In today’s world of big data and cloud-based solutions, increasing emphasis is placed on software applications enabling time and resource efficiencies when generating results. However, are we as an industry losing sight of the importance of input data quality in populating such models?
Data may derive from seismic or remote sensing (logs), along with primary rock (cuttings and core) and mud gases. Measurements from cuttings and mud gases are invaluable inputs to efficient cloud-based software applications as they are collected on almost every well, providing a framework of spatial and stratigraphic data.
© Geolog.
Assuming samples are analysed using a consistent approach, data points can be confidently compared: sample to sample, well to well.
In today’s exploration and production environment, surface logging companies are able to collect quantitative mud gas compositional (C1 – C8) and δ13C isotopic data (C1 – C3) in real time using standardised, consistent protocols, with measurements confidently comparable well to well and providing key information regarding hydrocarbons within the petroleum system. Similarly, cuttings data, obtained at wellsite or in the laboratory, using consistent, quantitative approaches, can be utilised to assess reservoir quality and connectivity. These datasets provide key calibration points for petrophysical interpretations.
Efficiencies gained using sophisticated cloud-based software applications can only be truly recognised if the primary input data is obtained through this consistent quantitative approach, allowing end-user confidence in the data. In a world demanding ever-increasing efficiencies, the importance of good quality primary input data is paramount and cannot be overlooked.
