1. Introduction

Oil and gas exploration and production industry aims to make huge profits for a lesser cost of exploration and production that justifies why several methods are being used to cut cost and still make profit. To reduce the amount of dry holes drill and excess well counts in a field, one of those methods is used while still adequately understanding the field and the optimiziproduction.

A total of about twenty-three oil fields in Nigeria, have been either shut in or abandoned. This has made exploration companies acquire huge and extra costs because of missed targets caused by errors in the reservoir characterization results. Quantitative seismic interpretation has been helpful in solving some of the cost excess challenges over the years.

Many scholars have also worked on the reservoir characterization on a lot of fields in the Niger Delta area, in Nigeria (Okoli et al. 2018; Okeugo et al. 2018; Ohaegbuchu et al. 2016, Uko et al. 2014; Ekwe et al. 2012; Omudu et al. 2008). Notwithstanding, just a few have worked on the quantitative analysis aspect, which is an essential research gap to be filled, and also it is helpful to improve the reserve portfolio. The estimation of elastic attributes and reserves in an oil field, sequel to these; the analyzation of the reservoir properties using a quantitative process is of extreme importance. Hence, this study was aimed at characterizing three (3) reservoirs in K-Creek field, while using both qualitative and quantitative approaches for the oil recovery in K- Creek field in Niger-Delta, Nigeria.

A qualitative and quantitative approach gives in details a link between petrophysics, geology and geophysics of the rock. This knowledge of quantitative approach is required for analyzing the rock properties: a link between the properties of the rock to the fluid properties of the reservoir (Avseth et al. 2005). The help of quantitative analysis makes certain reservoir parameters best understood. The need for a quantitative characterization of a reservoir is indeed essential to fill the gap in potentially erroneous FDP studies.

2. Research Method

The data obtained and used in this study included suite of well logs, in ASCII format; gamma ray log, density log, neutron log, resistivity log, sonic log, caliper log, well deviation surveys, check-shot survey, and 3-D seismic data from the field of study, K-Creek oil field in Niger Delta.

Below is the workflow chart of the project.

Figure 1. Research Workflow

3. Results and Analysis

Well log interpretation was carried out in order to delineate reservoirs and also to transform the wireline log data into the reservoir properties such as the volume of shale, porosity, permeability, water and net-to-gross ratio. The best rock properties were used to discriminate fluid and lithology. The best attribute was used to invert the seismic data. Finally, structural analysis was carried out sequel to the petrophysical analysis. All these were done to characterize the reservoir and analyze the potentiality and economic viability of the hydrocarbon reservoir prospect.

3.1. Reservoirs Identification and Well correlation

A total of twelve prospective hydrocarbon-bearing zones (Sand A to L) were identified from Wells A to G (W-A to W-G) using the conventional log analysis. Three (3) of these reservoirs namely Sand A, Sand J and Sand L were selected for further studies. The tops and bases of the three identified reservoirs are shown in Table 1. The wells display a shale/sand/shale sequence which is very typical of the Niger Delta formation.

The well correlation provides a general knowledge of the stratigraphy of the study field as seen in figure 2. The analysis done shows that each sand unit extends throughout the field also varies in thickness with some of the units occurring at greater depth than the adjacent ones which is the possible evidence of faulting. The observation of the shale layers shows that they increase with depth along with a decrease in the depth of the sand layers. This is a pattern in the Niger delta, indicating a transition from the Benin to Agbada Formation. Considering the analysis and the resistivity logs in particular, all of the three reservoirs delineated, were identified as hydrocarbon bearing reservoir across the four wells (i.e. well A – D).

Figure 2. Well section window showing the correlation panel for all the sand bodies identified on well log

Petrophysical Evaluation for Three Reservoir Intervals (Sand A, Sand J and Sand L)

The total summary of the estimated results obtained are presented below in table 1.

SAND A

Table 1 shows the summary of the petrophysical evaluation for SAND A reservoir correlated across WELL A to WELL G. The shale volume was calculated by using the gamma ray index formula. The values ranged from 7.74% to 18.57% indicating that the fraction of shale in the reservoirs was quite low. Also, it shows that there was a large volume of sand than shale deposit in the reservoir which certifies that it is hydrocarbon saturated. The total porosity of the reservoir was calculated using the porosity formula from the density log (RHOB), the values gotten ranged from 0.26 to 0.30 which was an indication of having a very good reservoir quality and also reflecting possibly a reservoir with well sorted coarse-grained sandstones. On permeability, the reservoir units ranged from 834 md to 2110 md, implying that the permeability of the reservoir was very good to excellent. The hydrocarbon saturation of the reservoirs ranged from 52.74 to 71.00, indicating that the void spaces occupied by water was relatively low; high hydrocarbon saturation, gives high hydrocarbon production. Net-to-gross ranged from 0.82 to 0.92, implying that the reservoir contained more sands than other rocks types. These results further show that the reservoir is very porous and permeable. Also, the high level of hydrocarbons which it contains shows that is has a very viable production life.

