区带勘探中的油气资源评价方法

发布时间：2019-03-10 20:39

【摘要】：国内外主流统计方法的预测结果,作为区带高效勘探部署的有效决策依据还有一段距离,评价方法研究与应用仍有提升空间。立足于提升区带商业发现效益以及解决区带勘探部署规划中的实际问题,分别提出:(1)区分边际概率、条件概率与空间概率模型的地质模型内涵与适用条件,揭示特定地质条件约束下的地质风险,辅助提升低勘探程度区带的预探目标或风险井的勘探成功率;(2)遵循油气成藏体系理论划分高勘探程度区带范围,按最早探井发现时间整理、归并储量计算单元为经历了相同地质作用过程的油气藏(样本)数据,为求解油气藏规模总体分布模型提供较为客观的样本数据;(3)充分考虑不同勘探投入对油气藏发现的影响,推导期望最大化算法并基于勘探效率图版求解地质帕莱托概率分布参数,更加客观地预测区带油气资源潜力及油气藏规模结构;(4)分别采用体现地质和市场不确定性的情景树,以及勘探目标地质相关性或战略相关性的贝叶斯网络,可有效将投资组合技术与公司战略规划部署有机结合起来。
[Abstract]:The prediction results of the mainstream statistical methods at home and abroad, as the effective decision-making basis for high-efficiency exploration and deployment in the zone, are still a long way away, and there is still room for improvement in the research and application of evaluation methods. Based on improving the commercial discovery benefit and solving the practical problems in zone exploration deployment planning, this paper puts forward the following points: (1) distinguishing marginal probability, conditional probability and spatial probability model's geological model connotation and applicable conditions, respectively, and puts forward the following problems: (1) distinguishing marginal probability, conditional probability and spatial probability model. To reveal the geological risk under the restriction of specific geological conditions and to promote the exploration success rate of the pre-exploration targets or risk wells in the low exploration zone; (2) according to the theory of oil and gas reservoir-forming system, the zone of high exploration degree is divided, the discovery time of the earliest exploration well is arranged, and the reserve calculation unit is merged into the data of oil-gas reservoir (sample) which has experienced the same geological process. In order to solve the overall distribution model of oil and gas reservoir scale, we can provide more objective sample data. (3) considering the influence of different exploration investment on the discovery of oil and gas reservoir, the expectation maximization algorithm is deduced and the probability distribution parameters of geological Pareto are solved based on the exploration efficiency chart. It is more objective to predict the potential of oil and gas resources and the scale structure of oil and gas reservoirs in the zone. (4) using the scenario tree which reflects the uncertainty of geology and market, and the Bayesian network of geological correlation or strategic relevance of exploration target can effectively combine the investment portfolio technology with the strategic planning and deployment of the company.
【作者单位】：中国石化石油勘探开发研究院;清华大学理学院数学科学系;
【基金】：中国科学院战略性先导科技专项(A类)(XDA14010101)的软件成果
【分类号】：P618.13

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