Evaluation of Hydrocarbon Reserve in AD Field, Offshore Niger Delta

This study was carried out to quantify uncertainty in the reserve estimate of hydrocarbon in the reservoirs of AD Field, offshore, Niger Delta. Three Dimensional (3D) seismic data and log suites of seven wells (AD1 to AD7), gamma ray, resistivity, neutron and bulk density logs, well deviation and checkshot data in AD Field acquired from companies in the area. Twelve faults (Fault1 to Fault12) were identified from seismic structural interpretation while Six hydrocarbon-bearing sand intervals (Sand A F) were delineated from the petrophysical analysis. The sand intervals thin-out basin wards, suggesting a prograding sequence. The porosity of the sand intervals ranges between 0.19 and 0.32, implying good to excellent porosity. The water saturation values ranged from 0.19 to 0.39, indicates a prospective accumulation of hydrocarbon. Sand A reservoir had the largest accumulation of hydrocarbon in-place with hydrocarbon pore volume of 2343 106 Reserve Barrel (RB), Stock Tank Oil-Initially-In-Place (STOIIP) of 175 MMbbl and gas initially-in-place of 0.30 TCF. The coefficient of variation in the reserve estimates of the reservoirs ranged from 0.09 to 0.15 indicating very low uncertainty of substantial hydrocarbon reserve that could be exploited.


Introduction
The Niger Delta basin is located in the Gulf of Guinea and represents one of the major Delta systems in the world (Figure 1). This basin sediment covers an area of approximately 75,000 km 2 and progrades southwest from Eocene to the present, forming different depobelts [1]. The basin rank (12th) among the best prolific petroleum belts worldwide and 1st in Africa [2]. The Niger Delta accounts for 2% to 5% of the present day sedimentary basins on earth with hydrocarbon reserves above 34.5 billion barrels (STB) of oil and 93.8 trillion cubic feet (TCF) of recoverable gas [3]. Continuous discoveries of hydrocarbon resources in the Niger Delta oil province have encouraged exploration and production activities [4]. The evaluation of hydrocarbon resource involves the integration of petrophysical interpretations from well log data with the structural interpretations from seismic data. This combination study would help to quantify subsurface hydrocarbon resource, identify the hydrocarbon prospects, leads, resource ranking, quantify uncertainty as well as provide information for well development [5]. The integration of seismic and well log information play key roles in hydrocarbon reserve evaluation [5] [6]. The commercial life of hydrocarbon reservoirs often begins with exploration goes through development and ends with the exploitation of hydrocarbon. Reservoir characterization has evolved as a result of the integration of geology, geophysics, petrophysics, and geostatistics as a tool for providing better understanding of subsurface reservoirs and their homogeneities [7]. Several authors have contributed to advancement in techniques used in quantifying the influence of uncertainties in reservoir modelling. Garb 157 Open Journal of Geology [8] proposed the use of both deterministic and probabilistic techniques for hydrocarbon evaluation while Caldwell and Heather [9] considered analogy, volumetrics and performance analysis as the three main categories for reservoir evaluation. Geological concepts and reservoir characteristics used in the evaluation of hydrocarbon reserves are often full of uncertainty regarding geological structures, hydrocarbon seals, and hydrocarbon charge. The inability to measure hydrocarbon resource adequately could result into field development failure [10]. The use of practical methods for estimating the uncertainty associated with the geology of reservoirs without compromising accuracy is of utmost importance in reservoir evaluation and field development programs [11]. Three dimensional seismic interpretation and petrophysical analysis were integrated to give information on the reservoir characterization of the study area for economic viability and cost effectiveness.

Regional Geological Setting and Basin Evolution of the Niger Delta Basin
The Tertiary Niger Delta lies within the coordinates of latitudes, 3˚N and 6˚N and longitude 5˚E and 8˚E [3]. The geological map of the province is illustrated in Figure 1. The basin is a product of sediments supply from rivers in the present day Niger River, Benue River and Cross River with their several distributaries flowing into the Atlantic Ocean. Sediments deposited by the rivers and distributaries consist of unlithified sand and shale forming the basin fill [12].
The Niger Delta sediments prograde southwest from Eocene to Recent to form depobelts which are the most active portion of the delta at the stage of every growth [13]. The Niger Delta depobelts formed one of the world's biggest regressive deltas within a region of some 300,000 km 2 , a sediment volume of 500,000 cubic kilometres and thickness of over 10 km [14] [15] [16]. Niger Delta are predominantly roll-over anticlines in the Agbada Formation.
The prodeltaic shales in the eastern part of the Niger Delta serves as active source rocks generating hydrocarbon while the shales in the central and western parts have also contributed to hydrocarbon pooling [21].

Tectonic Setting of the Niger Delta Basin
The

Stratigraphy of the Niger Delta
The stratigraphy of the Niger Delta can be divided into three diachronous units of Eocene to Recent age that form a major regressive cycle [17]. Shales of the Akata Formation constitute a world-class source rock. Deepwater turbidite sands also exist within this formation. The crossection of the Niger Delta is presented in Figure 3 while the correlation of subsurface formations are presented in Table 1.

Methodology
Datasets used in this study was provided by an oil and gas producing company in the region. A summary of the well log information is presented in Table 2 The dataset include: a post stacked 3D seismic survey, covering an area of 528    Pay. Reservoir characterization was carried out to understand the geological and petrophysical characteristics of the reservoirs. The workflow used for this investigation is presented in Figure 4. The data collected were interpreted using PETREL 2013 and GeoGraphix Discovery 2013 software packages. Prior to the importation of these data into the software, the data were validated and edited to minimize error. After validation, the well log data which were in LAS data format was imported into PRISM module followed by the importation of well header information which comprises of the name, coordinates and the start and stop depths of the wells into the WELLBASE LAYER module. The post-stacked 3D seismic data were also imported into the SEISVISION module.

