iris In Brief
Additive colors combined in different proportions make all other colors. All colors added together form white light.
Last updated
Additive colors combined in different proportions make all other colors. All colors added together form white light.
Last updated
An analogy can be made between Dynamic Integration of Knowledge and the Model for Additive Light. This is, the “white light”, sweet-spot of sound decision making, comes from overlapping all different “beams of knowledge.”
Knowledge in oil & gas Major Capital Projects (MCPs) can be grouped in 3 areas “or beams”. These are: Petro-technical knowledge, which includes industry standards, IP of the E&P companies (e.g., internal engineering standards), SME know-how (often times not documented), predictive models (from rules of thumb, formulas, equations, numerical simulators) Business drivers such as procurement KPIs, logistics, financials which can be relatable to CAPEX, OPEX and revenue streams in areas supporting projects and operations, and Risk management framework. Note that oil & gas companies are intrinsically tied to risk, whether is technical risk (uncertainty in reserves, oil quality in a project), HSE, procurement, financial or regulatory.
iris integrates these "knowledge beams" through a robust, transparent and organic architecture, resulting in better and faster decision-making in the context of MCPs and operating assets.
iris is a good first-step for oil & gas companies looking to develop cognitive-type assistive solutions, leveraging sound, 'cutting edge' digital technologies, and collaborative methodologies (Agile, Design Thinking, Systems Engineering).
The current commercial release of iris, iris risk knowledge is an end-to-end application which can be used by different functions in oil & gas operators, EPCs, and OFS to categorize a large number of risks into a risk matrix. iris risk knowledge relies on a configurable Computational Knowledge Graph, which integrates oil & gas domain knowledge representations, risk classification rules, and Natural Language Processing algorithms. A web-based user interface allows users to upload a large number of risk entries, see iris' suggested risk classification, evaluate contributing factors and provide corrections and feedback, which can help iris be more accurate in the future.
iris risk knowledge was found to reduce effort in risk classification, increase consistency and uncover latent risks in Major Capital Projects, in a major oil & gas company. In the words of a technical Subject Matter Expert from this large oil & gas company: "iris is a game changer for our organization, it simplifies our work process, gives us confidence in our rating and preserves the collective knowledge for years to come"
The Maana platform provides key backbone functionality to iris. Maana is not only The Digital Knowledge Layer for Microsoft® Azure, but it also serves as a fertile environment for petro-technical users (usually adept to physical sciences), to effectively collaborate with data scientists and software developers. iris' backbone is intended to be expansive, and as transparent and engaging as its front end.
Want to know more? Contact us at iris-dl@maana.io
