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Record W4246796378 · doi:10.2118/2006-123

Economic Analysis for Enhanced CO2 Injection and Sequestration Using Horizontal Wells

2006· article· en· W4246796378 on OpenAlexaff
P. Gui, X. Jia, J.C. Cunha, L.B. Cunha

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCarbon sequestrationEnvironmental scienceChemistryCarbon dioxide

Abstract

fetched live from OpenAlex

Abstract Carbon dioxide flooding has been recognized widely as one of the most effective enhanced oil recovery processes applicable to light to medium oil reservoirs. Moreover, the injection of CO2 into an oil reservoir is a promising technology for reducing greenhouse emissions while increasing the ultimate recovery of oil. Numerical reservoir simulation is an important and inexpensive tool for designing EOR CO2 projects and predicting optimal operational parameters. In this work, reservoir simulations performed with a compositional simulator were applied to investigate the macroscopic mechanisms of CO2 injection processes. Horizontal injectors were used to increase injectivity. Compared to traditional vertical wells, horizontal wells are more attractive to improve CO2 flooding economics by increasing injection rate, improving areal sweep and increasing CO2 storage. The effects of several important parameters on the performance of the CO2 process were studied to optimize the process. Operational parameters such as the primary production time, the injector pressure and length, injection time as well as production well pressure and different production schemes were investigated to determine the optimal operating conditions for simultaneous objectives of higher recovery and higher CO2 storage. The application of CO2 flooding using horizontal wells can shorten project life, which is critical to its economics. The simulation results served as the basic input parameters for the economic analysis performed. Furthermore, NPV (net present value) results were used to optimize the profitability of the project and to compare the CO2 application using vertical and horizontal wells. The analysis used actual design parameters, including equipment and operating costs similar to the ones associated with current ongoing projects. The evaluation emphasized the importance of reservoir characteristics, optimum design of operation parameters and economical factors in the economic feasibility of CO2 injection projects for enhanced oil recovery and sequestration. Introduction Carbon dioxide flooding process can increase oil recovery by means of swelling, evaporating and lowering oil viscosity. Many injection schemes using CO2 have been applied[1], including CO2 gas injection (continuously), CO2 gas slug followed by water, etc. Currently, atmospheric concentration of CO2 is raising increasingly concerns and different possibilities for CO2 sequestration are being studied, including CO2 storage in abandoned gas and oil fields. This paper studies CO2 flooding process using horizontal wells to simultaneously enhance recovery and increase CO2 storage. Obviously this is an economic and environmental issue which optimization will contribute to reach the two abovementioned objectives. Although usually specific economic, social, and environmental indicators should be studied systemically, this paper mainly focuses on the economic analysis of CO2 flooding and sequestration processes. Economic analysis is especially important in a CO2 flooding project because most of such projects have high investment and operating costs and low profit expectation. This paper studies the application of conventional CO2 miscible flooding process (continuous injection) using horizontal wells. Horizontal wells are become more cost effective with increasing productivity performance and decreasing drilling and completion costs. Comparison is made between schemes using vertica

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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 score0.295
Threshold uncertainty score0.983

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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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
Published2006
Admission routes1
Has abstractyes

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