm, James R. Gilman, Conrad Lara, Siemens Craig, Brian A. Zaitlin

Bibliographic record

VenueProceedings of SPE Annual Technical Conference and Exhibition · 2002
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCitationExhibitionArt historyAsset (computer security)Library scienceWeb siteComputer scienceHistoryArtWorld Wide WebThe Internet

Abstract

fetched live from OpenAlex

A Case Study: Using Modern Reservoir Characterization to Optimize Future Development of a Mature Asset John D. Campanella; John D. Campanella iReservoir.com Inc. Search for other works by this author on: This Site Google Scholar Mark D. Sonnenfeld; Mark D. Sonnenfeld iReservoir.com Inc. Search for other works by this author on: This Site Google Scholar Laura C. Zahm; Laura C. Zahm iReservoir.com Inc. Search for other works by this author on: This Site Google Scholar James R. Gilman; James R. Gilman iReservoir.com Inc. Search for other works by this author on: This Site Google Scholar Lara Conrad; Lara Conrad EnCana Corp. Search for other works by this author on: This Site Google Scholar Craig Siemens; Craig Siemens EnCana Corp. Search for other works by this author on: This Site Google Scholar Brian A. Zaitlin Brian A. Zaitlin EnCana Corp. Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. Paper Number: SPE-77671-MS https://doi.org/10.2118/77671-MS Published: September 29 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Campanella, John D., Sonnenfeld, Mark D., Zahm, Laura C., Gilman, James R., Conrad, Lara, Siemens, Craig, and Brian A. Zaitlin. "A Case Study: Using Modern Reservoir Characterization to Optimize Future Development of a Mature Asset." Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. doi: https://doi.org/10.2118/77671-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search Abstract Enhancements in software, hardware, expertise, data collection and interpretation have continued to improve the industry's ability to build detailed 3D geologic and dynamic flow models of the subsurface. These models in turn have improved our ability to make optimum reservoir engineering decisions for reservoir development. While such characterization has proved its worth for large assets over and over again, there continues to be much skepticism about the ability to use this process for mature or marginal assets.This paper describes a field example showing how a truly integrated approach can shorten the time frame and reduce the cost of the characterization process. This ensures that all the available data is being used in an efficient manner to optimize future recovery.Application of modern 3D reservoir characterization on mature fields is very problematic because of data quality issues, such as poor log quality, missing production data or lost cores. However, this lower quality data often provides bounds that are important in understanding the field performance and therefore should not be discarded. These data quality issues are addressed in this paper.Additionally this paper shows how a truly integrated approach shortens the time frame for development of detailed 3D geologic and dynamic models. For example early conceptual flow modeling and engineering analysis provides valuable input for constraining the static geologic model.Modern software tools and analysis techniques are greatly improving the ability to combine many scales of data. This provides a better understanding of the reservoirs and improved decision-making. The value of these tools is often a direct result of efficient analysis and visualization of the data and interpretations. These powerful capabilities should not be limited to large assets, as they can be cost effectively applied to mature properties also. Keywords: geological modeling, reservoir characterization, optimize future development, recovery, modification, permeability baffle, workflow, geomodel, reagan field, upstream oil & gas Subjects: Reservoir Characterization, Reservoir Simulation, Geologic modeling Copyright 2002, Society of Petroleum Engineers You can access this article if you purchase or spend a download.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.596

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)0.0000.000

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 head0.048
GPT teacher head0.286
Teacher spread0.237 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPE Annual Technical Conference and ExhibitionSame topicReservoir Engineering and Simulation MethodsFrench-language works237,207