Evaluation of seismic and petrophysical parameters for hydrocarbon prospecting of G-field, Niger Delta, Nigeria
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Adequate analyses of seismic and petrophysical data help to minimize drilling risk and maximize well and reservoir productivity. Reservoir characterization was carried out to provide information and improve understanding of the geological and petrophysical parameters, and hence improve decision making regarding the development of the field under study. Wireline logs obtained from three wells as well as a 3D Seismic data coverage of G-field in the Niger Delta were evaluated using the petrel software. Suites of gamma and deep resistivity logs aided the delineation and correlation of the sandstone unit, while the top was tied to the seismic data using synthetic seismogram to determine seismic characters. Well correlation enabled the delineation of reservoir sand across the wells. The quality of the reservoir was determined from petrophysical averages, in which the reservoir has an average thickness of 72 m, average porosity of 0.31, average net to gross of 0.75, average V-shale of 0.25, and average water saturation of 0.19, respectively. Listric normal faults were mapped across the field. The models reveal lateral and horizontal variations in reservoir properties which capture subsurface heterogeneity and anisotropy across the reservoir sand, and also possible sweet-spot zones were identified. These are diagnostic of areas for future exploitation and recovery of hydrocarbon. Seismic attributes analysis was done to predict variation in lithofacies across the sandstone body.
KeywordsReservoir Seismic Logs Porosity Faults
Many dry holes have been drilled in the Niger Delta as a result of inaccurate analysis of the integrity of the numerous fault-dependent closures and stratigraphic setting associated with the basin. The Niger Delta like many deltaic areas is extremely difficult to define due to the heterogeneous nature of the various sedimentary lithofacies units associated with it. This complex physical property of the basin has made it extremely difficult to define formations and their interfaces. And so integration of 3D seismic and well data for structural interpretation and reservoir characterization is a continuous process of providing an improved understanding of the geological and petrophysical controls of fluid flows in the reservoir. It encompasses all methods and techniques that can lead to a well-improved understanding and a much better handling of the reservoir. Reservoir characterization is defined as a systematic means of quantitatively determining and assigning reservoir properties, establishing geologic information and uncertainty in spatial variability (Lake and Carroll 1986). Subsurface configurations must be well understood in order to be able to efficiently delineate the structures that are favorable for the accumulation of hydrocarbon; and several geologic parameters are important accumulation, gas and oil in large quantities, to form a pool sufficient enough for production. These parameters include good source rock (an organic-rich) to produce the oil or gas, a reservoir rock with sufficient porosity to accommodate the hydrocarbon, and good structural framework to prevent the oil and gas from leaking away (Coffen 1984). The importance of data integration is usually in improvement in the accuracy of mapping complicated structural plays (Adejobi and Olayinka 1997). When 3D seismic data are interpreted and made clear with modern computer workstations and software for interpretation, structural mapping can be done swiftly and accurately. However, it is not enough just to map the top of the reservoir (as was the case in two dimensions). To understand how structures were formed and when, it is usually necessary to map a range of marker horizons above and below the target. This study therefore was aimed at integrating well and seismic data obtained from the field under study, to effectively characterize subsurface sandstone reservoir and evaluate its hydrocarbon potentials.
Location and geology of the study area
Materials and methods
Seismic and well log data analyses
Correlation of hydrocarbon-bearing sand bodies
The hydrocarbon-bearing sand bodies were delineated and correlated over the wells making use of the composite well log suite of neutron-density, gamma ray and resistivity logs. The log shape (motif) of the sealing shales above the pay sandstone was used as means for litho-correlation of the sand bodies across the wells along a specific direction in the field. Also by means of flattening the wells at a specific depth, the identification of pay sand levels was less demanding, and furthermore, there were conceivable signs of the structural geometry within the field before shale tectonics and faulting above the sandstone level.
Petrophysical properties estimation
Petrophysical properties were predicted and approximated for the different identified hydrocarbon-wielding sand bodies of interest from the generated petrophysical logs. The rock parameters estimated include: total porosity, thickness, water saturation, effective porosity hydrocarbon saturation, volume of shale and net/gross.
Well to seismic tie
Seismic reflections take place at lithologic interfaces; therefore, it is important to identify the events (i.e., peak or trough) that represent the tops of the different sand bodies interpreted from the well log data. This process is known as well to seismic tie and is usually carried out using an artificial or synthetic seismogram developed from the sonic and the density logs in the well. After the identification of events, the reservoir tops were posted on the seismic sections with the use of time–depth relationship data (i.e., checkshot data).
Fault interpretation and horizon mapping
The structural framework of a field is vital to comprehend the trapping mechanism and the relationship among faults and the fundamental anticlinal structures. Faults were interpreted or delineated from the inline sections moving at a step of 10. The criteria for the interpretation incorporate reflection discontinuities or sudden horizon termination and lateral change in seismic amplitude. The already identified event that corresponds to the top of hydrocarbon-bearing reservoir of concern was mapped across both inlines and crosslines as horizons moving likewise at a step of 10. The fault framework that was built from fault interpretation gave rise to the horizon mapping and simple identification of downthrown and upthrown displacement of the horizon.
Generation of time and depth structure maps
The horizon mapping facilitated the generation of a time map (i.e., map depicting lines of equal time). The related depth map was created from a velocity model calibrated by the checkshot data. The appearance of faults on 2D known as fault polygons were superimposed on the depth map and time map to show their lateral extent and the direction of dip and also in identification of the structural mechanism that traps hydrocarbon at the pinpointed reservoir level (i.e., fault-dependent, fault-assisted structures, etc.).
Results and discussion
Estimated averages of petrophysical parameter from wells
Average porosity (Φ)
Average water saturation (Sw)
Average volume of shale (Vsh)
Average net to gross
Seismic attributes analysis
Through seismic analysis and well log correlation across the G-field, a hydrocarbon-bearing sandstone reservoir was delineated. The well log suites of gamma and deep resistivity logs aided the delineation and correlation of the sandstone reservoir, while the top was tied to the seismic using synthetic seismogram to determine seismic characters. The quality of the reservoir was determined from petrophysical averages (porosity, water saturation, volume of shale and net to gross). Four listric normal faults were mapped across the field, and the structural trapping mechanism favouring hydrocarbon accumulation in the reservoir is an associated fault-assisted anticlinal structure. Additional hydrocarbon potentials were delineated across the mapped sandstone unit. The extracted amplitude attributes across the reservoir conform more to the lithofacies variation than to the structure (DHI).
The authors appreciate the funding support of Covenant University and off course, to Shell Petroleum Development Company for releasing the data with which the research was carried out.
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