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Record W2026873340 · doi:10.2118/2003-039

Cost-Effective Regulatory Compliance Management Solutions for the Petroleum Industry

2003· article· en· W2026873340 on OpenAlexaboutno aff
Reidar Visser

Bibliographic record

VenueCanadian International Petroleum Conference · 2003
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleumCompliance (psychology)Petroleum industryBusinessRisk analysis (engineering)Petroleum engineeringComputer scienceEngineeringChemistryEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract The Canadian petroleum industry spends large amounts of money and uses other critical resources to comply with a myriad of regulatory requirements and operational standards. The tasks of collecting and reporting information required for regulatory compliance are generally very onerous, which means that little time is available for adequate compliance management and data analysis. To compound the problem, regulations and standards change. From a due diligence perspective, these changes should be tracked and communicated to the appropriate employees because they may directly affect facility operations and production. Experience shows that a number of companies are unaware of the scope and magnitude of these regulatory requirements and are not properly equipped, from both a technical and business process perspective, to ensure their company complies with them. This presentation will begin with an overview of recent regulatory changes or initiatives that affect companies operating in the petroleum industry. The presenter will then provide a brief description of various web-delivered and wireless-enabled software applications that increase operational efficiencies, improve worksite health and safety processes, streamline field data capture and reporting processes, and substantially reduce the time and costs associated with achieving and maintaining regulatory compliance. These applications include mobile field data collection tools that allow for instant report generation and e-mail notification of non-compliant items. Finally, a case study will be presented that illustrates how BCI's software solutions have enabled a major oil and gas company to achieve "zero compliance issues". Particular emphasis will be placed on how BCI's handheld applications work in conjunction with server technology to facilitate the notification of compliance events, and reduce compliance costs and field data collection time by over 50%. Compliance Issues At The Workplace A worker is in the process of mixing caustic soda and sulfuric acid in a pipe. Initially, there is no reaction. After removing his protective eye safety gear to write down some notes, the worker peers back into the top of the pipe. The moment he looks in, a violent chemical reaction occurs that blasts highly caustic chemicals into his face. He becomes blinded, severely disfigured, and scarred around his head and facial area. A later investigation reveals that the worker in question had not been properly supervised or trained on how to conduct this procedure, and had not consulted the relevant Material Safety Data Sheets.i Company XYZ is a distributor of chemicals, and routinely receives tanker cars filled with liquid chemicals at its plant. An experienced employee inadvertently reverses the hose connections to the pumping equipment so that the chemical xylene is pumped to a rail tanker car already full of the chemical. The employee fails to monitor the transfer as required and as a result 18,000 litres spilled onto the ground. The transfer was stopped 10 minutes later, after a neighbouring business alerted the fire department. The xylene was absorbed into the ground and some made its way to the storm sewer catch basin located on the property. Some of the chemical entered the city's sewer system. ii

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: none
Teacher disagreement score0.904
Threshold uncertainty score0.669

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 head0.065
GPT teacher head0.297
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.

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
Published2003
Admission routes1
Has abstractyes

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