## 1 Introduction  
The heterogeneity of geological bodies, limited access to data, and cognitive limitations all have an impact on reservoir uncertainty. It is difficult to predict reservoir properties using limited information, and this difficulty persists throughout the entire reservoir description process [1, 2]. Due to the heterogeneity of geological bodies and the multi-solution of seismic data, there is uncertainty in the early stages of oil and gas exploration and development. This uncertainty arises from the uncertainty of geological bodies, geophysical and petrophysical properties, and dynamics. The level of accuracy in the reservoir modeling stage, the choice of various modeling algorithms and parameters, the theoretical underpinnings and practical expertise of reservoir crews, as well as the quality of the reservoir modeling, all contribute to the uncertainty. The objective and the cognition categories of system uncertainty can be distinguished [3, 4]. Uncertainty primarily results from incomplete geological data, varying solutions, and reservoir practitioners' inadequate comprehension of reservoir conceptual models in reservoir characterization [5]. The uncertainties in different stages and directions run through every link of reservoir research. In reservoir research, it is necessary to accurately analyze the source of uncertainty and evaluate the influence of uncertainty on reservoir development. The development of oil and gas reservoirs has faced several risks and challenges as a result of reservoir uncertainty [6, 7]. It mainly focuses on reducing and characterizing reservoir uncertainty. In order to reduce the risk of exploration and development and to provide instructions and suggestions for production, many scholars have studied the uncertainty from engineering, geology, and other aspects to reduce or have attempted to quantify the uncertainty of the reservoir [8; 9; 10; 11; 12; 13]. Some academics used numerical analysis and seismic data comparison to reduce uncertainty [14; 15]. Using dynamic data analysis and other techniques, we can better comprehend the reservoir and lessen its uncertainty [16; 17]. The three levels of values--pessimistic, most likely, and optimistic--are currently often used to evaluate or characterize the uncertainty of reservoir parameter values. For instance, [PERSON] et al. extracted the occurrence frequency of the specified sedimentary microfacies in multiple simulated three-dimensional spatial grids and obtained the three-dimensional probability distribution model of the specified sedimentary microfacies in each layer; they then used the Monte Carlo method to generate the cumulative probability distribution map of reserves. The cumulative probability points of 10%, 50%, and 90% are used to find the corresponding three reserve values, and the probability that the real reserves can reach these three reserve values is 90%, 50%, and 10%, respectively. These three reserve values are also known as P90, P50, and P10. They are used to evaluate the uncertainty of model reserves [18]. It should be noted that P10, P50, and P90 are not fixed, and they depend on the method of exceedance probability or non-exceedance probability. Taking P10 as an example, if the exceedance probability is selected, P10 means that the probability of being higher than the cut-off point is 10%, and the cut-off point is a higher value. P10 indicates a 10% probability of falling below this cut-off point, which is a lower value. In order to model the BZ oilfield in Bohai Bay, [PERSON] et al. chose six geological variables, including the lower porosity limit, the sedimentary microfacies change-difference function, the sedimentary microfacies content, the porosity variation function, and the oil-water interface. They adopted maximum, minimum, and average values to represent the uncertainty [19]. [PERSON] chose several seismic attribute truncation values to represent the uncertainty of the fan-delta border [20].