Make more profitable decisions by using all relevant data

Integrate data-management, -analytics and Machine Learning technology with the petroleum geoscience workflow

Despite the recent downturn in the E&P sector, it is clear that large quantities of hydrocarbons remain yet to be found, and that petroleum as an energy resource will be needed in many years to come. Disappointing exploration results, worldwide as well as on the NCS, illustrate how challenging it is to find commercial accumulations of hydrocarbons.

Dry wells negatively impact the ‘bottom lines’ of energy companies struggling to cope with low prices, as well as the economy of governments dependent on tax revenue from the industry. How can we improve the success rate for the benefit of industry and society?

Petroleum geoscience is hard

particularly when it comes to predicting key physical properties away from known measurements

It is hard because it is so complex. It is hard because there are no simple rules, like Newton’s laws of motion, that can help us predict the spatial distribution of, for instance, reservoir properties, or where to look for the next big commercial discovery. It is basically hard to “codify” and “formalize” what we do routinely as petroleum geoscientists.

Until today we have attacked these kinds of “hard-to-codify” problems by assigning teams of human experts to solve them. The combined experience of these experts, who typically come from multiple disciplines, helps us extract knowledge and insights from the available data. What if we could replicate this method with computers? Can we have the computer learn relationships directly from the data, from all relevant sources? This is exactly what machine learning, including deep learning is for.

Technological advances in geophysical data acquisition and processing (i.e. AVO, CSEM) have spawned several waves of exploration, and delivered discoveries in areas where these technologies provide sufficient reliability. Now, in the midst of the data-science renaissance, we argue that the time has come for radically new data-analytics methods to leverage the power of artificial intelligence and machine learning

The ever-increasing volume of subsurface data are exposing exploration geoscientists and managers to a formidable challenge; how to extract the right intelligence from the data, and how to use this to make better predictions? We argue that these large sub-surface data sets are under-utilized due to a lack of methods with which to handle such large volumes of data; this calls for entirely new methods of knowledge extraction and data-driven predictive analytics.

Unleashing the data repositories

National data repositories, such as CDA and Diskos, are great resources for training machine-learning models, when they are converted to structured data

The incredibly rich subsurface data and metadata available in national data repositories, such as CDA and Diskos, can be transformed into vast resources for training machine-learning models, by converting them to structured data repositories, that is. Machine-learning models work remarkably well when large, structured and labelled data sets are available for training. We can now start to use this technology to build incredibly detailed, high-dimensional models using all our relevant data. Even when machine-learning models are trained on smaller data sets they enable petroleum geoscientists to better understand the spatial distribution of reservoir properties and hydrocarbons. This technology is available; it is being used today and it is not solely a technology of the future, and because of this, workflow efficiency is being improved by orders of magnitude. Prediction accuracy is exceeding that of traditional “best practice”, today. Imagine what it will be like tomorrow when really large data sets are available for training models.

Data-science applications for exploration and production

Data-driven decisions

AI and Machine learning is being applied in exploration and production geoscience. We have developed applications for reservoir characterization, as well as for exploration at both regional scale and prospect level. These technologies and data science is exposing to geoscientists hidden relationships in measured data; we can reduce human biases and provide accuracy metrics for predictions and estimations. There is an enormous potential for value creation by applying data science technology on very large data sets. Value to society can be created by making more data sources openly accessible for big data analytics.

Earth Science Analytics mission is to empower geoscientists and decision makers with user friendly data-science technology that enable data-driven, and thus more reliable and profitable decision making in exploration and production.