SAND J

Table 1 shows the summary of the petrophysical evaluation for SAND J reservoir correlated across WELL A to WELL G. The shale volume was calculated by using the gamma ray index formula. The values ranged from 17.61% to 39.09%. Low volume of the shale indicates that the fraction of shale in the reservoirs is quite low which further shows that there is a large volume of sand than shale deposit in the reservoir. It also certifies that it is hydrocarbon saturated. Porosity values ranged from 21.34% to 30.49% indicating a very good reservoir quality. The permeability of the reservoir units’ range was from 375.21 to 2108.74 mD. This implies that the permeability varies from very good to excellent. The water saturation of the reservoirs ranged from 18.01 to 81.99 indicating that the proportion of void spaces occupied by water is low which in turn leads to high hydrocarbon saturation and high hydrocarbon production. High values of water saturation were observed in Well B, E and F. This implies that the reservoirs contain low hydrocarbon saturation and low hydrocarbon production. The net-to-gross ranges from 0.61 to 0.82, implies that the reservoir contains more sands than other rocks types. These results further confirm that the reservoir is very porous and permeable. Also, the high level of hydrocarbons it contains shows that is has had a very viable production life.

SAND L

Table 1 shows the summary of the petrophysical evaluation for SAND L reservoir correlated across WELL A to WELL G. The shale volume was calculated by using the gamma ray index formula. The values ranged from 11.28% to 37.44% indicating that the fraction of shale in the reservoirs is quite low. They further show that there is a large volume of sand than shale deposit in the reservoir which certifies that it is hydrocarbon- saturated. Porosity values ranges from 20.13% to 36.86% indicating a very good reservoir quality. The permeability of the reservoir units’ range was from 934.80 to 3327.39 mD. It also shows that the permeability varies from very good to excellent. The reservoirs hydrocarbon saturation ranges from 45.90% to 87.09% indicating that the void spaces occupied by water are relatively low. High hydrocarbon saturation gives in turn to high hydrocarbon production. The net-to-gross ranges from 0.63 to 0.89, implying that the reservoir contains more sand than other rock types. These results further show that the reservoir is very porous and permeable. Also, the high level of hydrocarbons it contains shows that is has had a very viable production life.

3.2. Fault and Horizon Interpretation

Seismic volume on the seismic time slice reveals the presence of a patterned reflection discontinuity which is identified and interpreted as a fault. As shown in Figure 3 seismic inline 8260 shows the interpreted faults that are present. Apart from the major faults seen, there are other minor faults formed by the post depositional processes. These faults are often referred to as synthetic. The faults can also be regarded as antithetic because they were formed on the hanging wall, down-throw axis. The presence of these faults in the study area; K-Creek field, is an indication that there is hydrocarbon accumulation possibility, as faults are known to be good migration pathways for the hydrocarbon into reservoir rocks. The normal faults also make a horst and graben which is very good for the accumulation of oil.

Figure 3. Seismic inline 8260 showing mapped horizons, interpreted synthetic and antithetic faults.

Horizon Mapping/Interpretation

Horizon/events were mapped, interpreted and then correlated all through the study area. Horizon picks were carried out iteratively in the in-line and cross-line directions, and then corrected for mis-ties. In areas where reflection quality and characteristics were of good quality, the lines were picked at larger intervals, while at areas where reflection quality was relatively poor and characterized by discontinuities and chaotic, the lines were picked at a closer interval so as to reduce the mis-ties to a minimum. Three major Horizons were mapped; Sand A, Sand L and Sand J respectively, as seen in Figure 3. Seed grids were generated across mapped or picked faults and horizon line. The mapping process began by mapping two inlines that were widely spaced apart and subsequently two crosslines with similar spacing. After the horizon mapping process was completed, then a seeded grid was generated. The seeded grid was mainly utilized for the surface generation.

3.3. Time Structure Map

Mapped horizons and the generated fault polygons were used to generate time structural maps for the three reservoirs. Time Structure maps were generated to aid in the data interpretation. The corresponding time values of the horizons on all the cross-lines were picked using the in-lines to generate the time map. The time structure maps of the three horizons generated are shown in figures 4-6. The contoured map has contour interval of 10 ms and the values range from 1750 (orange/yellow) to 2040 ms (purple/blue). Points of equal time are identified by the same color.

In figure 4 areas with very high times are seen to the southern part of the field. However, in the case of the deep reservoir in figure 6, regions of extremely high times appear in the southern and central parts of the field. Some growth faults that are seen on the seismic section are also displayed on the surfaces in both cases. The time map is compressed in its deeper parts and stretched out in its shallow areas because of the general increase in velocity with depth.

Figure 4. Reservoir time top structure maps for reservoir sand A.

Figure 5. Reservoir time top structure maps for reservoir sand J.

Figure 6. Reservoir time top structure maps for reservoir sand L.

