The stratigraphic evolution of the Niger Delta is dominated by a continuous stepwise progradation occurring during a major tertiary regressive phase that continues to the present. The successive phases of the delta growth form transient depobelts that are bounded by mega-structural growth faults.

Any seismic trace can be decomposed into a 2D function of amplitude versus time and phase. We call this process phase decomposition, and the amplitude variation with time for a specific seismic phase is referred to as a phase component. For seismically thin layers, phase components are particularly useful in simplifying seismic interpretation. Subtle lateral impedance variations occurring within thin layers can be greatly amplified in their seismic expression when specific phase components are isolated. For example, the phase component corresponding to the phase of the seismic wavelet could indicate isolated interfaces or any other time symmetrical variation of reflection coefficients. Assuming a zero-phase wavelet, flat spots and unresolved water contacts may show directly on the zero-phase component. Similarly, thin beds and impedance ramps will show up on components that are 90°o ut of phase with the wavelet. In the case of bright spots caused by hydrocarbons in thin reservoirs because these occur when the reservoir is of an anomalously low impedance, it is safe to assume that the brightening caused by hydrocarbons occurs on the component −90°out of phase with the wavelet. Amplitudes of other phase components associated with bright reflection events, resulting perhaps from differing impedances above and below the reservoir, thus obscure the hydrocarbon signal. Assuming a zero-phase wavelet, bright-spot interpretation is thus greatly simplified on the −90°phase component. Amplitude maps for the Teal South Field reveal that the lateral distribution of amplitudes is greatly different for the original seismic data and the −90°phase component, exhibiting very different prospectivity and apparent areal distribution of reservoirs. As the impedance changes laterally, the interference pattern for composite seismic events also changes. Thus, waveform peaks, troughs, and zero crossings, may not be reliable indicators of formation top locations. As the waveform phase changes laterally due to lateral rock properties variations, the position of a formation top on the waveform also changes. By picking horizons on distinct phase components, this ambiguity is reduced, and more consistent horizon picking is enabled.

Pressure prediction is challenging in the Niger Delta, as evidenced by the number of kicks taken i.e. a sign of under-balanced drilling. Incorrect pore pressure prediction can lead to wrong choice of well design and hazards during drilling operations. From a current study of 42 wells from the Shallow Offshore Shelf of the Niger Delta we present the techniques used to interpret pressures in the Koronama-3 well. The techniques presented included using velocity vs. density plots to determine overpressure mechanisms, using high quality seismic velocity data to interpret shale pressures and a model based on rates of sedimentation to establish theoretical limits with which to constrain the seismic-based interpretation. What emerges is a multi-strand approach to pressure prediction, using independent data types to provide a more realistic pressure model.

Time-lapse seismic data from Bonga Field, located in deepwater Nigeria, have delivered excellent results previously from dedicated streamer seismic surveys (2000, 2008, and 2012) that image changes in amplitude due to water replacing oil. One challenge with the streamer data is the presence of a floating production storage and offloading (FPSO) unit. It is difficult to accurately repeat streamer acquisition geometry in economically important updip regions of reservoirs that lie beneath the FPSO. To ensure an accurate repeat of this area, we acquired an ocean-bottom node (OBN) survey in 2010 and carried out the first time-lapse OBN repeat of the survey in 2018. Time-lapse processing of the OBN data produced excellent results. We obtained an OBN-on-OBN normalized root mean square (NRMS) difference repeatability of 6%. This was an improvement over the 12% NRMS for streamer-on-streamer repeats. The OBN data quality was unaffected by the FPSO, enabling us to properly image the changes occurring in this area. The 4D OBN interpretation was used to identify bypassed oil opportunities. The data also were used to derisk well placement, optimize water injection, and update the dynamic reservoir models. The new models are essential to enhance predictability and field performance. We also analyzed an area northwest of the field where we extended the 2018 OBN acquisition and compared it to streamer data to optimize a new injection well location.