From Data to Information
STORY INLINE POST
More than 30 years ago, a geophysical operations manager proposed a plan to generate an organized, homologated, and standardized database for all the information from the exploration team, which implied digitizing well logs, acquiring photographs of cores and thin sections, standardization of maps, interpreted sections and the inventory of seismic information, among other activities. However, the project was not globally authorized and the only part that was achieved was to create a seismic information database. This led us to the task of organizing and discretizing analog seismic information and placing it in a national repository, with a couple of security backups, in different locations.
At that time, the Society of Exploration Geophysicists (SEG) had already launched seismic data standards for digital recording SEG A, SEG B and SEG C, followed shortly by SEG Y and SEG D. The standardization showed us that we can count on ordered and classified databases. However, 30 years later and seeing the advances in information technology (IT), we understand that stored data becomes expensive to maintain, especially when it is in various repositories, with different formats and organization. This implies enormous wear and tear on the organization in the search, integration, and interpretation of data to generate information that is useful for decision-making. Investing in sometimes more than 50% of the time only in the compilation of the data, leaves little margin for the analysis and interpretation of the data and the subsequent generation of information during the project lifetime.
Climate change has pushed us to make crucial decisions regarding the search for alternative energy sources to avoid the emission of greenhouse gases, as well as optimization in the use and a plan to avoid fossil energies as much as possible. Hence the importance of having data organized in such a way that it allows us easy access to obtain a global panorama and a more efficient way of exploring and exploiting all kinds of subsurface elements that allow us to generate energy, apart from of all the technological development that this implies, especially in the oil and gas industry, which is trying to exploit these resources as much as possible before it becomes less necessary.
One of the methods that has emerged as an alternative for handling all data in an intelligent way and that allows us to use the increasingly common algorithms of machine learning, artificial intelligence and the creation of collaborative environments or data ecosystems is to achieve a description of the data itself, which consists of analyzing and describing the information that can be extracted from that data or data set (metadata) and that it be in a standardized and homogeneous form before placing it in the repository, in such a way that if at some point we need to integrate information acquired over several time periods, with similar characteristics and, having acquired it under different circumstances, we can count on having it in real time, avoiding investing most of the time in its compilation, allowing statistical and probabilistic analysis of trends and to estimate the possible risk in specific circumstances.
On this occasion, I would like to mention the efforts made by multiple companies in the energy industry, but particularly in the oil and gas industry, in standardizing an easily accessible organization of all the data generated in exploration and exploitation. Among subsurface energy resources, called Open Subsurface Data Universe (OSDU), which I particularly consider a great idea since all our data will speak the same language and communicate with each other, creating an environment of interrelation that in the near future will achieve a greater efficiency in decision-making, reducing analysis times and achieving synergy between different teams handling the same information and avoiding costly repetitions by having the same data repository capable of being understood by all the computer platforms we use for handling and interpretation of such data. This will allow us more time to perform the analysis of the information generated and to test different scenarios in less time, thereby facilitating the effective quantification of risk and the actions for its mitigation.
If we carry out an analysis of the time used to compile the data necessary to perform an interpretation and do this iteratively during each cycle of generating exploratory or development prospects, drilling wells, reservoir characterization, and the updating of regional models, we will realize significant savings in time, effort, and resources. Working definitively and forcefully in the real organization of our data that we have acquired over too long periods of time and keeping it updated will allow us to generate valuable information in the shortest possible time. Then, relying on an organized structure of data that we can integrate in the least possible time to generate accurate information (models) will be the best way to transit to a balanced energy matrix.