The importance of estimation of reservoir recovery factors to a high level of accuracy cannot be overstated in field appraisal or development. Estimation of recovery factor depends on a combination of static and dynamic parameters which in themselves come with a lot of uncertainties and in most cases there is insufficient or poor quality data to enable effective and accurate estimation of a recovery factor. Empirically derived recovery factor equations have been developed for cases were there is limited data to be able to get an estimate of recovery factor. Empirical Oil Recovery Regression Equations were initially developed by the American Petroleum Institute (API) on sandstone reservoirs in the United States and adapted by Arps, popularly called Arps equation. Since then numerous other equations have been developed but there has been lack of a robust equation developed specifically for the Niger Delta environment, although Arps equation has been tested on Niger Delta Reservoirs and work done shows it can be used in the environment although with some caution. This paper shows a new approach to developing an Empirical Oil Recovery Factor Equation for water drive reservoirs in the Niger Delta using reservoir characterization and Artificial Neural Networks (ANN). The ANN is a mathematical model inspired by biological neural networks. In this approach reservoir recovery factors from mature water drive reservoirs in the Niger Delta Region were characterized based on facies type and properties into groups to draw inferences on similar reservoir types tied to different recovery factor ranges. The data from selected group set was randomly divided into three parts, of which 60% was used for training, 20% for validation, and 20% for testing. The final ANN model for particular groups was tested for robustness against the original empirical correlation developed by API.

The Bonga North West (BNW) field is located in Shell operated Oil Mining Lease (OML) 118, a deepwater development in Nigeria. This paper presents the BNW subsurface development strategy and the associated uncertainty management both at planning (Field Development Plan -FDP) and execute phase.The first development in the OML 118 acreage is the Bonga Main field which came on stream in November 2005 via a Floating, production, storage and offloading (FPSO) vessel. The development strategy of the block is to maximize value from the asset, ensuring that the hub is kept full by developing reservoirs in the nearby fields while optimizing production from the block. In pursuant of this strategy, detailed subsurface evaluations were carried out to estimate expected recovery of the nearby fields as potential tie-backs to the Bonga Main FPSO. The size of nearby discoveries and proximity to the FPSO were two key parameters used to develop the OML 118 development strategy.BNW was discovered in 2003 following the detailed interpretation of 3D seismic acquired in 1995 on the acreage. Integrated evaluation across key disciplines established commercial viability of the project. This, coupled with a detailed geo-hazard study, formed the basis for the 2011 BNW FDP. The FDP proposed the field being developed with nine wells in two phases of drilling. The development phasing was driven by the requirement to manage the field uncertainties by gathering additional data. The first phase development involves six wells (four oil producers and two water injectors).BNW leveraged on its close proximity to the existing Bonga Main infrastructure and was approved as a subsea tie back development to existing Bonga Main FPSO. The FDP enabled a robust management of challenges such as shallow gas occurrence, structural/depth uncertainty, stratigraphic/reservoir development, reservoir connectivity, fluid prediction in the shallow horizons, bore hole stability and overburden shale issues encountered in the execution phase. Also a new technology that targeted managing of impairments in water injection wells experienced in the operation of Bonga Main field was also successfully deployed in the BNW field development. All Phase 1 development wells have been successfully drilled and several learnings from this project have been documented and will be used on projects going forward.

A second monitor 4D seismic was recently acquired over a deepwater field in Nigeria. The acquisition was completed on time and without any Health Safety Security or Environmental incident. This survey used steerable streamers, advanced current prediction techniques, and a dedicated time-lapse data acquisition expert onboard the vessel to guide the sail-lines selection. These improvements contributed to a final 4D dataset with better geometric repeatability than the first seismic monitor acquired four years prior. Early results of the 4D seismic interpretation of the results from fast-track processing in the different reservoirs reveal two predominant sweep patterns: (a) localized channeling indicative of limited movements within sinuous channels and meander belts; and (b) broad flood-front movement associated with the more lobate, amalgamated systems. This interpretation has provided another level of detail in the understanding of reservoir architecture within the field. Areas of bypassed oil can be identified on 4D seismic and are being used in locating potential additional infill wells. In addition, there has been an improvement in the mapping of the flooded areas of the reservoirs and the seismic-derived flood patterns have provided additional constraints to enhance the quality of the history-matches, resulting in improved static and dynamic subsurface models.

Eko is a prolific Deepwater brown field on production for 10+ years and cumulatively produced ~750MMSTB from 30+ producers and 25+ injectors (average cumulative production 25MMSTB/well). However, the actual ultimate recovery developed by producers vary from <10MMSTB to >90MMSTB which can be attributed to STOIIP distribution, reservoir complexity & development strategy. As the field has matured to production plateau and with sufficient static & dynamic data now available, a Deepwater Analogue study was kicked off to develop an Ultimate Recovery(UR) predictive model to benchmark the robustness of the simulation predictions for the next phase of in-fill development wells. This database will also support the quick evaluation of other Deepwater fields which are in advance stages of hydrocarbon maturation funnel. An integrated study was carried out to collate all static/dynamic data in each reservoir/well. Drainage area for each producer(s)-injector(s) well pair was defined (Figure 1) and associated average reservoir properties were estimated from latest history matched models. Several combinations of parameters were cross-plotted against the objective function (UR/Well) and a proxy function was established to predict UR for future wells. An error analysis was carried out on the reference case function to establish prediction uncertainty ranges. The simulation based P90/P50/P10 UR ranges for the future in-fill development wells were compared with this analogue database and where the forecasts from models were not in sync with actual observations in Eko field, modeling assumptions were revisited. As way forward, this analogue database will be updated with new well information, static re-modeling and/or history matching exercise. Also, the in-fill well results will be used as litmus test for the robustness of the analogue model. Figure-1 Amplitude Map Showing Definition of Drainage Areas for Major Reservoir (Red wells = Oil Producers, Blue wells =Water Injectors)

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