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Record W4244793521 · doi:10.2523/89453-ms

Recent Successes: MEOR Using Synergistic H2S Prevention and Increased Oil Recovery Systems

2004· article· en· W4244793521 on OpenAlexaboutno aff
Donald O. Hitzman, Michael Aaron Dennis, D.O. Hitzman

Bibliographic record

VenueProceedings of SPE/DOE Symposium on Improved Oil Recovery · 2004
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCitationLibrary scienceComputer scienceOperations researchEngineering

Abstract

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Recent Successes: MEOR Using Synergistic H2S Prevention and Increased Oil Recovery Systems Donald O. Hitzman; Donald O. Hitzman Geo-Microbial Technologies GMT Search for other works by this author on: This Site Google Scholar Michael Dennis; Michael Dennis The LATA Group Search for other works by this author on: This Site Google Scholar Daniel C. Hitzman Daniel C. Hitzman Geo-Microbial Technologies GMT Search for other works by this author on: This Site Google Scholar Paper presented at the SPE/DOE Symposium on Improved Oil Recovery, Tulsa, Oklahoma, April 2004. Paper Number: SPE-89453-MS https://doi.org/10.2118/89453-MS Published: April 17 2004 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Hitzman, Donald O., Dennis, Michael, and Daniel C. Hitzman. "Recent Successes: MEOR Using Synergistic H2S Prevention and Increased Oil Recovery Systems." Paper presented at the SPE/DOE Symposium on Improved Oil Recovery, Tulsa, Oklahoma, April 2004. doi: https://doi.org/10.2118/89453-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Improved Oil Recovery Conference Search Advanced Search AbstractContemporary investigations of Microbial Enhanced Oil Recovery (MEOR) technologies began in earnest in the 1980's. Since then, numerous programs have examined the reliability of the concepts, processes, and technical efficacy of recovery biosystems. Despite reported successes, MEOR technologies have not been widely accepted by the oil and gas industry as a means to recover residual oil. The Bio-Competitive Exclusion (BCX) biological process has successfully demonstrated significant oil recovery results in mature waterfloods from the deliberate alteration of indigenous reservoir microflora to an anaerobic denitrifying bacteria (DNB) population through the use of nitrate-based formulae as alternate electron acceptors and microbial nutrient. These environmentally safe inorganic salts complement the naturally occurring volatile fatty acids (VFA) in the reservoir and produced waters, selectively stimulating and increasing the targeted DNB. This designed and controlled manipulation of an indigenous DNB population initiates and increases the production of multiple gas, solvent, and surfactant bio-products which have known and demonstrable oil recovery properties. The BCX technology offers a feasible, practical, and very cost effective, tertiary oil recovery system that concurrently controls reservoir and surface system souring. Field data from California, Oklahoma, China, and Canada are presented.IntroductionDuring its history, the petroleum industry has accumulated a large body of knowledge and evidence of indigenous microbial populations in oil and gas reservoirs and producing systems.1 It is well established and undisputed that certain bacteria - namely sulfate reducing bacteria (SRB) - are extremely harmful to production operations. The logical response has been to attempt the eradication of the detrimental microflora by applying a wide range of biocides.It is only in recent years that biological technology innovations and new evidence of favorable economics and effectiveness have enabled industry to reconsider its approach to existing in-situ microbial populations. It is now known that microbial consortia activity is a potentially powerful process that can profoundly and beneficially affect the entire reservoir, production volumes, and related production system infrastructure.The old axiom that microorganisms cannot significantly influence reservoirs has been disproved by the progressive biogenic sulfide contamination of massive reservoirs and production facilities. Souring, hydrogen sulfide (H2S) corrosion, iron sulfide plugging, related financial costs and the threat to worker health and safety has been well documented throughout the global industry. Prolific SRB growth and activity best illustrates the potential for microorganisms to quickly affect entire systems, no matter the size, and validates the potential for an alternate, beneficial replacement microbial community to exert a very large positive effect within an observable and acceptable timeframe.As a method that exploits this potential, the BCX process targets beneficial indigenous microbial species and deliberately manipulates the entire reservoir microflora to release trapped oil in commercial quantities, as well as permanently inhibit the production of sulfide by SRB.This oil-releasing/sulfide-destroying biological system has proven its effectiveness in the field and is initiated and sustained by the injection of low cost nutrient products, first introduced to industry as Max-Well 2000. The BCX system functions in-situ, perpetually stimulating targeted indigenous de-nitrifying microorganisms (DNB) to produce chemical and physical oil-releasing agents. It is literally a gas and chemical "factory" that is situated within the reservoir, where its effectiveness is optimized.2 Keywords: recovery, oil recovery, corrosion, recovery system, biocide, effectiveness, souring, microbial method, enhanced oil recovery, srb Subjects: Improved and Enhanced Recovery, Microbial methods This content is only available via PDF. 2004. 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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.246
Teacher spread0.232 · 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

Citations5
Published2004
Admission routes1
Has abstractyes

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