油气勘探大数据分析在中亚盆地优选中的创新应用

doi:10.11743/ogg20210620

Abstract

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:

TE132.1

Ke Zhang, Yina Zhang. Application of big data analytics to hydrocarbon exploration for favorable basin selection in Central Asia[J]. Oil & Gas Geology, 2021, 42(6): 1464-1474.

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[1]	Tian Naxin, Yin Jinyin, Tao Chongzhi, Kong Fanjun. Petroleum geology and resources assessment of major basins in Middle East and Central Asia [J]. Oil & Gas Geology, 2017, 38(3): 582-591.
[2]	Yu Yixin, Yin Jinyin, Zheng Junzhang, Li Feng, Xu Xiaolong, Wu Hang, Yu Xiao. Hydrocarbon accumulation rules and exploration potential in the North Ustyurt Basin [J]. Oil & Gas Geology, 2016, 37(3): 381-386.
[3]	Wang Suhua, Qian Xianglin. TECTONIC EVOLUTION AND OIL-GAS POTENTIAL OF BASINS IN CENTRAL ASIA AND NORTHWESTERN CHINA [J]. Oil & Gas Geology, 1999, 20(4): 321-325.

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

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盆地	烃源条件	储层条件	圈闭条件	盖层条件	资源丰度	井震密度	待发现资源量	勘探趋势	流体性质	勘探成本	开发难度	财税政策
滨里海盆地	2	1	1	1	2	1	2	2	1	7	8	7
南里海盆地	4	4	5	5	3	2	1	1	4	3	1	9
阿富汗-塔吉克盆地	5	7	7	7	7	3	9	10	10	6	2	5
阿姆河盆地	1	3	3	2	1	4	3	3	11	2	7	6
北乌斯秋尔特盆地	7	5	2	4	5	5	10	8	5	4	4	4
图尔盖盆地	8	6	6	6	6	6	7	9	2	8	9	8
费尔干纳盆地	6	8	8	8	8	7	11	11	3	1	6	1
曼格什拉克盆地	3	2	4	3	4	8	8	7	9	11	3	10
楚-萨雷苏盆地	9	9	9	9	9	9	4	4	6	9	5	2
斋桑盆地	10	10	10	10	10	10	5	5	7	5	10	3
田尼兹盆地	11	11	11	11	11	11	6	6	8	10	11	11