Conceptual uncertainty in the interpretation of geological data: statistical analysis of factors influencing interpetation and associated risk

该项目将研究和开发新的方法，以便能够评估因不同的解释概念和范例对同一地质数据集产生的偏差而产生的风险。石油行业的一项关键任务是评估与碳氢化合物估计相关的风险。这对于最大限度地提高北海等产量达到或超过峰值的盆地的采收率具有越来越重要的意义。在更复杂的地质情况下识别新储量也必须在可接受的商业风险范围内进行。至少可以粗略地解释数据测量、收集和处理过程中的不确定性。然而，一个更根本的困难是，由于解释偏差，正确分配风险。地下地质模型是从三维地震数据、井筒地球物理测井和遥感数据等数据集创建的。这些数据采样的地下体积有限，在时间和空间上的分辨率也有限，因此最终的模型高度依赖于解释人员的概念框架。通常情况是，来自不同教育背景或具有不同油田背景经验的口译员可能会对相同的数据得出非常不同的结果。地下地质模型是数据受限的自然系统，“诊断技能”和统计不确定性具有重大的社会和经济影响。这一问题将通过使用一些解释概念确定从合成地震数据集得出的模型的可变性来进行研究。合成地震数据将从米德兰山谷软件中创建的完全定义的地质模型中创建。然后将对数据集进行解释，并将在没有先验信息的情况下应用于数据的概念与给定先验信息的对照组进行比较。将对结果进行分析，以确定与实际地质情况的差异，以及控制组和非控制组之间的差异。差异的量化将被用来评估由于解释偏差而产生的不确定性程度。合成地震数据的工作将得到基于现场的研究的赞扬，这一研究自然受到曝光的3D限制。绘制的断层网络将用于在二氧化碳自然泄漏的地区基于几何断层联系的不同概念创建多个结构模型。米德兰山谷公司新开发的软件4DMove将用于验证和评估与收集的不同解释概念有关的不确定性，包括合成地震和现场数据。4DMove的分析将通过对问卷参与者收集的信息进行多分类回归分析来支持，以评估其他因素的影响，如：经验、教育和培训。将在TRapTester软件包中对油气储藏或二氧化碳储存潜力进行评估，并针对不同的结构模型进行量化，以突出地下结构框架对储集层连通性的关键影响，从而突出储集层潜力。目前北海的油井成功率在35%-40%之间，随着每口井的成本从1000万美元增加到超深水的50万美元，错误的井位是对石油公司资源的浪费。任何减少地质概念不确定性的工具都将在行业内产生巨大影响。关于地下废物处理和二氧化碳储存的社会和经济影响，也可以提出类似的论点。项目的目标是：-开发技术和方法来评估影响概念不确定性的因素--量化解释误差--量化不同模型对前景的影响(使用4DMove)--创建一个程序，以减少石油勘探和废物储存中与结构模型相关的不确定性。

This project will research and develop new methods to enable the assessment of risk arising from the bias introduced by alternative interpretational concepts and paradigms to the same geological data set. A crucial task for the petroleum industry is assessing the risk associated with estimates of hydrocarbons. This is of increasing importance for maximising recovery in basins at or past peak production, such as the North Sea. The identification of new reserves in more complex geological situations must also occur within an acceptable commercial risk envelope. Uncertainty in data measurement, collection and processing can be accounted for, at least crudely. A more fundamental difficulty, however, is correctly assigning risk due to interpretational bias. Models of sub-surface geology are created from data sets such as 3D seismic data, well bore geophysical logs, and remote sensing data. These data sample a limited volume of the subsurface and at a limited resolution in time and space, therefore the final model is highly dependent on the interpreter's conceptual framework. It is often the case that interpreters from different educational backgrounds, or with experience in different oil field settings, can come up with very different results for the same data. Models of sub-surface geology are data-under-constrained natural systems and 'diagnostic skill' and statistical uncertainty have significant social and economic impact. This problem will be examined by determining the variability of models derived from synthetic seismic data sets using a number of interpretational concepts. Synthetic seismic data will be created from a fully defined geological model created in Midland Valley's software. The data sets will then be subject to interpretation, and the concepts applied to the data without prior information will be compared to control groups, given prior information. The results will be analysed for variation from the 'real' geology, and for variability between the control and non-control groups. Quantification of the differences will be used to assess the degree of uncertainty due to interpretational bias. The work on synthetic seismic data will be complimented by a field based study, naturally limited in 3D by exposure. Mapped fault networks will be used to create multiple structural models based on different concepts for geometrical fault linkages, in an area that has natural leakage of CO2. Midland Valley's newly developed software 4DMove will be used to validate and assess the uncertainties related to the different interpretational concepts collected, both for the synthetic seismic and the field data. Analysis in 4DMove will be supported by polytomous regression analysis of information captured from participants in questionnaires to assess influences from other factors such as: experience, education and training. Compartmentalisation, and hence hydrocarbon reservoir or CO2 storage potential will be assessed in the software package TrapTester and quantified for different structural models, to highlight the critical impact of sub-surface structural frameworks on reservoir connectivity and hence potential. The current success rate of wells in the North Sea stands at between 35-40%, and with the cost per well c. 10 million dollars, increasing to c.50 million dollars in ultra deep water, erroneous well positioning is a waste of oil company resources. Any tool that reduces geological concept uncertainty will have a large impact within the industry. Similar arguments, for social and economic impact can be made for sub-surface waste disposal and CO2 storage. The projects objectives are: -to develop techniques and methodologies to assess factors influencing conceptual uncertainty -to quantify interpretational error -to quantify the impact on prospectivity of different models (using 4DMove) -to create a process to reduce the uncertainty associated with the structural model in petroleum exploration and waste storage.