3D Petrophysical Modelling Of Queen Field, Onshore Niger Delta, Nigeria

  • C Eze
  • G Emujakporue
  • DC Okujagu
Keywords: Queen Field, Onshore, Niger Delta, 3D Petrophysical.


Petrophysical-Modelling is indispensable in upstream Projects, considering the high cost, risks and uncertainties associated with this sector. Petrophysical qualities for Queen Field was modeled using Information obtained and analyzed from well-logs and 3-D Seismic data. Coarse-grain, Medium- grain and fine-grain Sands as well as Shale were all delineated by GR log. Results of petrophysical evaluation conducted on seven reservoir intervals correlated across the field showed that; Shale volume was below 35%, Total Porosity are > 20% Effective Porosity are >15% Permeability is > 380.00mD all of this conforms to excellent reservoir quantity. Seismic interpretation showed the presence of synthetic and antithetic faults. Two horizons were mapped on seismic data and utilized for modeling. These models were the framework for facies and petrophysical properties distribution. Facies models were generated using sequential indicator simulation while petrophysical properties were generated using sequential gaussian simulation algorithm. A comparison was further done between facies constrained and non-facies constrained models. It was found that for Porosity, Permeability, Water of Saturation and Shale Volume Models not constrained to facies all showed overestimated Models, in addition Stochastic STOIIP not constrained to facies gave an Over Estimated P50 value for Surface I and O Reservoir Interval as 624.028M, 76.28MM, when compared to Stochastic Hydrocarbon STOIIP when constrained to facies that showed Stochastic P50 value of 513,247 and 67.04MM for surface I and O and Deterministic STOIIP of 742.90M and 87.88MM. This study validates the practice of constraining Petrophysical model to facies available on the field as the best practice.

Keywords: Queen Field, Onshore, Niger Delta, 3D Petrophysical.


Journal Identifiers

eISSN: 2659-1502
print ISSN: 1119-8362