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Record W2012104403 · doi:10.2523/iptc-11361-ms

Surveillance & Optimization of a Waterflooded Fractured Carbonate Reservoir

2007· article· en· W2012104403 on OpenAlexafffundabout
Larry M. Dittaro, Connie Schwindt, Trevor Lane Holding

Bibliographic record

VenueInternational Petroleum Technology Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of ReginaImperial Oil (Canada)
FundersImperial Oil Limited
KeywordsPetroleum engineeringGeologyInflowCompletion (oil and gas wells)Water injection (oil production)Injection wellEnvironmental geologyPermeability (electromagnetism)GeobiologyWell loggingCarbonateEconomic geologySampling (signal processing)Fossil fuelNatural gasPetroleumMining engineeringRegional geologyGeotechnical engineeringEngineeringHydrogeologyMetamorphic petrology

Abstract

fetched live from OpenAlex

Abstract The surveillance and optimization of Norman Wells, a waterflooded fractured-carbonate reservoir in northern Canada, is presented. Discovered and on-production since 1920, Norman Wells is one of Canada's largest conventionally produced oil fields. Full development of this resource was undertaken in the early 1980's when directional and horizontal drilling technology allowed access to the majority of the reservoir, which underlies the Mackenzie River. Several artificial islands were constructed to provide pads for directional injection and production wells. The five-spot waterflood patterns were aligned and elongated to take advantage of the directional permeability associated with the natural fracture system. To optimize production and increase ultimate recovery, a sophisticated multi-disciplinary approach to reservoir and production surveillance has been employed. Basic surveillance methods including frequent well testing, fluid sampling, and gas sampling to ensure the accurate allocation of volumes. Surface pressure measurements have been used to accurately allocate production/injection volumes and monitor the status of the wells. Pressure measurements, including static, build-up, fall-off, flowing, and interference well tests have been utilized to monitor reservoir pressures, inflow/outflow performance and reservoir connectivity. Waterflood conformance has been assessed through the use of tracers, cased-hole production logging and injection logging. Waterflood effectiveness has been optimized through the use of voidage replacement analyses, Hall plots, the imposition of injection targets, and staging of fresh and produced water volumes. A holistic assessment of the surveillance information gathered has been accomplished through the use of streamline and floodfront analyses, material balance models, and several generations of full-field reservoir simulations. Ongoing optimization of the depletion of this complex reservoir has resulted in a 20% increase in the expected ultimate recovery since full-field start-up. An example of how the surveillance information has been utilized to improve reservoir performance is presented. prevalent Introduction The Norman Wells oilfield is located approximately 150 kilometers south of the Arctic Circle and lies 450 meters beneath the Mackenzie River in Canada's Northwest Territories (Fig. 1). Oil staining and hydrocarbon seepages had been reported by fur traders and explorers along this portion of the Mackenzie River since the early 1700s. In 1920, Imperial Oil Limited drilled the Norman Wells discovery well on the north bank of the river near these seeps. A refinery was built on-site and from 1921 to 1944 three wells were produced on a seasonal basis to supply the modest needs of the local market (Journal of Canadian Studies, 1981). The advent of World War II and the subsequent threat to Alaska posed by the Japanese occupation of the outer Aleutian Islands prompted the Canadian government, the United States Army, and Imperial Oil to sign the CANOL agreement. The CANOL project was designed to provide a supply of oil to Alaska free from the submarine threat to tankers. This agreement provided for the drilling of 63 additional wells on the mainland and two natural islands, construction of a refinery alongside the Alaska Highway in Whitehorse (1000 km southwest of Norman Wells) and a pipeline between Norman Wells and Whitehorse. Completed in February 1944, the CANOL pipeline was only in operation for 13 months before being shut in at the end of the war. Following the war, the field reverted back to its former level of production as a local supplier. Annual average production at Norman Wells, which had peaked at nearly 900 m3/d in 1944, quickly dropped to about 80 m3/d in 1946. In the following three decades, local demand for Norman Wells products increased to approximately 450 m3/d. In 1981 Imperial Oil initiated a full-field expansion of the Norman Wells field. This expansion used the latest technology in artificial island construction and directional drilling to fully access the remainder of the reservoir beneath the river. A field-wide five-spot waterflood program was implemented and in 1985 pool production increased from 450 m3/d to over 4000 m3/d. A total of 237 new directional wells (producers and water injectors) were drilled (Fig. 2) and a 870 km pipeline was constructed to bring the Norman Wells crude oil to markets in Alberta.

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: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.663

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 integrity0.0000.000
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 head0.016
GPT teacher head0.271
Teacher spread0.255 · 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

Citations2
Published2007
Admission routes3
Has abstractyes

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