Dynamic Reservoir Uncertainty Evaluation for Production Delivery Assurance

Abstract

A new production development scheme was proposed for one of the major offshore fields in the Gulf. To assist building assurance of the delivery of this scheme, subsurface uncertainty evaluation was initiated; firstly based on an in-house approach and later through the use of commercial softwares. The proposed study is primarily focused on dynamic reservoir uncertainties. The objective of this study is to evaluate the impact of the reservoir uncertainties on plateau duration period in the prediction phase. The workflow, based on experimental design and response surface, advantages and outcomes of this study are presented and its limitations discussed. This study was completed over a short period of time, thanks to an optimization of the CPU resources. This was a key advantage obtained while carrying large models with long history exceeding 50 years. The study resulted in successfully delivering probabilistic profiles (P90, P50, P10) in order to assure the production delivery of the proposed development scheme and enables to develop risk mitigation plans. A ranking of the most influential uncertainty parameters with quantification of their interactions is obtained. Consideration is also given to the history match quality, which results in reducing the parameters distributions and highlights the value of the data acquisition. Introduction ADMA launched a study to assist building assurance of the delivery of new production development scheme and to assess the risk of applying such strategy. The objective of this study is to evaluate the impact of the reservoir uncertainties on plateau duration period in the prediction phase. This study come up with a probabilistic forecast (P90, P50, P10) instead of a single deterministic forecast as usual. First, ADMA launched the study in-house in two phases. The fist phase is simple and based on mono-parameter sensitivity at a time method but requires a large number of runs and doesn't evaluate the impact of interactions between parameters. 13 parameters are used and 4 runs are generated for each parameter. The second phase is based on the Monte Carlo analysis. After assigning a probabilistic law to each uncertain parameter, n realizations of the uncertain parameters are picked to obtain a sample of n reservoir simulations. This method is expensive in terms of number of reservoir simulation runs (at least 100 runs for 10–15 parameters). Secondly, ADMA called for two companies and their commercial softwares (X and Y) to tune the study further. ADMA's optimized methodology is based on experimental design and response surface modelling to model flow simulations. The same 13 uncertain parameters have been considered for this Field study. Those parameters have been re-evaluated and constrained by historical data in order to reduce uncertainty range and they were assigned a new distribution law. Using new ranges and distribution laws, uncertainties are propagated and probabilistic production forecasts are obtained. The work flow of this study is shown in figure 1.