Why is Data Management Important for the Energy Sector?

(By Edy Irnandi Sudjana, Energy Data/Well Log Analyst practitioner lives in Qatar)

Background

Energy sector activities mainly for upstream oil and gas activities such as oil and gas well surveying, drilling, wireline logging, seismic surveys, production monitoring & surveillance and static & dynamic model generation by specialists (geoscientists and engineers) will produce very varied data, in terms of data types, format or amount of data. For example, for wireline logging operations, logging data will be generated in the form of digital formats such as the Digital Log Interchange Standard (DLIS) and Log ASCII Standard (LAS). In addition, a “field print" in the form of hard-copy or digital image such as in TIFF format will be provided by a wireline logging service company. These logging data are obtained both during the exploration phase, drilling (open-hole log) and at the production stage of a well (cased-hole & production log). After the data is acquired and processed to generate high values curves, then specialists in an energy company will use it as a data source along with other subsurface data to search for potential hydrocarbons, drilling new wells and also to monitor distribution of hydrocarbons, to monitor condition of wells & reservoirs and production optimization.

For these activities, a significant investment is made by Energy companies, for example the costs for open-hole logging activities represents 10% of the total well costs (Frank Jahn et al, Hydrocarbon Exploration and Production, Elsevier Science BV, 1998), while data acquisition costs of seismic accounts for 80% of the total cost of acquisition and processing of seismic data (Mamdouh R. Gadallah, Ray Fisher, Exploration Geophysics – An introduction, Springer, 2009).

For these activities, a significant investment is made by energy companies, for example the costs for open-hole logging activities represents 10% of the total well costs (Frank Jahn et al, Hydrocarbon Exploration and Production, Elsevier Science BV, 1998), while data acquisition costs of seismic accounts for 80% of the total cost of acquisition and processing of seismic data (Mamdouh R. Gadallah, Ray Fisher, Exploration Geophysics – An introduction, Springer, 2009).

This condition requires energy companies to have capability to manage data properly, treat the data as an important asset considering large investment spent to support the operational activities of the hydrocarbon exploration or the development and optimization of current production.

Furthermore, particularly for energy sector activities in Indonesia, in accordance with the regulations of Republic of Indonesia No.22 Year 2001 concerning Oil and Gas, Government Regulation Number 35 Year 2004 concerning Upstream Oil and Gas Business Activities and Minister of Energy and Mineral Resources Regulation No. 027 Year 2006 concerning the management and utilization of data obtained from general surveys, exploration and exploitation of oil and gas-- later superseded by the Minister of Energy and Mineral Resources Regulation No. 7 Year 2019 concerning management and utilization of Oil and Gas Data, it is stated that the data obtained from general surveys and/or exploration and production activities are owned by the state, thus energy companies are obliged to manage the data.

Basically, data management programs or activities involve three main components, namely:

• People

• Process or procedure, and

• Technology

• People

The role of the people function is to manage the entire data management program, including ensuring that this data management program is operating well. People consists of several teams in energy companies who are responsible for the data that generated, including the data management team to coordinate & monitor the sustainability of the data management program (governance function).

• Process

Process or procedure is a guideline for the implementation of data management activities which in principle consist of standard/nomenclature data, data handling procedures, process and data flow along with the role of the team responsible for each type of data. With this process/procedure clarifying the main tasks and functions between the teams involved in it.

• Technology

Data management technology, are the tools to support the implementation of data management activities in order to run efficiently, consisting of a computer system and data storage system, data management software and quality management & data analysis software.

The function of the data management software is as a centralized database for oil and gas data, thereby facilitating users in digitally cataloging data and facilitating the process of searching and retrieving data. The function of the data quality management & data analysis software is to monitor and improve data quality systematically and automatically, it is also capable of synchronizing the corrected data to other database or repository systems that are connected to the data quality management software.

The data management software and data quality management & analysis are integrated with software systems used by specialists, so that in the event that specialists need the data for analysis and interpretation purposes, they can be fulfilled in a relatively short time with maintained data quality.

These three components of data management are interrelated and support each other so that the objectives of the data management program can be achieved. Full support and endorsement from senior management of energy companies is needed so that the data management program can perform well and to support the achievement of the company's vision, mission and goals.

Upstream Oil and Gas Data Classification

As mentioned earlier, in upstream oil and gas activities, various types and formats of oil and gas data will be generated in the exploration, development and production phases, the following table contains classification, data type and acquisition time (Ganesh C. Thakur, Ph.D, Abdus Satter, Ph.D, Integrated Petroleum Reservoir Management: A Team Approach, PennWell Books, 1994); 1note for the Logging classification can also be obtained during the production phase or known as Cased-Hole Logging & Production Logging.

