A Flow Based Reservoir Model Ranking Strategy

Abstract

Abstract Even though production data have direct responses of reservoir heterogeneity and connectivity, they are rarely incorporated into reservoir modeling workflow among the geological community. In this paper, a designed simulation method is proposed to mitigate reservoir connectivity uncertainty. This proposed method is more accurate and efficient by integrating production data with reasonable computational cost. This method, applied on a North Africa shallow marine reservoir, includes following four steps.A Folded Plackett-Burman design (FPBD) is used to generate typical reservoir facies models using multipoint geostatistics method. Then reservoirs properties (i.e. porosity, permeability and saturation) are populated among different facies. The different scenarios of reservoir properties represent geological uncertainties.The recovery factor of each model is computed by using a commercial black oil simulator. A polynomial response surface of recovery factor and modeling parameters is generated as a proxy of flow responses. A Monte Carlo Simulation Bayes Method (MCSBM) estimates the uncertainty of recovery factors and derives the posterior probabilities for modeling parameters.The facies models are changed by using a probability perturbation method, which reduces the error between observed pressure data and simulation prediction. Different from traditional methods, the objective of this step is to mitigate connectivity uncertainty by integrating production data instead of forcing history match.Similar to step 2, another polynomial response surface between updated recovery factor and modeling parameters is generated by using perturbed reservoir models. MCSBM derived the conditional probability of modeling parameters and probability distribution of recovery factors. Both derived probabilities are compared with initial models. Sensitivity analysis shows that recovery factor heavy hitters of updated models are significantly different from initial models. MCSBM shows that the production updated modeling parameters have smaller uncertainty ranges than the prior uncertainty ranges, which means the production update reduces parameters uncertainty. The updated recovery factors have wider uncertainty ranges (18 percent) compared with initial recovery factors (15 percent). This is understandable because the updated geological models honoring production data may be more heterogeneity and have tortuous connectivity. The updated models help capturing the full uncertainty ranges for recovery factor. The updated recovery uncertainty as well as reduced modeling parameters uncertainties help correct business decisions. Furthermore, the updated response surfaces show confounded higher order nonlinear effects. Therefore, further study based on more detailed design is desired. The method in this paper honors available data without over-tuning geological parameters for history matching. It also provides guidance for data acquisition to mitigate production prediction risks. 1. Introduction The goals of this work are 1) reducing modeling parameters uncertainty by integrating dynamic data; 2) changing the reservoir connectivity to capture correct uncertainty ranges; 3) developing a flexible, integrated method to model sedimentary facies connectivity. Stochastic reservoir modeling technology provides geologists and engineers an efficient way to integrate various types of data for predicting inter-well reservoir properties. At the early stage of reservoir exploration, well data are usually limited, which pose a big challenge of inter-well prediction. Those reservoir properties are then used to compute oil in place (OIP) on which business decisions are usually based. In the study area, existing geological data are limited with poor quality. One major object is then to integrate all available data to accurately estimate the reservoir properties using accurate modeling parameters.