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Record W2020012380 · doi:10.2118/01-06-05

Water Quality Considerations Resulting in the Impaired Injectivity of Water Injection and Disposal Wells

2001· article· en· W2020012380 on OpenAlexfundno aff
D.B. Bennion, F.B. Thomas, Derya Y. Köseoğlu-İmer, Tao Ma, B. Schulmeister

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
FundersUniversity of Calgary
KeywordsPetroleum engineeringProduced waterDispose patternWater qualityEnvironmental scienceWaste managementWater injection (oil production)Injection wellEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Produced water is routinely re-injected into many producing or disposal formations as a portion of ongoing operations. The inherent success of these operations is conditional on the ability to successfully inject the required volume of water in an economic fashion below the fracture gradient pressure of the formation under consideration. Many issues affect the success of a potential injection project, including well placement, geometry, and inherent formation quality and relative permeability characteristics. In addition to these factors, poor injection or disposal water quality can compromise the effective injectivity of even high quality sandstone or carbonate formations, resulting in economic failures and the need for costly workovers and recompletions on a regular basis to facilitate injection operations. This paper reviews the state of the art in diagnosing and evaluating injection water quality, and determining the effect of various potential contaminants such as suspended solids, corrosion products, skim/carryover oil and grease, scales, precipitates, emulsions, oil wet hydrocarbon agglomerates, and numerous other phenomena that can result in the degradation of injectivity. Screening criteria are presented, which review the process of analyzing the quality of a given injection or disposal formation, and the associated injection and disposal water. Suggestions for improving water quality, when required, are provided. Introduction Water injection has been used worldwide as a means of pressure maintenance for improving recovery of hydrocarbons, as well as to dispose of unwanted produced water from oil and gas wells in an environmentally responsible fashion. Conditional to the success of this process is the ability to inject the required volume of water into the porous formation of interest at a pressure; in most cases, under the fracture pressure gradient of the reservoir (toaintain good conformance of the injected water within the target formation). A number of reservoir issues can obviously impact the ability to inject into the formation of interest, including inherent permeability, relative permeability effects associated with initial immobile fluid saturations, stress-induced issues, etc.(1) Mechanical formation damage issues associated with fines migration may also be problematic(2). In many cases, even if formation character and reservoir parameters are favourable, injection rates are compromised due to quality problems with the injected water. The purpose of this paper is to review common water quality problems, describe how they may impact potential injectivity in a water injection /disposal well, and then indicate screening and design criteria for acceptable water quality evaluation purposes. Injection Water Sources Water that is injected into porous formations for waterflood or disposal purposes can be sourced from a number of locations. The source of the water, its temperature and pressure path during production and injection operations, compatibility issues between blended waters, and possible seasonal variations in the water quality are all issues which may affect the overall quality of the injected water from a damage/impaired injectivity perspective. Common sources of injection/disposal water include:Produced formation water(s)Surface water sources (lakes, rivers, ocean, etc.)Shallow groundwater (potable)Deeper "wet" formations which act as water sources

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 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 score0.623
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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

Citations25
Published2001
Admission routes1
Has abstractyes

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