Oil & Gas Geology ›› 2021, Vol. 42 ›› Issue (6): 1464-1474.doi: 10.11743/ogg20210620

• Methods and Technologies • Previous Articles     Next Articles

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

Ke Zhang(), Yina Zhang   

  1. CNOOC International Ltd., Beijing 100027, China
  • Received:2020-07-24 Online:2021-12-28 Published:2021-12-16

Abstract:

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.

Key words: multidisciplinary integration, key parameter indicator (KPI), data visualization, comprehensive KPI sco-ring, Big Data fusion, hydrocarbon exploration-centered Big Data Analytics, favorable basin selection, Central Asia

CLC Number: