GRIF is evolving and so is its website!

With a new visual identity and for a better digital accessibility, on PC and mobile.

Discover it here!

>> <<

GRIF, Technology of TotalEnergies, offers you a complete range of services to stay at the forefront of your reliability studies, whatever your sector of activity.

To learn more about the new website and its functionalities, read the article: Welcome to the new GRIF software suite website!

Note: the website will remain online until the end of 2022. For any further information, please contact our team.


GRIF is a systems analysis software platform for determining the essential indicators of dependability: Reliability – Availability – Performance – Safety.

GRIF enables the user to choose the most suitable modelling technique for solving the
system under study: block diagrams, fault trees, Markov graphs or Petri nets. The software includes pre-configured architectures, making modelling all the easier.

GRIF was developed within TotalEnergies and is the fruit of 25 years of Research and Development. This platform therefore features mature, high-performance computation engines and modelling capabilities that meet the needs of any reliability study.

GRIF is structured into 3 packages

The Boolean package includes the 7 modules.

  • The BFiab module is based on reliability block diagrams modelling.
  • The Tree module serves to model system as a fault tree.
  • The ETree module serves to model system as a event tree.
  • The SIL module is used to evaluate Safety Instrumented Functions (SIF) based on the computation of Safety Integrity Levels (SIL) in conformity with IEC 61508 & 61511 standards
  • The Reseda module serves to model reliability network.
  • The Risk module dedicated to risk analysis.
  • The Bool module allows to mix the different models in an unique model.

These modules use ALBIZIA, the new BDD (Binary Decision Diagrams)
developed by TotalEnergies.

The Simulation package includes the Petri, BStok, Petro and Flex modules.

  • The Petri module serves to model large, complex industrial systems using stochastic Petri nets with predicates and assertions.
  • The BStok module is used to model complex systems based on stochastic block diagrams.
  • The Petro module is used to model complex Oil and Gas multi-flow systems.
  • The Flex  module is used to model Multi flow diagram with user prototype created in Petri nets.

These modules use the MOCA-RP computation engine based on the MonteCarlo simulation and developed by TotalEnergies.

The Markovian package includes the Markov module.

  • The Markov module serves to model and compute small dynamic systems as Markov Graphs.
    This module uses the analytical computation engine Albizia-Markov, which processes multi-phase systems.

The use of GRIF in the TotalEnergiesenvironment

GRIF can be used to check whether the reliability of safety-instrumented functions such as HIPS (ultimate safety barrier against scenarios of over-pressure, overflow, etc.) meets SIL (Safety Integrity Level) requirements. To avoid any accident, it is essential to ensure that the architecture selected and the associated maintenance policy offer an efficient response when faced with the unwanted event.

By modelling the architecture, behaviour (failures, interactions between pieces of equipment, etc.) and operating conditions (logistics support, maintenance policies, etc.) of an installation, GRIF makes a forecast of this system’s production level throughout its entire lifecycle. GRIF acts as a decision-making tool for assessing the cost-efficiency of a project by predicting its performances.

The variety and relevance of the methods

With the help of various computation techniques, GRIF will evaluate the reliability and availability of any system, be it an oil platform, a plane, a train, a water supply system, etc.

The variety of the methods managed by GRIF enables users to choose the one best suited to the situation: analytical, simulation, etc.

Versatility of the models

GRIF adapts perfectly to the complexity of most problems, thanks to the diversity and flexibility of its modelling languages. If the targets are not reached, it is very simple to test different architecture variants or to modify the input parameters so that their impact on results can be measured as quickly as possible.


GRaphical Interface for reliability Forecasting