3.4. Depth Structure Map

The time structure maps were converted to depth structure maps using the velocity model. The contouring was done by joining points of equal depth going around the data with contour interval of 50ft for each surface. Points of equal depth are identified by having the same colour and the depth of each colour is shown in the colour bar. Depth structure map of horizon Sand A, L & J are shown in Figure 7, 8 & 9 respectively. The contoured map has values ranging from 3400ft to 6800. Structural highs are observed at South Western and South Eastern parts of the field. These areas form a good trapping system thereby increasing the retentive capacity for the hydrocarbon. The hydrocarbon trapping system in the central part of the field where the wells are located is a faulted rollover anticline. The low faults throw in the area is responsible for the excellent retentive capacity of the hydrocarbons. Structural lows are seen in the North east and North western region and the area does not have good hydrocarbon deposits.

Figure 7. Reservoir Depth top structure map for reservoir sand A.

Figure 8. Reservoir Depth top structure map for reservoir sand J

Figure 9. Reservoir Depth top structure map for reservoir sand L

3.6. Fluid Substitution Modelling

Using 50% brine, 30% gas and 20% oil model, we obtained new V_p, V_s and density logs (V_p, V_s_frm and ρ_frm as seen in figure 10).

Figure 10. Results of fluid substitution for brine showing modified logs in red.

3.7. Well Log Attribute-Crossplot Analysis

The cross plot of V_p/V_s ratio against Acoustic impedance (Z_p) (figure 11), distinguishes the Sand A and L reservoirs into two zones namely Sand zone (red ellipse) and shale zone (green ellipse). Sand J has three clusters which are sand, shaly sand and shale zones. This crossplot shows better lithology discrimination along the acoustic impedance axis, proving that acoustic impedance attribute will better describe the reservoir conditions in terms of lithology than V_p/V_s ratio.

Figure 11. Crossplot of Vp/Vs versus Acoustic Impedance for Sand A to L reservoirs

Crossplots of Lame psrameters; Mu-rho (μρ) against Lambda-rho (λρ) in figure 12, shows a separation into three zones that can be inferred to be possible zones of hydrocarbon (red), brine (black) and shale (green) which is next validated by the lowest density values. These crossplots were created with the modelled logs obtained from the fluid substitution modeling. The plots indicate that λρ is more robust than μρ in the discrimination of fluids in this field, and that λρ values are relatively low for the reservoir sand, therefore it is expected that lambda-rho differentiates fluid better in the inverted seismic section.

Figure 12. Crossplot of lambda-rho (λρ) vs. mu-rho (μρ) for Sand A to L reservoirs

4. Discussion and Conclusion

An authentic representation of a reservoir cannot be over emphasized during the exploration in the oil industry. This helps in avoiding unforeseen future hazards. The Well logs and Seismic data were appropriately used to give a qualitative and quantitative representation of the reservoir in K-Creek oil Field. It is a clastic environment which is sedimentary in nature and characterized as Sand and Shale, as accustomed with the Niger delta area. These were next validated by using the well log data in this study.

The qualitative and quantitative reservoir characterization of K-Creek oil field revealed a few hydrocarbon bearing sands that varied in thicknesses. The three reservoirs that were picked for the purpose of this work were all associated with different petrophysical properties of Vsh, K, Sw, Φ.

Petrophysical parameters such as porosity, permeability, hydrocarbon saturation, water saturation, were identified using a combination of resistivity, neutron, density logs and mathematical models.

The findings of the study indicated that High density and Neutron correlated with a shaly environment.

From the reservoir characterization using qualitative method, it can be inferred that all wells have hydrocarbon content, based on the dhi’s, but the quantitative approach gave more insight for FDP on the areas that can be exploited for the economic reasons.

The Rock physics crossplots showed better lithology discrimination along the acoustic impedance axis which is an indication that acoustic impedance attribute better describes the reservoir conditions in terms of lithology than Vp/Vs ratio. From Fluid substitution modelling carried out, new Velocities; Vp, Vs and densities ρ, were obtained. Lamé parameters (LMR) were used for the rock physics crossplots, and this gave good lithology and fluid separation in the crossplots analysis performed. It also properly delineated hydrocarbon zones from the brine zones.

The structural analyses showed that the faults were dipping in the south west direction, and cutting across the three delineated reservoirs. The hydrocarbon trapping mechanism in these reservoir was structurally controlled and the faults were synthetic.

Prior to this information wells were usually drilled on the basis of qualitative reservoir characterization, fault interpretation and DHI alone. In fact, no quantitative or seal tests were done which caused wasted funds due to drilling dry holes and the probable erroneous forecast.

From the reservoir characterization using both the quantitative and qualitative analyses, it can be concluded that all wells have hydrocarbon content and the field can be a possible economic pay zone. The hydrocarbon reservoirs in K-Creek field are economically viable for the hydrocarbon production as exemplified from the results of the porosity and permeability values and the overall analyses carried out in this work.

Proper qualitative and quantitative reservoir characterization should be carried out on fields before any FDP be conducted. This in turn would save more funds, increase the production rate and provide more accurate forecasts.

Recommendations

Rock physics analysis can be carried out for more research purposes and more detailed insight of the field in question. Observing the fault throw closely, there might be a possible gap in the sealing fault. A seal integrity analysis and Integration of 3D modelling of the basin and faults in the field can be carried out to have a better insight of the occurrence in the K-Creek Field.

Further investigation can be carried out on other wells. In addition, seismic inversion can be done for more detailed information as well.

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