NEMS Chemicals

NEMS Chemicals® - HOCNF simplified

NEMS Chemicals® is an online chemical management software, designed to handle eco-toxicological data in the form of Harmonized Offshore Chemical Notification Format (HOCNF).

Simplicity delivered

NEMS Chemicals is delivered as SaaS (Software as a Serice). No installation is needed. Only acces to a modern browser.

Our solution simplifies and reduces work related to preparing discharge applications and reports for oil and gas operators. The solution simplifies and reduces work related to HOCNF documentation for chemical suppliers.

NEMS Chemicals is the solution for environmental documentation of chemicals in the North Sea.

Competence included

NEMS Chemicals is developed and operated by NEMS and our KPD (Chemical Product Data) Centre. NEMS Chemicals is used for environmental management of offshore chemicals. More than 25 years of experience with registration and quality assurance of HOCNF data, on behalf of practically all oil companies operating on the Norwegian Continental Shelf, has given the KPD Centre a unique competence and knowledge about the requirements for an efficient chemical management tool.

A tedious job automated

NEMS Chemicals is designed to register information on chemical products and their substances, such as physical properties, hazard labelling and eco-toxicological properties (i.e. aquatic toxicity and the potential for bioaccumulation and bio-degradation).

Using NEMS Chemicals ensures automatic controls, and expert user QA of registered data for completeness according to OSPAR HOCNF.  The environmental color code is calcualted automatically.

The tool has the possibility to print out complete HOCNF reports, parts of the report, or even a short-version with the most important data summarized in one single page.

Used by suppliers, operators and authorities

Supplier, operator and authorities have access to the same data. This reduces risk through improved communication between the different stakeholders.

The tool includes the possibility to select data from self-made criteria, for instance, products filtered based on usage and environmental properties.