Abstract

Oil and gas industry is data driven industry with more than petabyte of seismic data, well data, reservoir and production data growing in size continuously. In current scenario there is an urgent need to deploy new technologies and approaches to integrate and interpret all these data to drive faster and accurate decisions leading to finding of new resources, increased recovery rates and reduced environmental impacts in a cost effective manner. In this study, data analytic approach of reservoir characterization was attempted in Eastern deep water offshore basin of India (Fig. 1), which is one of the most promising deep water block with several discoveries. Reservoirs in this area are sands within Godavari clay of PlioPleistocene age. These reservoirs are the slope channel sands which occur as high amplitude anomaly bursts within low to moderate value pertaining to encasing clay in the background. Capturing these high amplitude anomalies as geobodies bring out the geometry of NW-SE trending slope channel sands and the data from the drilled wells targeting them confirm that these high amplitude bursts to be reservoir facies. Most of these sand bodies are charged with hydrocarbons. However, as observed in some wells these high amplitude events, though correspond to reservoir sands, are not always hydrocarbon bearing. These surprises are a major challenge during exploration and development of field. To understand and overcome this challenge, a data analytic approach of reservoir characterization study was carried out.