Simultaneous elastic impedance inversion is performed on the 2D North Viking Graben seismic data set used at the 1994 SEG workshop on amplitude variation with offset and inversion. P‐velocity (Vp), S‐velocity (Vs), density logs, and seismic data are input to the inversion. The inverted P‐impedance and S‐impedance sections are used to generate an approximate compressional‐to‐shear velocity ratio (Vp/Vs) section which, in turn, is used along with water‐filled porosity (Swv) derived from the logs from two wells, to generate fluid estimate sections. This is possible as the reservoir sands have fairly constant total porosity of approximately 28 ± 4%, so the hydrocarbon filled porosity is the total porosity minus the water‐filled porosity. To enhance the separation of lithologies and of fluid content, we map Vp/Vs into Swv using an empirical crossplot‐derived relation. This mapping expands the dynamic range of the low end of the Vp/Vs values. The different lithologies and fluids are generally well separated in the Vp/Vs–Swv domain. Potential hydrocarbon reservoirs (as calibrated by the well data) are identified throughout the seismic section and are consistent with the fluid content estimations obtained from alternative computations. The Vp/Vs–Swv plane still does not produce unique interpretation in many situations. However, the critical distinction, which is between hydrocarbon‐bearing sands and all other geologic/reservoir configurations, is defined. Swv ≤ 0.17 and Vp/Vs ≤ 1.8 are the criteria that delineate potential reservoirs in this area, with decreasing Swv indicating a higher gas/oil ratio, and decreasing Vp/Vs indicating a higher sand/shale ratio. As these criteria are locally calibrated, they appear to be valid locally; they should not be applied to other data sets, which may exhibit significantly different relationships. However, the overall procedure should be generally applicable.

With the maturity of the Niger Delta region, having been producing hydrocarbons for the past fifty years, exploration has to look deeper to find any economic accumulations. Some of the consequences of looking deeper include the onset of overpressures and velocity anisotropy. One of the measures to counter these challenges in Onshore Niger Delta is the introduction of anisotropic prestack depth migration for all newly acquired long offset 3D seismic data.The need to properly image the deep leads to acquisition of long offset data as in Okubotin 3D survey. The consequence is that Pre-Stack Depth Migration (PSDM) based on isotropic velocity model assumptions, though effective in imaging conventional reservoirs at shallower levels, is no longer adequate. The introduction of the long offset acquisition brings about a new challenge to derive an accurate velocity model to correct for the socalled 'hockey stick' effect at long offsets. Resolution of the velocity challenge helps in the better imaging of steep dips and corrects for non-hyperbolic moveout on Common Mid Point (CMP) gathers to give better image for the deep exploration plays in the field. This paper presents a workflow for the use of anisotropic velocity model building in Okubotin area. It discusses the possible causes of anisotropy and the impact of anisotropic Pre-Stack Depth Migration processing of seismic data in Onshore Niger Delta. Results showed a much-improved imaging of the subsurface revealing new play potentials that were hitherto unknown in the area.The successful imaging of the deep as in Okubotin 3D has enabled the development of a standardized workflow for this process, successfully unlocked the deep play in the Onshore Niger Delta by revealing impressive leads, explained the influence of anisotropy in the region and SPDC is now able to gain full dollar value of the legacy acquired long-cable seismic data.

Acoustic and simultaneous elastic impedance inversions of a 2D land seismic data set are performed to characterize a carbonate reservoir of Mississippian age in the Turner Valley Formation, in the Rocky Mountain foothills of western Canada. The inversions produce P‐wave and S‐wave impedance sections (Ip and Is, respectively), from which Lamé parameter × density (λρ and μρ) sections are derived. The Ip data provide a separation between the clastics and carbonates. The μρ data provide an estimate of porosity distribution within the dolomitized limestone target. Deviations from baseline curves for water‐saturated carbonates, of λρ versus porosity, λ/μ versus porosity, and Is versus Ip, are interpreted as indicators of gas potential. These indicators all provide similar spatial patterns of areas of high gas potential and are consistent with the gas occurence observed in a well.

Two methods exist for the estimation of Gas Initially In-Place – volumetrics (probabilistic or deterministic) and performance (material balance equation or numerical simulation). The more appropriate method will depend on the maturity of the project reservoir. For potential accumulations with limited information, the GIIP estimate may be volumetric-based using probabilistic methods, covering the range of possible outcomes with P90, P50 and P10 outcomes as low, best and high estimates. For fields where more subsurface data are available, the preferred method will generally shift towards volumetric-based GIIP estimates using deterministic low, best and high cases. Once a field is in production and sufficient data are available, a performance-based GIIP estimate should be established, including the appropriate range of uncertainty. Over the years, the tendency has been to over-rely on volumetric-based estimates with little attention paid to performance based GIIP update leading to sub-optimal gas field development. In this paper, we took a case-study reservoir having or tending towards negative reserves in course of its production life to underscore the need for timely update of GIIP based on performance data as an aid to optimal development of a gas field. Reservoir A has volumetrically based GIIP of 1.6 Tscf respectively leading to prediction of early End of Life (EOL) or negative reserves. When compared with performance based GIIP and updated volumetric GIIP of 2.04 Tscf to match performance data, the issue of early EOL or negative reserves was ameliorated and ultimate recoveries were optimized. Performance based GIIP estimate is reliable when premised on appropriate choice of methods (P/Z material balance for depletion reservoirs, aquifer influx model in water drive gas reservoirs material balance model or dynamic simulation) integrating all available data as illustrated in the case studies. To allow comparison between volumetric-based (static) and performance-based (dynamic) GIIP estimates and to understand the potential difference between the two, it is recommended that a current volumetric GIIP estimate should be maintained throughout the life of the field in addition to an up-to-date performance-based GIIP estimate to aid optimal gas field development.

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