As the development of large structural oil and gas reservoirs continues to advance, oilfield production declines. During in-depth exploration, geological researchers often discover areas with extremely complex strata and lithology, as well as small-amplitude, small-area, and small-fault structures. These oilfields possess the potential for further development, but their development is also very difficult and risky. Facing both opportunities and challenges, the Jianghan Oilfield Exploration and Development Research Institute pursued a collaborative approach between enterprises and universities, and after decades of research, perfected the "locator" for oil and gas reservoirs—reservoir stochastic modeling technology. Initially, this technology was first applied in the late 1970s when American scientist Andre G. Journel proposed the basic concept of geological simulation in his book "Mining Geostatistics." In the mid-to-late 1980s, with the popularization of computers, and utilizing computer science and geostatistics principles, a spatial distribution model was established to represent the spatial distribution of various physical parameters within the reservoir. This was the early application of deterministic geological modeling technology in oil extraction. The earliest geological modeling techniques, under ideal conditions, assumed good continuity and sustainability of reservoir geological conditions, using average values ​​of conventional parameters to roughly outline the underground geological situation. This technique solved some problems related to unclear understanding of underground geological structures. However, in practical applications, not all drilled areas are in a state of good continuity and sustainability. The diversity and complexity of geological conditions are objective realities, and differences exist between wells. Even if two wells are only a few hundred meters apart, the significant errors and variations will lead to huge deviations, inaccuracies, and unrealistic understandings of the underground reservoirs determined by reservoir modeling, potentially resulting in drilling errors and increased exploration and development costs. With the increasing recognition of the application prospects of this technology, more and more researchers are dedicated to addressing its shortcomings in application. How can this technology truly and objectively reflect underground geological conditions and oil and gas reservoir information, facilitating the navigation and location of oil and gas reservoirs? Jianghan Oilfield Exploration and Development Research Institute, considering the complexity of the oilfield's geological conditions, adopted a collaborative approach with universities, joining hands with Yangtze University to tackle the challenges of reservoir stochastic modeling technology. Based on the study and collaborative research of reservoir modeling technology principles, they gradually integrated collected geological, logging, and seismic data, utilizing statistical principles to explore a set of stochastic reservoir modeling methods and processes. The resulting computer-generated geological reservoir models not only met the requirements for predicting reservoirs and objectively reflecting three-dimensional spatial changes underground, but also achieved the conditions for quantitatively, precisely, objectively, and stochastically reflecting the existence of underground geology. This is the stochastic reservoir modeling technology. This new technology has been validated in practical applications in selected oilfield areas. The Jianghan Pingbei Oilfield is a unique low-porosity, ultra-low-permeability reservoir. Its rapid reservoir changes, poor physical properties, and poor heterogeneity increase the difficulty of exploration. How to understand underground resources and reduce exploration and development risks using the most economical and scientific methods? They developed a new method to predict the distribution of underground oil and gas reservoirs using relevant information provided by surface oil and gas outcrops. This method integrates various relevant information into a proposed geological model, using multiple simulation results to predict and evaluate the distribution of reservoirs and reservoir sand bodies. The geological model (i.e., stochastic reservoir modeling) then guides drilling. The success rate of new well drilling reached 100%, with sand layer thickness encountered during drilling increasing by more than 16% compared to previous methods. New proven reserves of 7.62 million tons and recoverable reserves of 1.142 million tons were added. The advantages of new technologies were demonstrated in the Qianbei East Area of ​​the Jianghan Basin. Geological researchers boldly applied stochastic modeling technology and achieved another success, proving that stochastic modeling methods have great potential in reservoir distribution and prediction. The Qianbei area is one of the richest and most promising areas for detailed exploration of oil reserves in the Jianghan Basin. It contains old oilfields such as Huangchang, Zhanggang, and Wangchang, with resources totaling 148.12 million tons. However, after more than 30 years of exploration and development, the oil resource proven rate in this area is as high as 54.4%, making the discovery of new oil-bearing blocks and the achievement of reserve replacement extremely difficult. In response to the specific conditions of the area, geological researchers, combining extensive geological, core analysis, well logging, and seismic data, and considering the characteristics of lithologic and structural-lithologic reservoirs in the region, established a database based on a large amount of data. They then employed a series of techniques, including high-precision target processing, detailed interpretation, attribute analysis, seismic inversion, and geological modeling of 3D seismic data, to focus on lithologic interpretation in and around the old oilfield, identifying and confirming lithologic traps, stratigraphic traps, and "three small" structures. By establishing a sedimentary microfacies knowledge base, a structural model, a sedimentary microfacies model, and a physical property model for the Qianbei East area, and combining the 3D model with production data, three favorable target areas for rolling exploration and development in the Qianbei East area were comprehensively predicted. Through the implementation of rolling exploration and development, the dual goals of increasing oil-bearing area and proven oil geological reserves were achieved. The stochastic modeling technology for oil and gas reservoirs has made new breakthroughs in solving the challenges of understanding complex structural geology, providing a valuable technical means for the scientific and accurate discovery of oil and gas reservoirs. The continuous improvement of this technology has demonstrated its advantages in addressing the challenge of improving recovery rates during secondary and tertiary oil recovery in old oilfields. According to expert estimates, due to various heterogeneous barriers and influences within reservoirs, over 20% of movable oil remains uncaptured by secondary recovery agents. However, utilizing reservoir stochastic modeling technology, with relatively accurate model conditions, allows for targeted exploration and development in older oilfields, significantly increasing the potential for recovery. Further development and improvement are needed. Experts have also pointed out shortcomings in the development of reservoir stochastic modeling technology. Firstly, the difficulty of prediction using stochastic modeling technology is increasing. The complexity of the reservoir's geological conditions, formation process, and geological heterogeneity all contribute to the difficulty of reservoir prediction. Secondly, the shift in exploration focus also leads to the arduousness and long-term nature of reservoir prediction. The shift in oilfield development from easily developed areas to difficult and remote areas, and the trend from a basic balance between reserves and production to a severe imbalance, all pose serious challenges to the research, improvement, and widespread application of new technologies. Optimizing development methods to achieve maximum oil and gas recovery and economic benefits with minimal human and financial investment is therefore particularly urgent. Third, research efforts on reservoir stochastic modeling technology need to be intensified to continuously develop a comprehensive and systematic scientific framework. This will add technological sophistication to future reservoir modeling techniques, while also indicating that the road ahead for this new technology will be long and arduous.