Despite the current 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?
Although policy makers can mitigate the downturn by reducing taxes and making more acreage available, thus making it cheaper for companies to drill more, potentially dry, wells, we argue it is better to encourage the industry to drill less wells (at least partly for environmental reasons), but to drill them based on better predictions that make them more likely to be successful. 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 in order to dramatically improve exploration success. 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.
The Earth Science Analytics AS mission is to improve exploration success in challenging geological settings by developing Geoscience-driven Machine Learning workflows and software capable of delivering high quality data-driven predictions based on complex relationships within large, multidimensional, data sets. A large fraction of the yet-to-be-found hydrocarbons likely reside in parts of sedimentary basins where geophysical techniques such as AVO, seismic inversion and CSEM are less reliable, either due to i) deep burial, ii) thin reservoirs (or HC columns), and/or similar acoustic and electric properties of the hydrocarbon accumulations and their surrounding rocks Traditional approaches to exploration in areas where so-called DHI’s are absent/not expected involve; i) interpretation of stratigraphic surfaces and geological structures from seismic, ii) assessment of lithology and rock properties from neighboring wells, iii) identification of geological (structural, stratigraphic, sedimentological, petrophysical) features and trends, and iv) erection of conceptual models. Statistical analysis based on these data, and conceptual models, are used in order to make probabilistic predictions about the; i) presence and quality of reservoir rocks, ii) presence and quality of sealing rocks, iii) presence and quality of source rocks, iv) presence and size of hydrocarbon traps, and v) presence of migration routes from HC kitchen to trap. Numerous, imperfectly quantified, and often inter-related geological features are used as inputs to the probabilistic predictions of properties of the hydrocarbon prospects. When evaluating prospects, geoscientists (or humans in general) are not able to fully understand the multi-dimensional relationships of all the features that are relevant for the predictions they are making. As a result, prospect evaluation is based on predictions that do not fully exploit all the available data, and the multi-dimensional relationships between all observed features.
Recent developments in Machine Learning (including, but not limited to deep neural networks) enable accurate and efficient predictions in complex multi-dimensional systems. Earth Science Analytics AS is developing software and workflows that leverage Machine Learning in order to extract knowledge from, and make predictions from large subsurface data sets. We believe that our data-driven approach will enable geoscientists and exploration strategy makers to make more precise predictions, more efficiently, while using all available data.