It is commonly practice in reservoir modelling to use constant kv/kh values to define vertical permeability in numerical reservoir models. This is often in response to paucity of core data and need to reduce simulation run time. That traditional approach works fine in fairly homogeneous reservoirs; however, in reservoirs with heterogeneity, due to more complex depositional environment like shallow marine deposits, it is not necessarily a good practice to assume single averaged kv/kh values to represent vertical connectivity for such reservoirs. This paper highlights the importance of understanding reservoirs' vertical connectivity by properly defining kv/kh as a function of lithofacies and evaluating its impact on history match and prediction outcomes on one case study for a heterogeneous brown field reservoir with complex depositional environment in the Niger Delta. Core data, wellsite cuttings data along with available production, and pressure data were used to calibrate the petrophysical and numerical dynamic models. The study also considers substantially a multi-disciplinary approach to uncertainty management, using Experimental Design (ED) methods to understand and validate kv/kh ranges as a function of lithofacies. This methodology was successfully implemented across several oil reservoirs (D2000-D7000 EXHJK) in a recently concluded field project in the Niger delta of Nigeria. As an example, this paper discusses the results, and recommendations for one reservoir (D5000X) selected out of this project study. The main objective from this project is the importance of understanding vertical connectivity in a complex reservoir by capturing heterogeneity and major geological features in the integrated 3D dynamic model aiming to achieve acceptable history match at reservoir level, but also more importantly on a well-by-well basis. The robustness of the integrated dynamic model provided higher level of confidence for prediction, and resulted with increased certainty for the forecasts by a factor of 3 (reduced solution space) after implementing variable kv/kh as function of facies.

Net-To-Gross (NTG) is the ratio of pay rock volume to the gross rock volume estimated over an interval used to compute hydrocarbon-initially-in-place (HCIIP). Identifying NTG as a series of discrete data points along the well path where the rock is ascribed as reservoir (NET = 1), or non-reservoir (non-NET = 0) based on a suitable cut-off estimated from a combination of petrophysical logs is a first step. In a deltaic setting, prevailing depositional processes result in inherent vertical and lateral variability with the effect that the determination of NTG is scale sensitive and interval dependent. This reality imposes a requirement of deliberate focus in 3D NTG modelling to preserve the depositional heterogeneity and ensure that any integrated reservoir modelling effort is geologically representative. This paper discusses different practices in the use and upscaling of NTG from well data and distribution in a static model, by comparing three approaches: (1) upscale and distribute using Sequential Gaussian Simulation (SGS) biased to facies, (2) upscale and distribute independent of the facies model but biased to established NTG regional trend, and (3) generate a discrete model based on a combination of the facies model and NTG data from the wells. The best method, which is still a simplification of reality, will be one where the model can rightfully predict NTG in undrilled areas and assess the impact on recovery estimation. Assessing these methodologies using a mature field in the coastal swamp depobelt in the Niger delta helps build a case for model simplification in an adjacent partially appraised field in the same depobelt. Thus, providing a simplified approach and clarifying insights on the conundrum of utilizing discrete NTG ‘logs’ to build a continuous property that is useful for HCIIP estimation but has minimal impact in the dynamic behavior of the reservoir.

FLO reservoir in the depth range of 8225-8780 ftss has an unknown nature of compartmentalization, unknown fluid property and also five well penetrations with none encountering OOWC (original oil water contact). Resolution of these uncertainties in this study created platform for the reservoir development in terms of growing production and increasing the reservoir ultimate recovery. This paper therefore focuses on an integrated approach of resolving uncertainties associated with key reservoir evaluation parameters to deliver a fit-for-purpose development plan of the reservoir. A multidisciplinary approach, involving integration of: PVT (Pressure Volume Temperature) Property estimation, Fault Seal Analysis (FSA), Petrophysical analysis and Dynamic simulation were employed to pin down these uncertainties in fluid contact and connectivity across blocks K, L and M within FLO reservoir. PVT property estimation was used to capture the phase behaviour of FLO reservoir fluids. While FSA, which relied on the degree of throw thickness and lithologic juxtaposition, was used to validate connectivity across fault blocks in FLO reservoir. Possible base case contact for the two producing blocks in the reservoir was established through dynamic reservoir model calibration by history matching for 41 years of production history. Results obtained from FSA, pressure profiles, sand-to-sand juxtaposition and dynamic history matching all combined showed an established hydraulic communication between reservoirs block K & M and also blocks K & L. The dynamic history matching with adjustments within physical meaning of basic history matching parameters realized 8525 ftss and 8810 ftss for POWC (present oil water contact) of the reservoir blocks K and M respectively. With these uncertainties resolved, different tests of development concepts were conducted and checked for economic viability and this showed that a "Do Nothing" approach with recovery of 3.6 MMstb should be adopted for FLO K and a "New Well" with recovery of 1.92 MMstb be drilled to develop FLO M.

