MétaCan
Menu
Back to cohort
Record W4240984359 · doi:10.2523/59119-ms

Environmental Management, Cost Management, and Asset Management for High-Volume Oil Field Waste Injection Projects

2000· article· en· W4240984359 on OpenAlexaboutno aff
Michael Bruno, Alex Reed, Susanne Olmstead

Bibliographic record

VenueProceedings of IADC/SPE Drilling Conference · 2000
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCitationAsset managementProject managementOil fieldComputer scienceLibrary scienceEngineeringBusinessManagementPetroleum engineeringEconomicsFinance

Abstract

fetched live from OpenAlex

Environmental Management, Cost Management, and Asset Management for High-Volume Oil Field Waste Injection Projects Michael Bruno; Michael Bruno Terralog Technologies Search for other works by this author on: This Site Google Scholar Alex Reed; Alex Reed Terralog Technologies Search for other works by this author on: This Site Google Scholar Susanne Olmstead Susanne Olmstead Terralog Technologies Search for other works by this author on: This Site Google Scholar Paper presented at the IADC/SPE Drilling Conference, New Orleans, Louisiana, February 2000. Paper Number: SPE-59119-MS https://doi.org/10.2118/59119-MS Published: February 23 2000 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Bruno, Michael, Reed, Alex, and Susanne Olmstead. "Environmental Management, Cost Management, and Asset Management for High-Volume Oil Field Waste Injection Projects." Paper presented at the IADC/SPE Drilling Conference, New Orleans, Louisiana, February 2000. doi: https://doi.org/10.2118/59119-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/IADC Drilling Conference and Exhibition Search Advanced Search AbstractOngoing exploration and production activity, combined with increased regulatory requirements, are increasing the volume and costs associated with disposal of oil field wastes, including produced oily sands and tank bottoms, drilling mud and cuttings, crude contaminated surface soils, and naturally occurring radioactive materials (NORM). A cost-effective and environmentally sound disposal option is to re-inject waste material into the subsurface into non-productive and/or depleted zones under controlled fracture conditions.High volume injection projects often involve annual injection exceeding several hundred thousand barrels of waste for several years. The critical engineering management goals for such operations are to:Maintain waste containment in the target formation (environmental management);Sustain long-term injectivity with minimum equipment repairs and well work overs (cost management); andMaximize formation storage capacity and well life (asset management).More than five years experience operating, analyzing, and managing large volume waste injection projects in the US and Canada has enabled Terralog to develop specific design, monitoring, and operating strategies to achieve these goals. Target injection formations must be selected with appropriate overlying barrier and absorption zones. Offset well completions must be carefully examined. The injection well completion should be appropriately designed to take into account high formation stresses and potential movement. Continuous monitoring and analysis must be performed to evaluate varying formation properties, injectivity, stress conditions, and fracture orientation and height growth. Finally, through continuous monitoring and analysis of formation response, injection parameters and properties (such as solids concentration, density, flow rate, shut-in time, etc...) can be adjusted in order to maintain containment, reduce operating costs, and optimize long-term injectivity.IntroductionDeep well injection of exploration and production wastes provides significant environmental and economic advantages over traditional landfill disposal for oilfield wastes. These include:Improved protection for surface and groundwater;Little impairment of surface land use;Reduced long-term liability risk to waste generator;Reduced transportation and disposal costs.The use of deep well injection, therefore, has expanded significantly in recent years1–5. For example, large-scale E&P waste injection operations have been ongoing in Canada (Srinivasan et al, 1997), Alaska (Schmidt et al, 1998), California (Hainey et al, 1997), and Louisiana (Baker et al, 1999).Large-volume injection, which may involve several hundred thousand barrels or more of waste material injected annually over several years, must be performed in relatively high porosity sands at fracture conditions. In spite of increased use of this technology, however, the mechanics of massive slurry injection into soft formations remain poorly understood, and there are few guidelines available to industry to optimize and manage this process. In some instances injection zones have filled or pressurized prematurely; well casings have been sheared by excessive formation movement; and in extreme instances waste material has broken out of zone and to the surface.To help improve industry practices, Terralog Technologies is currently engaged in a two-year research project, supported in part by the Canadian and US Departments of Energy, to develop and test improved fracture injection disposal techniques and diagnostic tools. This effort involves extensive field data assembly and analysis, model development, and field verification. Keywords: upstream oil & gas, waste injection project, bruno, formation response, enterprise performance management, drillstem/well testing, operation, injection project, tti 6, disposal Subjects: Hydraulic Fracturing, Formation Evaluation & Management, Drillstem/well testing This content is only available via PDF. 2000. IADC/SPE Drilling Conference 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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.186
Teacher spread0.178 · 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 designOther design
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

Citations6
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of IADC/SPE Drilling ConferenceSame topicDrilling and Well EngineeringFrench-language works237,207