For reservoirs that are not fully appraised, fluid type and resource volumes above Shallowest Known Oil (SKO) or Deepest Known Oil (DKO) are often unknown. This paper discusses the workflow adapted to predict the fluid type above the SKO with reasonable certainty. This enables to book SPE compliant resource volume (SCRV) above SKO to be proposed for booking and up dip wells to be justified. As part of SCRV estimation for Field-X to book hydrocarbon volumes above the SKO, the oil properties, reservoir pressure data and seismic information were integrated utilizing the limitations and uncertainties around each data for the candidate reservoirs. The fluid properties (specifically bubble point pressure) are the most essential element in this analysis, which sometimes are not available or not of sufficient quality. This article will discuss the methodology and workflow adopted to use the existing limited information with their corresponding uncertainties to circumvent these limitations. This methodology was demonstrated as a reliable technology supporting SCRV for Field X, resulting in the increase of ~ 25-30 % of the oil SCRV. Furthermore it has potential to be applied to other fields with similar circumstances.

History matching is an inverse modeling technique in which production-driven changes in reservoirs (pressure/saturation) are used to calibrate reservoir model properties and thus narrow down the associated uncertainty ranges, such that historical dynamic data is honored within acceptable tolerance, which results in subsurface models that are adequate for production forecasts & development planning. This paper summarizes an iterative history matching technique carried out to better understand early water breakthrough and observed water-cut trend in the W10 reservoir of Dotun field developed through a recently drilled producer-injector well-pair. Early water breakthrough & high water-cut seen in the producer well can be explained by the existence of a strong aquifer and a high permeable conduit between the producer and the aquifer which may not be resolved by seismic. This assumption is supported by interference tests showing weak pressure communication between producer/injector and chemical analysis indicating that the formation is the source of the produced water. A decision based modeling approach was adopted whereby facies updates were iteratively carried out in the sector between the producer and the aquifer to achieve incremental improvement in Water Breakthrough Time and Water-Cut matches. Dynamic parameters like possible Contact uncertainty, Relative Permeability and Skin were also varied. For accurate flood front modelling, local grid refinement techniques were implemented. A reasonable history match was achieved with a shallower contact, a facies realization replicating continuation of a high permeability streak between producer and aquifer, significant aquifer strength and dynamic local grid refinement. The model was history matched with four months of post-water-breakthrough production data and the robustness of the technique has been validated by actual well performance (post study) with simulation predictions. The exercise also suggests that the pressure support at current production rates is maintained mostly by the aquifer and that the injector could indeed be redundant.

This study highlights a technique adopted for predicting and mapping net-to-gross (NTG) away from well locations through a combination of rock physics and seismic inversion applied in the Baza Field. The Baza field is located offshore Nigeria, with reservoirs poorly to mildly consolidated that were originally deposited in a deep-water submarine canyon system. The field is a partially appraised green field with three well penetrations encountereing amalgamated channels and lobes within the canyon system of tuiditic origin. Of the three wells drilled to date, only one well penetrated the key reservoir of interest- the B4 sands. The paucity of well penetration posses a challenge for accurate reservoir property assessment, particularly net-to-gross that has direct impact on hydrocarbon volume computation and ultimately on field development. Net-to-gross was predicted from seismic data based on a linear relationship observed from log derived P-impedance-AI (density × compressional velocity logs) and S-impedance-SI (density × shear velocity). Both properties when integrated can descrimate between sands and shales, and therefore serves as a proxy for calculating NTG. The linear relationship was applied to AI and SI seismic volumes built from simultaneous inversion of three sub-stack seismic data – the near (0-18), median (12-24) and far (24-45). The seismically derived net-to gross computed from simultaneous inversion compares favorably with log derived net-to-gross at well locations. The net-to-gross model resulted in a robust static and dynamic model that ultimately formed the basis for selecting optimal locations for future development wells for the B4 reservoir.