3D reservoir modelling of stacked reservoirs is often difficult, due to interplay of various uncertainties related to heterogeneity of the sand units modelled together within the stack. In the past, reservoir simulation had been used to model stacked reservoirs for commingled wells with varying predictive capacity. Typically, dynamic models use conventional approach in assessing uncertainties, involving discrete sensitivity of uncertain parameters and often lack robust subsurface uncertainty management. Additionally, handling dynamic cross flow issues by modelling of control valves in such cases presents production allocation challenges. Huge man-hours are needed for several run times, without achieving relatively good history-match and predictive capacity. As a result, only base-case history-matched model is developed; thus robust assessment of impact of sub-surface uncertainties on the predictions is usually inadequate.This paper details a multidisciplinary approach adopted in integrated dynamic modelling of stacked reservoirs in a Niger Delta field using experimental design methodology. The phased development was aimed at full-scale reservoir development of multi-zone and commingled wells. Instead of single average for each sand unit, facies-dependent kvkh values were required to adequately capture reservoir heterogeneity for the complex depositional environment. Field-wide history-matches for 23 reservoir blocks and over 70 individual conduit matches were done to calibrate the simulation models. Model robustness was conducted through blind-testing and calibration with carbon-oxygen and openhole log data from newly drilled wells.From the model predictions, recoveries for existing conduits from simulation models were benchmarked against Decline Curve Analyses and the results compared closely. Response Surface Models were used in selecting best history-matched realizations. 1P, 2P and 3P models were selected from the ultimate recovery probability distribution curves of these history-matched realizations. The modelling of interval control valves settings within the stacked models and use of smart well routines assisted evaluation of recoveries from planned commingled wells.

Managing uncertainties during subsurface modelling in brown field re-development requires robust identification and quantification of impact of the underlying uncertainties. Within a given cycle of integrated reservoir modelling, a modelling strategy can only be defined based on associated uncertainties and development options. This paper focuses on using the Design of Experiment to screen and quantify the impact of uncertainties and evaluate development outcomes in a brown field.The paper details steps taken to identify and quantify subsurface uncertainties in a multi stacked reservoirs that could impact development options. The overall development strategy was to: (a) Introduce artificial lift and offtake management to keep existing wells flowing, (b) Install gas lift supply lines from new gas-lift skid at the flowstation to existing wells requiring gas-lift, (c) drill and complete additional wells using existing locations, (d) hook up the new wells to existing remote manifold and route to flowstations via existing bulklines. To access the impact of uncertain parameters and its ranges of uncertainty were identified and quantified based on current understanding of the reservoirs. The PlackettBurman Design of Experiment was used to screen each parameter using the Tornado and/or Pareto Plots.The key uncertainties (heavy hitters) identified from the screening stage were carried forward to develop a Response Surface Model (RSM) using Box-Behnken experimental design in order to sample the full uncertainty space associated with each reservoir. The probability distributions of In-Place and cumulative production were generated using Monte-Carlo analysis and estimate of the Proved, Probable and Possible volumes and ultimate recovery were obtained (part of methodology)From the study results, the feasibility for further oil and gas development based on 3D reservoir simulation with several development scenarios and options were evaluated. Results of the deterministic possible outcomes were used to identify specific cases that closely matched the Proved, Probable and Possible volumes from the Monte-Carlo distribution. Model provides tool for better well and reservoir management.