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Record W1896605274 · doi:10.2172/824743

INTEGRATED GEOLOGIC-ENGINEERING MODEL FOR REEF AND CARBONATE SHOAL RESERVOIRS ASSOCIATED WITH PALEOHIGHS: UPPER JURASSIC SMACKOVER FORMATION, NORTHEASTERN GULF OF MEXICO

2003· report· en· W1896605274 on OpenAlexaboutno aff
Ernest A. Mancini

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsShoalGeologyReefReservoir modelingCarbonatePetrophysicsPetroleumPetroleum engineeringPaleontologyGeomorphologyGeotechnical engineeringOceanographyPorosity

Abstract

fetched live from OpenAlex

The University of Alabama in cooperation with Texas A&M University, McGill University, Longleaf Energy Group, Strago Petroleum Corporation, and Paramount Petroleum Company are undertaking an integrated, interdisciplinary geoscientific and engineering research project. The project is designed to characterize and model reservoir architecture, pore systems and rock-fluid interactions at the pore to field scale in Upper Jurassic Smackover reef and carbonate shoal reservoirs associated with varying degrees of relief on pre-Mesozoic basement paleohighs in the northeastern Gulf of Mexico. The project effort includes the prediction of fluid flow in carbonate reservoirs through reservoir simulation modeling that utilizes geologic reservoir characterization and modeling and the prediction of carbonate reservoir architecture, heterogeneity and quality through seismic imaging. The primary objective of the project is to increase the profitability, producibility and efficiency of recovery of oil from existing and undiscovered Upper Jurassic fields characterized by reef and carbonate shoals associated with pre-Mesozoic basement paleohighs. The principal research effort for Year 3 of the project has been reservoir characterization, 3-D modeling, testing of the geologic-engineering model, and technology transfer. This effort has included six tasks: (1) the study of seismic attributes, (2) petrophysical characterization, (3) data integration, (4) the building of the geologic-engineering model, (5) the testing of the geologic-engineering model and (6) technology transfer. This work was scheduled for completion in Year 3. Progress on the project is as follows: geoscientific reservoir characterization is completed. The architecture, porosity types and heterogeneity of the reef and shoal reservoirs at Appleton and Vocation Fields have been characterized using geological and geophysical data. The study of rock-fluid interactions has been completed. Observations regarding the diagenetic processes influencing pore system development and heterogeneity in these reef and shoal reservoirs have been made. Petrophysical and engineering property characterization has been completed. Porosity and permeability data at Appleton and Vocation Fields have been analyzed, and well performance analysis has been conducted. Data integration is up to date, in that, the geological, geophysical, petrophysical and engineering data collected to date for Appleton and Vocation Fields have been compiled into a fieldwide digital database. 3-D geologic modeling of the structures and reservoirs at Appleton and Vocation Fields has been completed. The models represent an integration of geological, petrophysical and seismic data. 3-D reservoir simulation of the reservoirs at Appleton and Vocation Fields has been completed. The 3-D geologic models served as the framework for the simulations. The geologic-engineering models of the Appleton and Vocation Field reservoirs have been developed. These models are being tested. The geophysical interpretation for the paleotopographic feature being tested has been made, and the study of the data resulting from drilling of a well on this paleohigh is in progress. Numerous presentations on reservoir characterization and modeling at Appleton and Vocation Fields have been made at professional meetings and conferences and a short course on microbial reservoir characterization and modeling based on these fields has been prepared.

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)0.001
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.821
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.036
GPT teacher head0.261
Teacher spread0.225 · 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
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
Published2003
Admission routes1
Has abstractyes

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