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Data Management for Oil and Gas: Unconventional Data versus Big Data

Unconventional Data Management

In Unconventional Hydrocarbons, Big Data, and Analytics, Allen Gilmer of Drilling Info (DI,) explores unconventional data management in the hydrocarbon industry. Each day about 50 experiments take place.

Unconventional Data ManagementWhenever an experiment succeeds, properties change value instantaneously across the board. Knowing and acting on data resulting from experiments and incorporating it into your own information base gives you more substantive quantification.

Incorporating geological and petrophysical signatures associated with hydrocarbon production enhances the success rate for discovering hidden plays.

In the last 2 years, DI has re-architected their latest system to integrate interpreted geological data, while continuously updating statistical grading of acreage and operations in popular unconventional plays.

Over 100 terabytes of information from over two million historical inventories of scout cards and well logs from worldwide sources allow clients to accelerate their productivity.

Some of the numerous benefits of this emergent architecture are:

  • DI’s data and interpreted knowledge products will have a higher availability on other platforms.
  • Users can create immersive, secure environments, enabling them to view work from distributed teams.
  • Cross disciplinary collaborative workflows through the DI web, or as managed client components, will contribute to optimal data management effectiveness.

Improving Forecasting with Data Management

The Society of Petroleum Engineers published Probabilistic Performance Forecasting for Unconventional Reservoirs with Stretched-Exponential Model. The value of this approach in oil and gas exploration is investigated.

Unlike deterministic estimates, probabilistic approaches provide a measure of uncertainty in reserve estimates. In a probabilistic model, statistical analysis is used as a tool that performs estimates based on historic data and a set of current traits. The probability of an event occurring again is determined.

The Polish Geological Institute created a report for recoverable shale, gas and oil resources assessment, using a broad range of geological, geochemical, geophysical and geo-mechanical data. For a specific basin under analysis, some key data are still to be determined.

Data such as porosity and permeability of shale reservoir and gas composition falls into this category.

As a result of this, some assessment data is based partly on assumptions from analogue basins, and results in increased analytical error bars for calculation of hydrocarbon resources.

Data Lineage as part of Real Time Data Integration

Part of employing unconventional data may require using data lineage. With this technique, time becomes a characterizing aspect of data. Data lineage can trace data from its origin to its current state.

Many sources and transformations may have contributed to the final value within a given time segment. A selected instance of data may possess a lineage path that runs through cubes and database views, datamarts, intermediate staging tables and scripts.

The ability to view the lineage path visually fosters greater comprehension.

A System for Integrated Management of Data, Uncertainty, and Lineage

Stanford’s Infolab offers Trio as a new database management system especially designed to process data, data uncertainty, and data lineage. Trio is based on an extended relational model for uncertainty and lineage called ULDB. The SQL-based query language supporting it is TriQL.

Scientific and sensor data management, information extraction systems, data cleansing and approximate and hypothetical query processing are product representative.

Petris Technology Inc., is a supplier of data management and geosciences applications to the global oil and gas industry, and has implemented a Statoil project providing borehole data management technology.

Data management for unconventional resources provides the opportunity to create a new body of knowledge that will help guide producers by utilizing data based on a history of operational field data. The extraction of oil from oil sands and oil shale requires such advanced operations necessary for effective use of proppant and other highly expensive fracture fluids.

For more on data management in the field, check out this post on sorting out a common language for well data.

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