Application of Uncertainty Management in a Brown Field using Reservoir Simulation: A Case Study of SPDC's Field Re-Development

Abstract

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.