Industry Standards for Data Management

Currently, various industry standards for oil and gas data management are formed with the aim of assisting the oil and gas community in the world consisting of oil and gas companies, oil and gas service companies, governments, research/independent institutions, educational institutions/universities, and software developers to be more efficient and effective in implementing technology, “best practices” and data management standards in dealing with current and future data management needs and challenges.

The Open Subsurface Data Universe (OSDU)

The OSDU Forum enables the Energy industry to develop transformational technology to support the world's changing Energy needs, consisting of industry operators, software application developers, oil and gas service firms, and academic institutions, the OSDU Forum aims to change the oil and gas subsurface business by breaking down information silos, putting data at the center in a new data platform, and stimulating the development of new and innovative applications.

The OSDU Data Platform is the centerpiece of the OSDU project. It will collect and store all exploration data, development data, and production data in the same format on the same data platform and provide a well-defined set of application programming interfaces (APIs) that makes it easy for E&P companies to locate and access all relevant subsurface data.

The core principle of the OSDU Data Platform is to separate data from applications so that it becomes easier to access data, to experiment and innovate, and to improve the efficiency and accuracy of exploration and production processes.

The Professional Petroleum Data Management (PPDM)

This non-profit organization was founded in 1989, based in Calgary, Canada and consists of more than 100 organizations consisting of oil and gas companies, governments, research/independent institutions, software developers and oil and gas service providers. There are three main data management standards developed by PPDM, namely:

1. Data Repository.

An example of implementing a standard data repository is the PPDM version 3.9 relational data model and also PPDM lite 1.1 data model.

2. Data Exchange.

Application of data exchange standards based on XML and GML for data exchange, thus facilitating the process of reading data by various software.

3. Data Content

An example of the application of standard data content is "reference values" for reference data such as well status, coordinate system, and units of measurement.

Energistics

Formed in 1990 by several oil and gas companies, namely: BP, Chevron, Elf Aquitaine, Mobil and Texaco, at that time it aimed to facilitate the development, promotion and support of open standards for science, engineering and operational aspects of upstream oil and gas activities. Later it developed to facilitate the development, and adoption of data exchange standards for the upstream oil and gas industry. An example of a standard developed by Energistics is the Wellsite Information Transfer Standard Markup Language, shortened to WITSML. WITSML is a standard technology for exchanging wells, drilling, and workover data based on XML. The last active version of WITSML is currently 1.4.1.1

Data Quality Management System

Why is data quality management system & data analysis needed? What are the benefits of having data quality management system & data analysis?

Essentially we want the data in the current database or repository, which will be used by specialists for analysis and interpretation, free from data quality problems. Examples of upstream oil and gas data quality problems are as follows:

• Incomplete wellbore data, for example:

1) The exact surface location of an oil or gas well is not known or there is information on the location of the well, but the source or origin of the coordinate system for that location is unknown,

2) Incomplete well elevation reference information needed to determine the reference of well depth (depth point),

3) Directional surveying, Azimuth reference system is not known for instance whether True north or Grid north.

• Inconsistency of the same data in several database or repository systems; for example: the location of the same wellbore is recorded to differ significantly from one database or repository system to another,

• Incompatibility of data based on the rules/principles of the data. As example;

 1) Location of wells outside concession block managed by an energy company,

 2) The logging depth point or logger's depth is significantly deeper than the driller’s depth,

3) Production volume data shows the start of production before the well is completed.

These examples of data quality problems above will affect the company's performance when faced with the need for quality data and in a short time, for example for an oil and gas field development study project, due to the low level of confidence in the data and uncertainty about decisions taken based on the data.

The study stated that the costs incurred due to data quality problems reached at least 15% to 25% of the company operating costs (see USGS on the value of data management) and also took up to 50% of the time spent on a project required for the process of searching and organizing data if the data unmanaged, readily accessible and of high quality (see SPE PetroWiki on Reservoir Management>Quality Assurance )

The development of an integrated and automatic data quality management system, in stages and according to the priority needs of data consumers will help improve the data quality performance of an oil and gas company in a measurable manner, so that existing data quality problems can be handled properly and systematically.

Data Management Trends & Takeaway

A few notes for future trends related to Energy sector data management includes the following:

• Types & amount of data, structured & unstructured data both digital and hard-copy, will increase massively in line with the increase in oil and gas exploration and production activities

• Increasing demands for the application of an open system of technology & data platform according to the specific needs of users

• Increased application of data management industry standards and breakthrough technique such as Machine Learning

• Data quality is an absolute necessity for every data management user to support valid & timely decision making

The implementation of good and sustainable data management and data quality management programs will help the performance of energy companies in the search for hydrocarbons and optimizing existing reservoir sources, for that we need joint efforts, support from the higher management & the collaboration between teams to realize the program data management and data quality management to perform optimally.

(By Edy Irnandi Sudjana, Energy Data/Well Log Analyst practitioner lives in Qatar)