油气勘探大数据分析在中亚盆地优选中的创新应用

随着数字化时代的到来,石油公司开始重视基础资料的获取及整合,不断加大油气大数据的分析应用力度,以期从大数据中寻找"大油气"。然而,以传统数据统计分析方法研究海量数据的时效性很差,而且有效分析方法和关键评价指标缺乏,严重制约了油气勘探行业大数据的深度应用。中亚油气资源丰富,是中国石油公司实施"一带一路"国家能源合作战略的重点地区和现实之选。在战略选区盆地筛查阶段,研究范围大、周期短、井震资料匮乏,难以开展有效的石油地质分析,无法形成宏观认识指导决策。为此,引入大数据思维,开展大数据分析,深度挖掘、二次开发数据库,整合海量多源异构数据,创建中亚战略选区知识库,为勘探大数据分析奠定资料基础;创新油气勘探数据挖掘技术和大数据分析方法,创建"三位一体"KPI综合打分模式,优选多个有利勘探潜力盆地,有效指导中亚战略选区。研究提供了新的思路和对策,从油公司的角度阐述了开展勘探大数据分析的必要性和可行性,具有较好的应用价值和推广意义。

Application of big data analytics to hydrocarbon exploration for favorable basin selection in Central Asia

With the advent of the digital era, oil companies have invested more in obtaining and integrating basic data, and constantly improved the utilization of big data analytics, as an emerging trend, in oil and gas industries, with a view to discovering "big oil and gas". With the use of big data, companies can capture large data in real time; in contrast, traditional statistical analysis is characterized by poor timeliness in capturing large volumes of data, as well as a lack of efficient methods for analysis and critical evaluation parameters, seriously restricting the in-depth application of the big data analytics in hydrocarbon exploration. Central Asian is a region rich in natural resources, including oil and gas, and also a key area and ideal choice for China's oil companies to implement the Belt and Road Energy Cooperation Strategy. However, it is difficult to carry out effective petroleum geological analysis at the stage of study area selection, given the large scope of researches in short time, and the lack of data in seismic interpretation and from wells, and a macro-understanding to guide decision-making cannot be reached as a result. In this regard, we firstly carry out big data analysis following deep mining into and secondary development of purchased databases, integrating massive multi-sourced heterogeneous data, creating a knowledge base for strategic area selection in Central Asia, serving to lay a data foundation for big data analysis in petroleum exploration. Secondly, methods of data mining and big data analytics are innovated for hydrocarbon exploration, and a comprehensive key parameter indicator (KPI) scoring model based on a Trinity of petroleum geological conditions, exploration maturity and commercial value, is established to select multiple petroliferous basins of great exploration potentials and effectively guide the strategic area selection in Central Asia. The study provides new ideas and solutions, and expounds the necessity and feasibility of big data analytics for petroleum exploration from the oil companies' point of view. In all, it is of great significance to application and promotion.