Results
Structural framework showing the identified growth faultsof different orientations mapped across the entire seismic survey using the Ant Tracking attribute is presented in Figure 5. Sixteen faults, labeled Flt 1 to Flt 16 were identified. The faults were observed to be elongate and generally trending East to West. Figure  6 and Figure

Well Correlation Log Facies and Depositional Environment
Seven wells were correlated across the field using the GR and Resistivity logs to give a good description of the reservoirs and to determine the lateral continuity of the sand intervals. The correlation chart and cross-section of the wells from East to West in the following order: AD3, AD1, AD2, AD4, AD7, and AD6 to AD5 are shown in Figure 8. Six sand intervals which serve as reservoirs within Open Journal of Geology

Horizon Interpretation and Reservoir Description
In order to understand the subsurface geology and structural trend for possible hydrocarbon accumulation, seismic and well data were tied. Based on the seismic to well ties, six major horizons were identified, picked and interpreted across the seismic volume. The six hydrocarbon bearing sand units within the Agbada Formation labeled Sand A to F were mapped within the seismic section.    across the wells. The lowest point on the map is along the north-eastern portion.

Reservoir Properties of AD Field
The pay summary of the reservoirs, especially porosity, permeability and Net to Gross in the AD Field is presented in Table 3. The Net to Gross ratio (NTG) ranges between 0.79 and 0.87, while the porosity varies from 20% to 28% and  can be quantitatively evaluated as very good [29]. These values are similar with what has been reported for Niger Delta, ranges from 15% -40% in the reservoir rocks and below waht was proposed by Edwards and Santogrossi [30] (40%) for primary Niger Delta Miocene paralic sandstones reservoirs. The computed porosity of Sand A to F reservoirs vary between 0.19 and 0.32 (avg. 0.29) indicating a good to excellent reservoir characteristics [29] and were also observed to reduce with depth with Sand A (26%) and Sand B (28%) having the highest porosity while Sand F (0.20%) have the lowest. The thickness of the reservoirs varies laterally and they are controlled by the growth faults. The net pay thickness across the field varied with depths between 59 ft. and 192 ft (avg. 125 ft.). The Gross Rock Volume (GRV) for each of the six reservoirs was computed from the volume within reservoir polygons and fluid contacts within the reservoirs. The GRV for gas and oil in the entire field were computed as 266.33 (acre foot) and 180.21 (acre foot) respectively. The water saturation (Sw) in the field ranges from 0.19 to 0.39 with Sand A (0.19) having the lowest water saturation. The total STOIIP and GIIP from the field was estimated as 565 mmbl and 4.08 tcf respectively. Sand A was reported the most economic and viable interval with 39% of the STOIIP and 46% of GIIP in the AD Field.

Static Reservoir Modelling
The static reservoir model of Sand A, the highest hydrocarbon potential, was developed to give a fair reality of the subsurface. The Sand A reservoir was used because it extends across all the studied wells representing the reservoir with the greatest hydrocarbon accumulation in the field. Structural and stratigraphic modelling of Sand A reservoir is presented in Figure 12.

Porosity Model of Sand A
A 3D porosity model showing the porosity distribution in the Sand A reservoir is presented in Figure 13. The porosity model shows well distributed porosity within the field and range from 0.10 to 0.42. However about 90% of the data have porosity ranging between 0.20 and 0.42 which indicates good to excellent porosity (pore spaces) capable of retaining hydrocarbon. Wells in the eastern portion (AD4, AD5, AD6 and AD7) falls within the region with very good reservoir characteristics with average porosity between 0.26 and 0.32.

Water Saturation Model of Sand A
The water saturation model for Sand A reservoir ( Figure 14) shows water saturation from 0.2 to 0.9. The south-eastern part reported water saturation greater than 0.75 indicating high accumulation of water. However, the north-eastern part of the model shows grids of water saturation with values between 0.2 and 0.5 which is indicative of hydrocarbon zones in the reservoir. The hydrocarbon producing wells in the Field (AD4, AD5, AD6 and AD7) were situated in this part of the model.

Facie Model
The facie model of Sand A reservoir Figure 15 shows that all the facies have a regional distribution pattern with a North-South orientation composed of shale (30%), fine sand (60%) and coarse sands (10%). The abundance of shale in the reservoir indicates a transgressive marine environment with minor influence of tides in marine condition [31].

Reservoir Volumetric
The volumetric estimates after modelling Sand A reservoir is presented in Table

Uncertainty Analysis
The influences of uncertain geologic parameters including saturation (S w ), Porosity (ρ), NTG and GRV on the STOIIP were identified and quantified using deterministic values and the summary result of the P10, P50 and P90 after Monte Carlo simulation for the field is presented in (Table 5). The influence of the geological parameters on the STOIIP was and graphically displayed on the Tornado charts presented in Figure 16.   that an increase in the geologic parameter will decrease the output value. Water of saturation (Sw) and the net to gross ratio (NTG) were observed to have the greatest influence on the STOIIP estimates.

Summary and Conclusion
The hydrocarbon resource in AD field, Niger Delta of Nigeria was evaluated by calculating the Hydrocarbon initially in place using both deterministic and sto-