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Data maturity in geology

Friday, December 11, 2015

How a data management 'capability maturity model' could be used in geological and geophysical data management. By Naila Huseyn-zada, principal consultant data management, Landmark Software and Services (Halliburton) in Baku, Azerbaijan.

A capability maturity model (CMM) analysis enables you to identify the current level of data quality in an organization, and the means of both improving data management and moving forward to eliminate gaps.

This article describes how it could be used to assess the maturity of geological and geophysical data management. This method has been piloted on Landmark's OpenWorks database, and can be applied to any similar geo-data database.

CMM was originally developed by the Software Engineering Institute at Carnegie Mellon University as an objective assessment tool to measure the performance of government contractors on a software project. Later, this model gained worldwide popularity as a general model to aid in business processes.

Maturity levels

CMM uses five maturity levels to assess the data management maturity for geological and geophysical data.

Level I - The Initial Level. The lack of rules, standards, or procedures characterizes Level I as chaotic. While the use of a specialized database helps to structure data, the absence of a data management policy can create a situation in which data are available but not useful; data may exist by different names, which leads to duplication and data disorder. Additional, data sets may not be fully complete and may contain unconfirmed data with no assurance of accuracy.

Level II - The Recognizing Level. The movement from Level I to Level II calls for implementing data management rules. Quality requirements dictate the development and implementation of standards and procedures. Organizing the data increases the level of data integration within the project and allows only accurate and consistent data to be maintained within the project.

Level III - The Defining Level. Implementing the data management policy and adjusting all or most data types to stipulated data quality requirements enables the move from Level II to Level III up the data capability maturity ladder where data are treated as a corporate asset.

To successfully meet the requirements of Level III, data management should be centralized and standards and procedures applied consistently across the organization. However, at this level, an insufficient centralized data management process often leads to the creation of a variety of similar standards or to standards that can vary between asset teams. Consequently, integration across various projects can be a complex process.

Level IV - The Managing Level. Data on Level IV are already integrated into the corporate data structures and managed centrally. At Level IV, management of metadata, data about data, is implemented and the process of catalyzing all data into corporate data structures/repositories begins.

Level V - The Optimizing Level. At Level V, the data quality, standards, guidelines, and procedures are continually improved. Any changes applied to the data first go through data processing to meet the standards and documenting requirements within the metadata repository.

Weight factor

Because project data are not equal, the weight factor of data must be used. The weight factor is a numerical assignment that reflects the relative importance of a parameter as compared to other parameters.

Fundamental data: the functioning of all or most applications is impossible if these data are absent or of low quality. Data in this category include seismic data, well headers, well positioning data, and seismic survey data.

The Main data enable comprehensive research to be conducted on the basis of fundamental data, and are the primary results of this research. This category includes interpretation data, such as seismic horizons and faults, and log curve data.

Minor data increases the efficiency of analytical research; they are minor data both for applications and for users. Cultural data, well completion and plugging data, and data lists are examples of this category

Secondary Data includes intermediate or temporary data, such as application sessions and well notes.

Evaluation matrix

The next step is the development of an evaluation matrix.

At Level I, the data simply exists, and there are no requirements for them, in other words, data management comes from simply loading data to a project. At this level, one or another type of data is present in a project, enabling a quantitative analysis to be performed and the weight factor for each data type to be specified

Criteria of Level II include quality requirements, working standards, working procedures, and integration within a project, which indicate the presence of standards and procedures of one work group. These standards or procedures, however, may not be obligatory for other work groups. At the second level of maturity, it is assumed that all or most data have been processed in accordance with quality requirements, meaning that standards and conventions have been created.

Standards and conventions include naming conventions, standards and requirements of the mandatory data, quality requirements for reliability, and quality evaluation procedures.

They also include requirements for the need periodic data cleaning and procedures to implement data cleansing, data archiving requirements and procedures to implement archiving, integration and synchronization data between the same project in different locations, and/or integration and data synchronization with other projects.

At Level III, the standards and procedures are common for all work groups of the organization and the data can be integrated between various projects without additional processing. Such concepts include corporate quality requirements, corporate standards, corporate procedures, and integration across different corporate projects. The list of corporate requirements, standards, and procedures is similar to the list of the second level.

Level IV criteria include replication to a corporate repository, integration across various databases, metadata cataloging, and integration of projects at a corporate level. When a corporate data structure has been created, and data are replicated to the metadata repository and documented, the fourth level has created a basis for the next level.

At Level V, it is accepted that the replication to the metadata repository has occurred and that integration across various corporate repositories is in place.

Data evaluation

The next stage of the evaluation involves gathering information about the evaluated data of the project(s). Both quantitative and qualitative methods can be used to collect the information.

A poll of technologists and data management specialists provides qualitative information about the availability requirements, standards and procedures, application of standards, and procedures.

The final outcome of the analysis is the positioning of the projects on the maturity ladder. Conformity to levels can be low, medium, and high; this level is determined by the degree of conformity of all estimated data to the accepted criteria.

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