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Davis, Graham (Premier Oil) | Newbould, Rob (Premier Oil) | Lopez, Aldo (Premier Oil) | Hadibeik, Hamid (Halliburton) | Guevara, Zunerge (Halliburton) | Engelman, Bob (Halliburton) | Balliet, Ron (Halliburton) | Ramakrishna, Sandeep (Halliburton) | Imrie, Andrew (Halliburton)
The oil and gas potential of the basins surrounding the Falkland Islands has attracted exploration drilling that resulted in discovering the Sea Lion Field in the North Falkland Basin in May 2010. Recent exploration drilling has resulted in new oil discoveries to the south of the Sea Lion Complex that has not only confirmed the area as a significant hydrocarbon province but has also enhanced the likelihood of future commercial development of resources. Primary oil targets are stacked and amalgamated deepwater lacustrine turbidite fans comprising multiple lobes. In exploration and appraisal wells, porosity characterization, permeability assessment, pressure measurements, and hydrocarbon fluid identification are essential input data for robust reservoir characterizations and resource estimations.
A comprehensive suite of advanced logging measurements, in addition to conventional log measurements, have been used to facilitate data analysis and calibration to laboratory core measurements. The pressure gradients and fluid samples obtained from formation testing when combined with the wireline log measurements are fundamental when determining the thickness, quality, and connectivity of hydrocarbon zones, which, in turn, impact the commercial evaluation of the well. In these remote offshore basins where rig costs are high and the ability to focus data acquisition in specific zones of interest and minimize logging time whilst identifying and reacting early in real time to data points that lie off the expected trends can add significant value to the operating company.
Formation evaluation challenges include hydrocarbon identification and resolving fluid contact uncertainties. In addition, establishing whether there are any baffles or barriers in the system or significantly varying reservoir properties as a consequence of facies changes has the potential to complicate the evaluation in respect to permeability characterization and volume estimation.
A method of facies classification using a combination of resistivity-based borehole imaging data and nuclear magnetic resonance (NMR) data is outlined in this paper. This method, when combined with conventional log data, has exhibited encouraging results in terms of identifying lithofacies and determining a rock quality index (RQI). The mud logs and gamma ray logs were interpreted with the borehole image logs in these turbidite reservoirs, which resulted in identifying four distinct depositional lithofacies. These lithofacies were integrated with the free fluid index (FFI) to bulk volume irreducible (BVI) ratio determined from the NMR data. The FFI to BVI ratio was used as an index for RQI classification, which was then subsequently used to optimize formation pressure testing and sampling points.
The contribution and importance of lithofacies identification is typically ignored when optimizing formation pressure depths and interpreting the results. The methodology presented in this paper uses an integrated workflow jointly developed by the operator and service company that allows detailed reservoir evaluation in the zones of interest and real-time adjustments to optimize the data acquisition programme that potentially enables rig-time savings and, consequently, reduces overall formation evaluation costs.