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Data Management in Oil & Gas: A Model
by: Luis D. Mateos, CEO
The Oil & Gas Data Challenge
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• Finding supplies in sufficient quantities so producing oil and gas is plausible.
• Finding and producing hydrocarbons is technically challenging and economically risky.
• The E&P process generates a huge amount of data; it is necessary to integrate and interpret this data to drive decisions, which leads to: • safely finding new resources, • increasing recovery rates • reducing environmental impacts.
Challenges
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• Managing the Product Lifecycle through efficient systems with readily-available, real-time, accurate information, while still controlling the components of information security (Confidentiallity, Integrity, Availability – CIA)
• Devising a framework to meet these management and security requirements while still being able to adapt to the ever-changing needs of the industry.
Challenges
The Reference
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Data Governance, the starting point
Data Governan
ceQuality
Architecture
Operations
Exploitation Security
Master Data
Content & Metadata
Warehousing
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1. Planning
2. Collection
3. Assurance
4. Identification
5. Preservation
6. Discovery
7. Integration
8. Analysis
Data Lifecycle
The Model
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The High-Level Oil Lifecycle
© Cairn Energy
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An Ontological Approach - example
Dataset: Well Drilling
Data
Dataset: Financial
Information
Dataset:Extraction & Production
Dataset:GIS, CAD, etc.
Highly Critical Information
Critical Information
© Mateos Consultores
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Criteria
© Mateos Consultores
Confidentiality:Determines how many individuals (and their hierarchy) have access to the dataset. This allows to infer other information such as availability and precision. This is the main criterion for determining Relevance.
Transactionality:This can tell us how fast a dataset grows in time. The faster the dataset grows, it will mean that a single data entity in the dataset will be less relevant. This also tells how big a dataset is as well as accessibility options through ‘big data’ interfaces.
Validity:Based upon the Oil Lifecycle, the validity of the data is the criterion that allows us to know when a dataset starts to gain/lose relevance. It will also allow the software tool to establish timeframes to move data from one dataset to another (e.g. From current to historical).
Relevance:The result of the combination of the 3 aforementioned criteria.
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CriteriaLow Confidentiality
Validity | Transactionality Low Medium HighLow Low Low LowMedium Low Low MediumHigh Low Medium Medium
Medium ConfidentialityValidity | Transactionality Low Medium HighLow Medium Medium MediumMedium Medium Medium HighHigh Medium High High
High ConfidentialityValidity | Transactionality Low Medium HighLow High High HighMedium High High CriticalHigh High Critical Critical
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Dataset 2
Sorting Datasets
Con
fiden
tiali
ty
Transactionality
Valid
ity
Dataset 1
Dataset n
…
Dataset 3
High Confidentiality & Medium Validity & High Transactionality= Critical Relevance
Medium Confidentiality & Low Validity & Medium Transactionality= Medium Relevance
Low Confidentiality & Medium Validity & Medium Transactionality= Low Relevance
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Future work
• We are currently working on defining the ‘gold standard’ ontology and the Best Practice guidance that accompanies the ontology, in order to have a complete semantic dictionary to build upon
• Once the ontology is finished, we need to predetermine some criteria and parameters for each dataset; this will require an enormous amount of data analysis, industry surveys and collaboration with companies within the ecosystem
• As Best Practices experts, we recognise that the standard is incomplete without a software tool. We are seeking big data experts to build together this software
• We are also accepting investments/funding for this endeavor
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Questions?
Feel free to contact us for research collaboration
